I started writing for this blog almost three years ago now, and I’ve been thrilled and humbled by its enthusiastic reception. More than 3,500 of you have been kind enough to subscribe. I’ve appreciated the thoughtful and heartfelt conversations we’ve had over the last few years about (un)grading, generative AI, and other aspects of our teaching lives.

If you’re a regular reader, I wanted to let you know that I’ll be taking a break from the blog over the next few months. This is for two reasons. One is that I have two books about grading currently in progress, and it turns out that writing books requires a significant amount of my time and energy. But secondly and more importantly, my husband and I will be welcoming our first child in the next few weeks—a project that will require even more time and energy, probably for the next two decades or so!

I hope, however, to return to writing regularly when things settle down a little. I’m not sure exactly when I’ll be back, but please stay subscribed to find out! I’ll also publish updates and announcements about my forthcoming books here, so stay tuned for those.

Thank you again for reading. I’m looking forward to many more stimulating conversations in this space, after I’ve had time to tend to all the new blossoms 2026 will bring.

-Emily

In my last post, I shared some things I learned about students’ experience of collaborative grading in my fall class, drawing on their end-of-semester evaluations and conversations I had with them in their final grade conferences. This week, I’ll break down their reactions to the new AI policy I implemented.

I’ve written extensively about my approach to AI last fall, sharing both my policy document and my own reflections on how it went. To summarize briefly: I gave students the option to choose either an AI-free track or an AI-friendly track through the class. Students going AI-free committed to not using any AI tools in their work. Students going AI-friendly could choose to use AI on most assignments but only in ways I had specified. They also committed to transparently sharing and honestly reflecting on their experiences with the AI tools.

In general, I think students and I both feel it went well. But reviews were a little more mixed on our AI policy for the course than for collaborative grading. About 75% of students said the policy supported their learning, with an additional 20% saying it “partially” supported their learning, and one student saying that it didn’t really support their learning.

Here are some major themes in their comments (which are shared with their permission):

AI as a safety net

Several students said that just having the option to use AI if they felt they needed it made them feel more secure. For example:

I was on the AI friendly track. The policy helped me knowing that if worst comes to worst I have something to help me understand and give me idea topics.

I was on AI-Friendly, even though I never used AI in this course I really liked having the option to. This supported me by giving me an extra tool to help my writing regardless of if I actually needed it.

This is the first time I’ve received comments expressing this sentiment since I started asking about AI, and I have…some feelings about them. I’m a little uncomfortable with the fact that students are starting to feel dependent on AI, even if it’s just an emotional dependence. I’m sad that it’s the thing they’re turning to “if worst comes to worst”—though I’m not sure what they would have fallen back on before. A peer or parent? A writing center? Me, their instructor? A paper mill? Or just a shoddy product, thrown together in a panic and/or submitted beyond the deadline? Is turning to these options better or worse than turning to AI? And under what circumstances?

AI as unnecessary

At the same time, as the last comment above indicated, most students ultimately felt they didn’t need to use AI tools to be successful in the class—or at least were able to wean themselves off of those tools by the end of the semester:

I was on the AI friendly track for the semester. I think that they really supported my learning and as the semester went on, I stopped needing to use AI.

I was on the AI friendly track, but I ended up completing assignments on my own without that feedback.

I was on the AI friendly track, but then I ended up not using it on almost all of my assignments so I can’t properly judge how it would have affected my learning.

I had the AI track but I didn’t use AI once so probably shouldn’t have chosen that path.

As I mentioned in an earlier post, I didn’t see indications of heavy AI use this semester, so I believe students are telling the truth here. (It’s an anonymous survey, after all.) And as I wrote in that post, there were lots of potential reasons for students’ ultimate rejection of AI. The collaborative grading system likely played a huge role.

Using AI in “a good way”?

There were also a few students who expressed gratitude for the opportunity to experiment with AI for learning:

When I was offered the AI polices, I was so happy to use AI in a good way. And with AI, it has been proven to help improve my writing. There were times when I was off topic, but I had asked AI to help create an outline, and that helped me out a lot when there were times that I was stuck.

I feel like it helped me because I could use AI to help me learn, but not to cheat.

One thing I think students are expressing here is an interest in moving beyond the simple idea that using AI, in any capacity, is an automatic violation of academic integrity. In our in-person conversations, some of them expressed frustration with professors who didn’t seem to know anything about how AI could be employed as a learning tool and, consequently, instituted blanket bans and harsh penalties for “cheating” with AI.

I understand this frustration. That said, I’m also uncomfortable with the level of certainty students express in differentiating between “good” and “bad” uses of AI. First, of course, there’s the ethical questions. Can one ever “use AI in a good way” given the many problems it creates in the realms of labor, intellectual property, the environment, misinformation, exploitation, etc., etc., etc.?

Then, there are the questions around teaching and learning. What does it look like, exactly, to “use AI in a good way” when learning to write? While I suggested some possibilities to students, I don’t think I have a good handle on the answers here. Some uses might be okay for some students and not for others. Some uses might be okay for certain kinds of assignments or cognitive tasks and not for others. Some uses (like outlining, mentioned above) I’m just not sure about yet.

If even I—an educational developer and writing instructor who thinks about these issues pretty much constantly and has years of experience and research to draw on—can’t definitively say what a “good” use of AI would look like, how can I expect students to make those determinations? Especially in their first semester of college?

At the same time, I see students’ point here. None of us may know, at this moment, how AI will affect their learning processes, but they are eager to explore it, and they should have an opportunity to do so. They also seem to appreciate when professors can provide expert guidance on what uses of AI might be appropriate. However, I want to make sure to convey, in future courses, that all such guidance is tentative and context-dependent, rather than definitive—something we continue to explore together.

Commitment and confidence-building

Of the few students who chose the AI-free track, it seems that a handful, at least, were happy that it helped them stay committed and build their confidence as writers:

I was on the AI free track. I think that the course policy supported my learning because it allowed me to come to terms with the style of writing that I see fit for myself. I think it helped me become more disciplined as a writer as well because I knew that I signed a contract to refrain from using AI.

I did AI free. I think that being AI free allowed me to build my own confidence in writing rather than relying on AI to help me with it.

One of the main reasons I wanted students to choose between two AI tracks in the first weeks of the semester was to create a pre-commitment device that might help them stick to their principles even when the pressure was on. It seems that this worked!: students who chose to go AI-free were ultimately committed to that choice.

I also like that students who chose the AI-free track felt that it helped build their confidence as writers. A few years ago, I had a student who was constantly turning to her parents for assistance on writing assignments. In our final conference, I asked why she felt the need to do this, and she admitted that it was because she lacked confidence as a writer. In making this admission, however, she realized (with no prompting from me) that getting this assistance was not building her confidence; on the contrary, it was damaging her relationship to her own writing. She was never able to be proud of anything she had written because she always felt that she owed its success to someone else.

There’s a real danger that, in the future, AI will be this “someone else” for students. One major goal of my first-year writing class is to help students lay a firm foundation for their future as writers by developing self-assured writerly identities. I’m pleased that offering an “AI-free” track helped some of them do this.

AI apathy

As in previous years, there was also a certain level of disinterest or apathy about AI among some students:

I was AI free because I don’t even really know how to use AI

I was on the no AI track. I don’t think the policies necessarily changed anything for me because I’ve never really used AI for English classes.

I saw a lot of this sentiment in past classes—there was much less of it this year. Given the growing ubiquity of AI, I suppose the shift is not surprising.

Techno-exhaustion

Finally, one small but significant thing I noticed in this year’s end-of-semester survey was a sense of techno-exhaustion. (Is that term a thing? If not, it should be.) The comments I’m thinking of here came in response to a new question to my evaluation form, which I added with an eye toward this blog. It read:

I write a blog on alternative grading so that other educators can benefit from hearing about my (and my students’) experiences. I’d love to be able to communicate with readers about things students want them to know. Is there anything you want your college professors to know about your learning or what it’s like to be a college student in 2025?

For the most part, students didn’t have much to say on this question. But one theme was the drawbacks of technology:

It can be very confusing at times due to all of the apps/technology used.

I think that being a college student in 2025 heavily relies on technology. Personally I feel like doing things more hands-on is a better way to get students to obtain information. I think computers increase risk of distraction.

These are the opinions of only two students, a small number in an already-small sample size. Hard to draw any real conclusions here, and I’m sure many students feel differently.

However: I think this may be an underrecognized viewpoint, particularly in our rush to “adapt to generative AI” and create “workforce-ready” students. Some students are already feeling overwhelmed by all the ed tech we require them to use, and throwing AI into the mix adds a whole other layer of complexity. Some also seem to be craving more “hands-on” experiences and personal interactions—which are hard to come by in a world that asks us to access so much of our lives through computer screens.

Just imagine spending 24 hours as a first-year college student at a university like mine these days. You go to your classes, almost all of which contain a minimum of 50 other students. You take notes on your computer (where all your academic work happens) but keep getting distracted by constant notifications. After class, you check the LMS for your academic to-do list. You log into four different platforms to complete homework and quizzes. You get stuck on an assignment, so you ask ChatGPT for help. You get hungry, so you order food through your phone and have it delivered by a robot. You get bored, so you check social media. You get tired, so you turn on a video to lull you to sleep. At no point are you expected to look away from your screen or engage with another human being for an extended period of time.

I’m not saying that college students have no opportunities for unmediated experiences or face-to-face interactions. They do talk to their roommates, hang out with their friends, go to the gym, go out to the bar, whatever. And I’m not saying that smartphones are frying kids’ brains or anything like that.

But I am saying that you can, conceivably, live out four years of your life and get a whole college degree without talking to another living soul or looking away from a screen for more than a few minutes at a time. And I think we probably need to ask ourselves if this is something we really want as educators.

How have your students’ perspectives on AI changed since the initial release of ChatGPT, more than three years ago now? What approaches to AI are working well in your class? I’d love to hear from you in the comments.

All my best teaching insights come from reflecting on a class after it’s over—primarily by thinking back on the grade conferences I had with students and hearing what they had to say on their end-of-semester evaluations. (I make my own evaluation form in order to ask the kinds of questions I’m most interested in.)

Over the last few years, I’ve written about how student perspectives have shaped my own thoughts on the relationship between autonomy and accountability, how we teach reading, generative AI, progress tracking in collaboratively graded courses, and collaborative grading itself.

This year, my evaluations asked questions focused on collaborative grading, as usual, along with questions about the new AI policies I had implemented in the class. This week, I’ll share what students had to say about collaborative grading, and in two weeks, I’ll write about their thoughts on the course’s AI policy. (All comments are shared with their permission.)

Here were some common themes:

Learning over grades

As in past semesters, students confirmed that our grading practices supported their learning in the course, with only one respondent (out of 16) saying it “partially” supported their learning. No one said it did not support their learning. Additionally, as in past semesters, many students reported that the grading system helped them focus on learning and improvement rather than on simply getting a good grade. Based on everything I’ve read about student perspectives on “ungrading,” this is the most common feature of their experience with it.

Student voice and “fairness”

This semester, I had a higher than usual number of students express that they liked “having a say” in their grade and that they thought the grading system was “fair.” I got a few comments like these:

I like that time was taken to hear what we think our grade should be. I think this supported my learning giving me a chance to explain my work and effort from my perspective.

I like how our grade was most times discussed with our teacher about what we thought we deserved and what she thought we deserved also.

I wonder if the focus on student voice here is because several students had, as they told me in the first weeks of the course, bad experiences with grades in high school. For instance, a couple of them mentioned getting low participation grades because their teacher “didn’t like them.” This perception may or may not be accurate, of course. Thinking back to my own high school days, I remember some students making this complaint as a way to evade responsibility for their choices. But I also remember many teachers who absolutely would punish students with bad grades when they engaged in behaviors the teacher didn’t like—even when those behaviors weren’t particularly disruptive or could have been addressed more productively.

Regardless, several students seemed to feel that their past grades were not really representative of their work or their learning, and they appreciated the chance to explain their perspectives to the instructor. Students and I don’t always agree about their work, but I always find it useful to hear their thoughts on it. That is, apparently, a welcome novelty.

Valuing student efforts and feeling “seen”

There seemed to be a feeling among students that their efforts often go unrecognized in traditionally-graded classes. Several students this time around expressed gratitude that the grading system valued their labor and growth alongside the actual quality of their work. A couple noted that this is especially helpful to students who struggle with the material:

Most of my teachers do their assignments based on accuracy, and they don’t actually get to the kids who have a hard time understanding. Your grading scale makes me feel seen as a student because we have those conferences, and that helps out a lot.

I should clarify that effort is not the main criteria we use to determine final grades, but it is an important one. It’s my belief that every student, including those who come to us from under-resourced school districts, should be able to succeed in college if they can demonstrate steady growth and improvement.

Rubrics?

I also got a higher than usual number of comments that referenced rubrics:

The parts that I liked best about this grading system were that each assignment we submitted wasn’t tied strictly to a points based rubric

I do like that our grades aren’t limited to a rubric.

I find these comments a little puzzling, since I do use a kind of rubric for individual assignments, along with a checklist of evidence I would generally expect students to provide for proposing specific letter grades in the course. (Maybe the latter is not quite a rubric, but it seems like one to me.) I don’t think students and I have a shared definition here. Sometimes when they say “rubric,” they seem to mean something like “writing formula”: a list of specific requirements concerning word count, paragraph length, number of quotations, sentence order, etc. that they need to meet to attain an A on a paper. Sometimes they just mean that the teacher provides evaluation criteria to which there are points attached.

Either way, students seem to find these kinds of evaluation criteria limiting. At first, I think they can feel a little adrift without strict requirements for their writing. But by the end of the semester, many students express appreciation for the freedom to make their own writing decisions, without being penalized if something didn’t work well the first time.

Not knowing “where you stand”

As for criticisms of collaborative grading, there’s always at least one student who is uncomfortable with not knowing “where they stand” in the course throughout the semester. For example:

Sometimes I would like to know exactly how you feel I am doing in the class with like a letter grade, instead of establishing it at the end of the semester.

I developed a student progress tracking document in part to address this concern. But I don’t think the concern can be totally eliminated without going back to a running, weighted average. This is what students are used to; it’s what makes them feel comfortable. Given the stakes of grades, some are simply not going to feel secure without one. That’s fair. But I also think that this discomfort is a form of desirable difficulty, a friction that can lead to reflection and growth. As much as I value student ease and comfort, I value student learning more.

The other interesting part of these comments is when students say things like “I would like to know exactly how you feel I am doing in the class” (emphasis mine). Of course, I’m the subject matter expert, and ultimately, I have final authority over the grade. So, “how I feel” about student work is important. But at some point during their college careers, students are going to have to start learning to make their own judgments about their work. While they’ll continue to get feedback from others, they won’t be graded forever. Helping students develop habits of self-assessment at the beginning of their college careers is, I think, beneficial.

Moving beyond school as transaction

Finally, in some of the exchanges I had with students, I got a tiny inkling that the grading system helped a few of them move beyond a “schoolish” or transactional mindset: the idea that the work they did in the course was a series of tokens that could be exchanged for good grades, which might, in turn, be exchanged for a good job. This may be wishful thinking on my part, because it’s really the one main thing I want students to leave my course with, the “threshold concept,” as it were, that allows them to truly experience and benefit from learning.

I do have a spiel I give students about this on the last day of class. They aren’t ready for it in the first weeks. But after a semester’s worth of operating without grades (at least in one arena), some of them may be starting to understand school differently. So, for my parting words, I make a pitch to them that they should start taking their education seriously, as a thing that’s valuable in its own right rather than as a progression of hoops to jump through or series checkboxes to tick on the way to some “indefinite future reward,” as John Warner calls it. I try to sell them on the idea that just “getting through” school—cramming for chemistry and then forgetting everything you ever knew about it after the final, or asking ChatGPT to write your papers for that history survey—is a very expensive mistake. That their future livelihoods are important, but their lives should be about more than making themselves employable.

Honestly, I feel silly standing in front of students on the last day of class, talking like this in such a ridiculously earnest way. It’s so hard to tell how it lands, and I feel like I get a lot of blank stares in return.

But occasionally, a student will say something in a final grade conference that makes me think they were actually listening, and they got it. And sometimes I think that maybe they don’t get it now, but they’ll remember it, and they’ll come back to it later. And of course, some of them will probably never buy it at all. There’s only so much we can do with 3.5 months, working against a system that is, outside a few scattered classes or experiences, all-encompassing.

However, we do what we can. And I think collaborative grading, more than any other single intervention, helps.

I’ll be back in two weeks with more about my students’ perspectives on my new AI policy. Stay tuned!

In Seinfeld, the airing of grievances provides an opportunity for each celebrant to explain how their friends and family have let them down over the past year. In the words of Frank Costanza, “I got a lotta problems with you people—and now you’re gonna hear about it!”

I first observed Festivus on this blog in 2023 to air my own grievances with the process of assigning final grades. In 2024, my colleagues Josh Eyler and joined me on a short recording to air our grievances about grades in general.

We had so much fun with this that Liz and I decided to do it again to end 2025—a year that has been chock-full of grievances for both of us. We figured many of you have had a tough year as well. So, Liz had the great idea to solicit grievances from our readers and friends that we could discuss during our conversation.

Thanks to all of you who shared your grievances with us. And many thanks to Liz for doing most of the legwork to make this year’s airing of grievances happen.

Please note: the thoughts expressed in this recording represent our personal views and not those of our employer, nor any organizations to which we belong.

You can listen to our conversation above or read a transcript of it here.

Happy Festivus!

As an educational developer who promotes alternative grading, one thing I get asked about a lot is how to implement these practices in large classes or with high numbers of students. I have a lot of ideas about this. But because I teach small writing classes, I don’t have much direct practical experience to draw on. Moreover, I think the kinds of alternative grading that suit large classes best are not the kinds of grading I typically practice. While collaborative grading can work in large courses (my forthcoming book features a few people who have done it!), it’s not a great fit for every instructor.

That’s why I’m thrilled today to share some wisdom from colleagues who are making alt grading work in these contexts. I’ve been following the work of Jayme Dyer, Katie Mattaini, and Eden Tanner (who teaches at my own institution) for a while now. Each one of these instructors are using innovative grading methods in their STEM courses, often with many students at once. In this guest post, they have some advice for others who would like to explore the possibilities—just in time for spring semester planning.

So you want to try alternative grading. Maybe you’re intrigued by the promise that Alternative Grading can improve student learning and students’ relationship to learning. Maybe you’re tired of students gaming the system, seeming to focus more on their grade than their learning. Maybe you want to spend less time hating grading and more time helping students learn. Whatever brings you to Alternative Grading, you want to give it a try.

The problem is that you teach large classes. How do you implement alternative grading at scale?

In this post, we propose guiding principles and suggest concrete recommendations to help you design a grading system that better supports your students within the constraints of large class sizes.

Prioritize your goals

Large class sizes constrain what you can do in your grading system. Things you could do in a class with 10 or 15 or 30 students – like providing individualized feedback for each assignment, or coordinating with each student to arrange exam retakes – become much more challenging (if not logistically impossible) with 80 or 150 or more students.

However, we view alternative grading in large classes as logistically challenging, but not impossible. Importantly, large classes reduce the margin of error when trying out new grading systems. Just like cooking for a family of four can involve some experimentation (if you burn the rice, it’s easy enough to substitute bread or even scrap the meal and order pizza!), cooking a meal for a wedding with 200 guests has much less room for error. This doesn’t mean your grading system must be perfect before you deploy it in a large class! However, it does lead to three guiding principles:

1. When implementing a new grading system, start with small changes and plan to iterate

This is true for any class size, but especially important in large classes. Failures in your grading system can harm students; the more students you have, the bigger the impact if your grading system flops. So if you start with small changes, the risk associated with unforeseen hiccups will be smaller.

2. Identify and prioritize your goals

Starting small means focusing on only the most potentially-impactful changes in your grading system. What changes should you make? The answer depends entirely on your values and your students.

There are many reasons that instructors implement Alternative Grading strategies. Here is a non-exhaustive list:

• You want the final grade to reflect students’ learning, not behavioral compliance

• You want to reward students for learning, regardless of when it happens

• You want to level the playing field for students with different levels of preparation

• You want to promote real learning and measure real learning instead of one-off performance

• You want to improve students’ relationship with the learning process, including failure

• You want to provide different ways for students to learn and demonstrate learning

3. Context matters

Additionally, we also think it is essential to identify the needs of your particular students. One of us (Katie Mattaini) has used alternative grading at two different institutions. At one, more students struggled to pass the class, so she designed her grading system to help them identify areas of weakness and subsequently learn more so they could earn a passing grade. At the other institution, many of her students were high-achievers who carried significant grade-related stress, so she designed her grading system to support learning while reducing their stress about grades.

Your values and your students are unique to your context. What brought you here? What matters to you? Who are your students and what do they need? What problem are you trying to fix? Teaching large classes means you have significant constraints, and besides, change is hard. That’s why we think it’s important, from the first stages of designing your grading system to the process of iterating for future semesters, to be grounded in your why. Keeping your goals front and center will guide your decision-making process as you implement new grading strategies in your unique context.

Practical advice

Get student buy-in early

It’s important for your students to understand why you’re using alternative grading practices because it leads to buy-in to your grading system. If you’re open about your approach, your students will be invested - and maybe even excited - for the opportunity to escape the typical grading hamster wheel. Take some time early in the course for students to discuss the effects of grades on their learning and mental health, and share your values and the decision-making process that led to your grading strategy choices. If your grading strategies are supported by data, consider sharing the data with your students, even if it’s “only” from student surveys in previous semesters. If students hear good things about the grading system from their peers, they may buy-in more easily. If you have Teaching Assistants, it’s important to get their buy-in too, especially if they help with grading.

Use technology

The larger the class, the more time you can save by using technology to automate processes. You could use technology to help with grading,1 provide automated feedback, deploy and collect assignments, or establish an external gradebook to help students assess their progress if your Learning Management System can’t be appropriately wrangled for your grading system. Your institution’s teaching & learning center or edtech teams can be great resources to find out what’s available. Also, talk with other practitioners, for instance, on the Alternative Grading Slack or the Biology Grading for Growth group, to see how they’re leveraging technology.

Set limits on reassessments

The opportunity to use a feedback loop to reattempt assessments is a core feature of most alternative grading schemes, but providing infinite chances with no boundaries can backfire. Create some positive pressure by setting limits. You can limit the time window students have to retake the assessment, limit the number of attempts they get, or limit how they can access a reattempt (such as via a token system, exam wrapper, or reflection task). You can use just one of these strategies, or combine them, to create positive pressure to help keep students on track.

Be creative about providing feedback

In larger classes, providing one-on-one feedback to each student may be difficult to impossible. You can consider alternative means of providing feedback, including rubric-driven self-feedback and peer-feedback, collective feedback given at the beginning of class, or video feedback for common misunderstandings. To simplify the grading process for non-autograded work, consider streamlining the feedback you provide. For example, you can mark components on a 2-level scale such as having “met” or “not met” explicit standards, or you can use a 4-level scale such as EMRN.

Leverage on-campus resources

Talk to your Center for Teaching and Learning or similar to find out what systems or resources already exist that you can leverage in your courses. Some examples of what you might find are learning assistants or peer tutors, supplemental instruction programs, helpdesks staffed by teaching assistants, or assistance with reviewing your syllabus or setting up your Learning Management System to better handle alternative grading practices.

You can do it!

You face real constraints that limit what you can do in your classroom. Your class size may be so large that providing individualized feedback on student work is prohibitive. You may not have teaching support in the form of teaching assistants. You may need to coordinate with other instructors across multiple sections. If you are VITAL (Visiting, Instructors, Temporary, Adjunct, and Lecturers), you may have limited power to make changes to your grading system. If you do make changes, you may receive push-back from other faculty, or from students, who are accustomed to traditional grading approaches. Your identities may affect how much push-back you receive.

However, there is almost always room to make positive change. Identify the rules, whether written or unwritten, and identify where you can push. What is immutable? What is not? Take small bites. Try one small thing, in just one class. Get creative. There is no recipe that fits everyone, students or instructors! Keep your goals front and center.

We’ve done it. None of our grading systems are perfect, but we’ve seen the positive impacts on our students. You can do it, too.

How we do it

Jayme Dyer teaches biology courses up to 45 students using alternative grading at Durham Technical Community College. She writes about her use of multiple grading schemes and other nontraditional grading strategies on her Substack and shares resources for instructors on her website.

Katie Mattaini teaches biology courses up to 70 students using alternative grading at Tufts University. Her materials are shared publicly on the Grading Conference website. See Mattaini intro bio syllabi in the Biology Alternative Grading Syllabus Repository. (Other repository links can be found on the conference’s Resources page.)

Eden Tanner teaches chemistry courses up to 170 students using alternative grading at the University of Mississippi. She talks through her process for alternative grading a 170-person General Chemistry course in two podcast episodes, on Intentional Teaching with Derek Bruff and on The Grading Podcast with Sharona Krinsky and Robert Bosley.

*A note from Emily: the Grading for Growth book and blog also have several examples of how alternative grading can work in large(ish) courses, including this one from Jennifer Momsen’s introductory biology course.

A note about the Substack platform from the authors. We had several conversations about whether we wanted to write a post that was published on Substack, given Substack’s current stance on publishing pro-Nazi and racist content. We decided to write this guest post because we think alternative grading can help move the needle in a more just and equitable direction, but we nevertheless want to collectively express our discomfort with Substack for choosing to promote and platform Nazi rhetoric. Notably, one contributor to this post chose not to list their name as an author out of a moral objection to Substack’s pro-Nazi stance.

In other news: this week, I appeared on The Opposite of Cheating podcast with Tricia Bertram Gallant. You can watch the video here or listen on Apple Podcasts here. Tricia and I talked about AI and alternative grading, and I shared a story about cheating from my high school days. During our conversation, I also lament the lack of support for grad students in higher ed (another grievance!).

And finally, something to look forward to: soon, I plan to share a guest post authored by three of my favorite STEM instructors about alternative grading in large classes and how they manage it. This is something I get asked about all the time but have little direct experience with, so I’m excited to feature a few folks who can share some practical know-how. Stay tuned! And don’t forget to share your grievances with us:

Air your 2025 grievances

I’m happy to be back on the blog this week after being MIA for the entire month of October. (My apologies—we are moving from crisis to crisis here in Mississippi, plus, I have a book manuscript due at the end of the year, and another one due in late 2026…) Since I last wrote, I’ve appeared on two podcasts:

Sharona Krinsky and Robert Bosley from The Grading Podcast (and Center for Grading Reform) invited and me to join them for a conversation about neurodivergence and alternative grading. You may remember that earlier this year, Sarah and I wrote a three-part series on that topic:

Unmaking the Grade

Neurodivergence and Alternative Grading, Part 1

Note: This post begins a three-part series on neurodivergence and alternative grading by Sarah Silverman and Emily Pitts Donahoe. Each post is co-authored and cross-posted to both Sarah’s newsletter Beyond the Scope and Emily’s newsletter Unmaking the Grade…

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9 months ago · 20 likes · 7 comments · Emily Pitts Donahoe and Sarah Silverman

Beyond the Scope

Neurodivergence and Alternative Grading, Part 2

Note: This post is the second in a three-part series on neurodivergence and alternative grading by Sarah Silverman and Emily Pitts Donahoe. Each post is co-authored and cross-posted to both Sarah’s n…

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8 months ago · 10 likes · Sarah Silverman and Emily Pitts Donahoe

Beyond the Scope

Neurodivergence and Alternative Grading, Part 3

Note: This post is the third in a three-part series on neurodivergence and alternative grading by Sarah Silverman and Emily Pitts Donahoe. Each post is co-authored and cross-posted to both Sarah’s ne…

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8 months ago · 4 likes · Sarah Silverman and Emily Pitts Donahoe

We had a lot of fun digging into what we found with Sharona and Bos!

Additionally, I, along with my University of Mississippi colleagues Josh Eyler and , had the opportunity to talk with John Kane and Rebecca Mushtare at Tea for Teaching about faculty perspectives on AI. We had a rich conversation that left me with a lot to think about. I’ve continued mulling over our discussion as I’ve been teaching first-year writing this semester.

Back in July, I wrote about how I planned to handle AI in my classroom this fall, and in September, I shared some of my AI-related materials. If you want full details about what I’m doing, feel free to explore those posts, as well as the “Use of Generative AI” document I gave to students this semester. But here’s the short version:

At the beginning of the semester, I asked students to make a public commitment to one of two tracks in the course, AI-free or AI friendly. (Language for the latter track was inspired by Noël Ingram.) Students on the AI-free track commit to avoiding intentional uses of generative AI for writing support. Students on the AI-friendly track are allowed to use AI in limited ways, which I specify, for most assignments. They are also required to disclose their use of AI—sharing a summary of how they used it, along with their chatlogs—and write a short reflection on what they think they gained or lost from the process.

I went into the semester reasonably confident that this would be a good system, but I won’t lie: the first two weeks shook this confidence considerably. More students than I expected (about 75%) chose the AI-friendly track. This didn’t seem to align with the orientations of my previous classes, in which many students were ambivalent about AI. Moreover, students expressed a lot of enthusiasm about AI’s potential to assist them in the learning process—enthusiasm I thought was unwarranted. The kinds of things they said about this technology in our conversations made me worry that they would have a hard time distinguishing between what helped them generate a polished paper and what helped them actually learn to write.

At this point, however, I’m happy to report that my fears were mostly unfounded. I’ve had to have a few uncomfortable conversations about AI misuse, but for the most part, I’m finding that students are still pretty ambivalent about AI when they feel well-supported in their learning. In fact, many of the students on the AI-friendly track appear not to be using AI at all, and most are not using it extensively. Here’s what I think is helping:1

We work up to the longer papers. My class begins with a series of Rhetorical Analysis Exercises, which are more like worksheets in which students answer questions about the things they read. I think tackling one question at a time, as opposed to having a big, blank page to fill, helped encourage students to work on their own. They didn’t get in the habit of using AI from day one.

We do lots of in-class writing and have plenty of opportunities for revision. Hard to misuse AI when you have focused work time during which the teacher is looking over your shoulder. Throughout this time, I walk around and check in with each student individually to address any concerns. Students also know that if they don’t do so well on the first attempt, they can keep working up until they submit final portfolios at the end of the semester.

There are clear guidelines and suggestions for AI use. I’ve provided students with a list of ways I think it might be acceptable to use AI (getting feedback on your draft, brainstorming potential counterarguments to your position, etc.). Now that they’ve fully internalized the guidance, they do tend to use AI in these ways—lowering the chances that they’ll use it to generate whole essays or paragraphs.

Students know I can see their Google Doc version history. Ok, so this is the one “assessment security” measure I permit myself. Let me caveat this by saying that if you’re relying on version history to “catch” AI use, you’re going to make a lot of damaging false accusations, particularly of people whose writing process may differ from the norm. (More on that here, and in the conversation Sarah and I had on The Grading Podcast earlier this month.)

That said: if something seems off to me and a student hasn’t provided their chatlog or any information about using AI on the assignment, I do dive into their version history to see how the piece came together. This allows me to say things like, “I notice that you’ve copied-and-pasted some material that looks AI-generated to me. Can we review your chatlogs together to make sure AI isn’t inhibiting your learning?” Nine times out of ten, when pressed, students default to “I’ll just redo the assignment!” and we don’t even get to the chatlogs. I don’t really like doing this. But I think it helps that…

We talk about AI in non-punitive ways. Half the reason I want to see how students are using AI, truly, is because I am genuinely curious about how it might be affecting their writing process and their learning. I am just as concerned that AI will give them bad writing advice as I am that they will use it to cheat. So, that’s what I tell them. And we’ve discussed, as a class, a few occasions on which AI kind of led a student astray in their work. I think this really helps them feel more comfortable with being transparent about their use.

Students know I know their voices, and we have lots of face-to-face conversations. We had a brief in-class discussion about AI the other day, and one student observed in passing that I, as the teacher, could tell when they used AI because it didn’t sound like them. I try to convey that I’m paying close attention to this by crafting feedback that includes phrases like, “I’ve noticed that your writing tends to ___” or “One interesting thing about your writing style is ___.”

I also try to have individual conversations about AI mis- or overuse in person as much as possible—usually before/after class, during work time, or, when necessary, during office hours. Students know that I will notice when they use AI (or at least when they use it in lazy ways).They also know that they will likely have to talk to me about it face-to-face at some point. Under these conditions, I think it’s rare that a student will brazenly submit fully AI-generated work.

All that said, I have run into a few obstacles that are worth noting. Here’s what’s not working so well:

Students need a lot of reminders and reinforcement. In the first half of the semester, the handful of students who were using AI seemed to struggle to remember the rules and guidelines we have in place: you can only use it in specific ways and at specific times, you must share your chatlogs, you have to fill out the AI reflection section of your assignment, etc. Students weren’t, as far as I could tell, trying to circumvent the rules; they were genuinely confused, despite what I thought was clear guidance. I suspect this is because they had a lot of cognitive overload trying to manage several different classes (each with different AI policies) while also adjusting to college life. Things are better now, but getting to this point was a little rocky.

Students don’t use AI particularly well, even with guidance. I gave students specific copy-and-paste-style prompt language for the first assignment. After that, I provided only suggestions about ways students might use AI, not step-by-step instructions or particular prompts. This works fine for some students; others have seemed confused about what to do with these more general suggestions. Or it becomes clear to me, when looking at their chatlogs, that they don’t quite know how to get the most out of AI assistance. For example, occasionally I’ll see students entering prompts that are more like the kinds of phrases you would put into a search engine, even when they aren’t using it for search-like functions. Others have prompted it in ways too vague to be very useful.

All this presents a problem for me because while I want them to be able to explore AI if they would like to, I am not particularly enthusiastic about AI assistance myself, and I don’t want to spend class time teaching it. I was hoping this would be a good opportunity for some self-directed learning, but I’m having only limited success in facilitating it.

Some students have been reluctant to share their chatlogs. In the past few weeks, some of my students forgot, or “forgot,” to log in to whatever AI platform they’re using, and therefore weren’t able to save and share their chatlogs with me. In these cases, I ask students to explain, in detail, how they used AI. Some have offered perfectly clear and satisfactory explanations, which lead to fruitful conversations. Others have been vague about their use. In these cases, I simply explain that I don’t have a good sense, from our conversation, about whether or not AI has impeded their learning, and ask them to re-do the work. Almost everyone has reacted to this request with more gratitude than annoyance. But monitoring their AI use still takes up more time and brain space than I would like.

Student reflection on AI use is still not as robust as I would wish. Again, I was hoping to engineer this primarily through carefully-crafted policy and reflection questions, but it’s clear to me now that some students need a lot more support to reflect effectively. They need sample reflections, modeling, lots of class discussion on the issue where they might hear and consider other perspectives or experiences. As I noted above, however, I’m reluctant to spend too much time on AI in class, since I don’t think it’s very important for developing writers and most students don’t want to use it extensively anyway.

All in all, I think this is working better than my previous system in that student use of AI is still low, it’s more transparent, and it’s somewhat more intentional. But there are still some things I’m not entirely happy with.

How are things going with AI in your classes? If you’re permitting AI use, how are you balancing the desire for students to develop critical AI literacy with the need to help them build the knowledge and skills essential to your discipline? I’d love to hear from you in the comments.

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I always appreciate when people are willing to share the materials they create for their courses. It’s one thing to hear people talk about what they do and why they do it; it’s another to actually see the documents they’ve created and file them away as models for my own courses.

Today, I’m sharing two documents I’m currently using in my Writing 101 course: an updated version of my course progress tracker and a brand new document that contains guidelines for AI use in the class and an AI use contract. The semester is not even halfway over, of course, so it feels a little vulnerable to share them—I have no idea if they’ll actually work out in the end. But so far, there are a lot of things I like about the materials.

Before I get to that, a couple of updates and other resources:

Teaching in Higher Ed: “Learning About Grades from an Emerging Failure”

I’ve been listening to the podcast Teaching in Higher Ed for years, so I was thrilled when host Bonni Stachowiak reached out to ask if I would participate in a special episode. Bonni had recently had an interesting conversation with her fifth-grade daughter Hannah about grades and writing, which she recorded. On the podcast, I joined Bonni to discuss this conversation through the lens of “grading for growth,” a framework developed by Robert Talbert and David Clark.

It was such an honor and a joy to be in conversation with Bonni and her daughter. You can listen to the episode here or on your favorite podcast app.

The Alternative Grading Institute, December 17-18

I’ve also been working on another project that may interest some readers of this blog: a virtual Alternative Grading Institute. This two-day online event, sponsored by the Center for Grading Reform, is an “intensive, hands-on, online experience where faculty learn core concepts of alternative grading and build a course-ready grading scheme.” On the first day, participants will focus on aligning their grading practices with their pedagogical values and contexts. On the second day, they’ll build an alternative grading system for their chosen course, with guidance and feedback from peers and institute facilitators.

I’m collaborating with a truly all-star team for this event. During the Institute, Drew Lewis and Derek Bruff will work with participants interested in standards-based grading. Michael Palmer and Adriana Streifer will work with those interested in specifications grading. And Lindsay Masland and I will work with those interested in collaborative grading (sometimes called ungrading).

The Institute is open to higher-education instructors from all institution types, with a registration fee of $200 or pay what you can. Applications are due October 15, and we’ll only be able to accept a limited number of participants.

If you’d like to learn more about the Alternative Grading Institute, or apply to join us, you can find more information here.

And now, without further ado, my course documents:

The Progress Tracker 2025

Here is the Progress Tracker I shared with students this fall. You can navigate the various sections of the document using the tabs on the left.

Back in 2023, I wrote about a new document I had developed to help students track their progress throughout the semester. Over the course of five posts, I explained the different facets of the document and how they worked:

• Post 1: Introducing the Progress Tracker

• Post 2: Helping Students Track Learning Tasks

• Post 3: Helping Students Track Attendance and Engagement

• Post 4: Helping Students Track Learning and Growth

• Post 5: Helping Students Determine Final Grades

The progress tracking document I linked to in these posts has since gone through two revisions. The one I’m using now is primarily different in that it places more emphasis on the three metrics I want us to use, as a class, to measure students’ progress and determine their final grades:

• Quantity: how much work did you do?

• Quality: how good was the work you did? (i.e., how close did it come to reaching our learning goals)

• Growth: how much improvement have you shown over time?

Additionally, the self-assessments I have students complete throughout the semester are built into the document, so that when they go to write about their progress across the semester, all the information is right there.

But I think maybe the biggest individual change is that I now have an expanded table/checklist to help students make a reasonable case for their final grades:

Part of the reason I’ve expanded this over time is that many students a) felt uncomfortable determining their grade without clearer guidelines and b) tended to emphasize one category over another in their grade proposals.

Specifically, some students turned in work that wasn’t their best but proposed A’s (at least for their midterm grades) simply because they had completed all the assignments, more or less on time. I think this is mostly because I’m teaching first-year students. I suspect they’re used to checking off the assignment boxes and moving on—and not necessarily used to putting in lots of effort on homework. In any case, I wanted to emphasize that quality of work was just as important as quantity, even if it’s difficult to measure.

I have mixed feelings about this table. It feels a little more like a contract than I want it to. To be clear, students don’t have to check off every box in a specific category in order to propose that grade for themselves. If they miss a lot of classes, for example, but still complete most of their assignments and do high quality work, they can still propose an A. The table is meant to provide a set of guidelines rather than hard-and-fast rules. But I worry a little that its contract-like nature promotes the kind of transactional mindset I’m trying to get away from.

On the other hand, the expanded table has the advantage of clarity. Students know in the first week of class what they need to do in order to make a reasonable case for the grade they want. We have a shared set of expectations from the very beginning, and that’s a good thing.

So far, the biggest challenge, as always, is getting students to actually keep up with and use the document. I think that will improve after their first self-assessment/grade proposal next week. I’ll report back after the semester’s over to share what I’ve learned and what changes I’m planning to make.

New AI Guidelines and Use Contract

Here is the “Use of Generative AI” document I shared with students this fall. You can navigate the various sections of the document using the tabs on the left.

In the past, I have discussed AI with students, collaborated with them to develop a set of class guidelines for AI use, and then allowed them to make their own choices about whether and how to use it (as long as they stayed within the guidelines).

I wasn’t, however, entirely satisfied with that approach, as I’ve mentioned before. The main reason is that most students weren’t very intentional about their AI use. I had several students turn to ChatGPT only when they felt stuck or panicked about completing their assignments, at moments when I think sitting with that discomfort a little while longer would have benefited them. I had other students who used AI frequently but weren’t very reflective, despite regular prompting, about how use of AI was affecting their learning.

This time, I’m asking students to make an intentional commitment to go “AI free” or “AI friendly” in the class. Students on the AI free track won’t use AI chatbots or reading/writing/research assistants at all. Students on the AI friendly track can use these tools when they want to, but there are several caveats:

• Students commit to using it only in a limited number of ways (which I specify) for each assignment

• They disclose their AI use to me by briefly describing it and sharing their chatlogs

• They spend a little extra time reflecting on how their use of AI affected their learning after each assignment and on our regular self-assessments

All this is laid out in the AI use document I created over the summer. It contains…

• Student guidelines for going “AI free”

• Student guidelines for going “AI friendly”

• A “Use Case Library”

• An “AI Use Contract”

In the “Use Case Library” section, I list approved uses of AI for specific assignments in the class. In the “AI Use Contract” section, students make a public commitment to their chosen AI track, and I make a few commitments regarding AI as an instructor (mostly that I won’t use it). The contract also contains a section that spells out the potential consequences for AI misuse. (We developed a plan for addressing AI misuse collaboratively, as a class, and I expect it to change the next time I teach—so, I’ve left the particulars of that section out of the shared document.)

Like all first drafts of course documents I create, the whole thing seems too wordy to me. It also feels like it has an air of desperation in trying to control something that is ultimately out of my control. I cannot, in the end, make students use or not use AI in the ways I have suggested, and if they are clever enough to hide their misuse, I can’t really enforce any consequences for it. I’m well aware that some, maybe many, students will use AI in ways I know nothing about.

However, if students are interested in exploring AI, I want to be clear about what I think is appropriate or inappropriate for this class. This, so far, is the best way I can think of to do that.

Hard to say how this document is working so far, and I can report back later. But here are some preliminary notes:

Last year, most of my students were not that interested in AI and, I believe, would have largely chosen the AI free track. This year, my students are very interested in AI. About 25% opted for the AI free track, meaning 75% are going “AI friendly” for the class. I’ll admit that these numbers made me a little nervous at the start of the semester.

So far, however, most of my students haven’t used AI at all, even if they initially chose the AI friendly track. I suspect that’s mostly because we’ve been concentrating on rhetorical analysis, where (in my class) the assessments are more like worksheets than papers. I expect to see more AI use when we get into writing full-length arguments. But at least in the first half of the semester, I’ve been impressed with students’ wise judgments about where AI is and is not appropriate. That’s not to say there haven’t been any problems—just that they are far fewer, and more easily resolvable, than one might expect.

The biggest issue has been explaining the document and making the rules clear. Some students were initially confused about how to navigate the “Use Case Library.” I think we’ve (mostly) sorted out those issues now, after a lot of reminders, and I’m hoping the rest of the semester will go smoothly.

Things continue to be hard in higher ed right now, and I hope you’re holding up wherever you are and whatever you’re dealing with. I’d love to hear your feedback on the documents above, so please leave a comment if you have thoughts. And I hope to see some of you at the Alternative Grading Institute later this year!

I think it’s safe to say that it’s been a rough week for many of us in higher ed.

I started the week, as I often do these days, managing the implications of some recently passed anti-DEI legislation here in Mississippi (though some sections of the law are currently under a preliminary injunction). On Tuesday night, I learned that one of my former graduate school colleagues was fired from her position at Texas A&M for daring to discuss the subject of gender identity in a class on children’s literature. Yesterday, a staff member at my own university was fired for re-sharing a post, on her personal social media account, about the murder of Charlie Kirk.

I guess I’d better clarify, at this point, that the opinions shared in this newsletter are my own and do not represent the views of my institution—though I’m not sure such a disclaimer will do me any good. What is there to say about these events that has not already been said? What is there that I can say without fear of retribution? Am I still allowed to quote John Dewey?:

“The years immediately ahead will be characterized by struggle...If there be any teachers who chose their profession because they imagined that in it they might stand securely aside from the turmoil of battle for power, they will probably find the next decade or several decades very dismaying.

A free education is incompatible with fascism. Education is likely to be one of the great battlegrounds upon which is waged an intense and desperate struggle for power.”

I encourage you to use the remaining five minutes you might have spent reading a full length post here to stand up, in some small way, for “a free education.” Donate to the GoFundMe for the instructor fired at Texas A&M. Join the AAUP. Join United Campus Workers. If you don’t have a chapter for either of these, look into starting one. Ask a colleague whose discipline or job is under threat to get coffee and strategize. Explore some resources on academic freedom. Subscribe to the newsletter of the Center for the Defense of Academic Freedom:

Academic Freedom on the Line

This newsletter examines academic freedom, its role in democratic society, and what is lost when academic institutions face politicized attacks on institutional autonomy and shared governance. Views expressed are those of the authors, and not the AAUP.

By Isaac Kamola

Our students should be free to learn about important concepts in the disciplines they’re studying, and we should be free to teach them. The only way we’ll win these freedoms is if we fight for them.

Note: This post begins a three-part series on neurodivergence and alternative grading by and . Each post is co-authored and cross-posted to both Sarah’s newsletter Beyond the Scope and Emily’s newsletter Unmaking the Grade. Please consider subscribing to one or both to receive more posts in this series.

This collaboration grew out of a casual conversation. Earlier this summer, Sarah mentioned to Emily that she had increasingly been getting questions about how neurodivergent students experience alternative grading. Emily had also noticed the topic coming up in interviews for her book and other conversations about alternative grading.

We compared notes, and admitted that we had a bit of whiplash from the ideas we had heard so far. Some alt-graders believed their methods were especially supportive of neurodivergent students, but for all different reasons. Others questioned whether alternative grading created some barriers for neurodivergent students, especially on the topics of “unclear expectations” and “insufficient structure.” To the extent that neurodivergent students benefit from a high degree of transparency and clarity of expectations, might methods like collaborative grading, which uses an interactive process between student and instructor to determine the course grade, lack this clarity? How do “due dates” factor in? Since so many alt-graders eschew or limit penalties for late work, how might neurodivergent students who rely on external sources of motivation and accountability experience their classes?

Sarah’s stake and interest in this topic is part of her broad interest in neurodiversity in teaching and her experience as an alt-grading instructor who herself is Autistic. In Sarah’s words:

“I have always felt that traditional grading systems are a type of ‘steep steps’ (to use Jay Dolmage’s metaphor) for students in higher education—they are a barrier that excludes, whether by design or inadvertently. Coming into this collaboration with Emily, I had two main reasons that I think alternative grading relates to neurodiversity. The first is that the way that traditional grading often measures student performance can easily slide into ranking students by ability, a precursor to norming and the removal of students who do not fit in. The second, based on some of my own experiences as a neurodivergent student, is that alternative grading usually offers tolerance for error, a principle of universal design in which there are fewer adverse consequences for mistakes or other unintended actions.”

Emily, who is neurotypical, came to the topic with concerns about how her collaborative grading system and the practices she recommends in her in-progress book might be affecting neurodivergent students:

“I find that for many students, the motivation to get good grades is at odds with the motivation to learn; the former tends to impede the latter. By minimizing grades in my classroom, I hope to help students engage with the material more deeply. I also hope to give those who don’t fit the mold of a ‘good student’ a fair chance to succeed in my class and to help every student discover a sense of agency and ownership over their education.

I think many of my grading practices support the success of neurodivergent students. But after reading more about neurodivergence, and learning about others’ experiences with it, I started to wonder if some of those practices were actually barriers to some neurodivergent students’ success. Does the lack of grades remove an important source of structure or motivation? Do some students experience ‘agency’ as overwhelm? What if my attempts to give all students ‘a fair chance to succeed’ actually make success less likely for some neurodivergent students?”

We set out to do some digging and thinking about the existing discourse on alt-grading and neurodivergence. This started with a shared Google document in which Sarah linked any source she could find—blog posts, social media posts, peer-reviewed articles, and even a book—that discussed neurodivergence in relation to alternative grading. We continued with individual close readings of each of these sources, after which we added notes to a shared document. For many of the sources we reviewed, we collaboratively annotated key quotes and sections using Google docs. This proved to be a useful method to fully understand the arguments authors of the sources were making, as well as to understand the way that terms like “ungrading,” “alternative grading,” and “neurodivergent” were being used by the authors.

We then wrote down and discussed some of the conclusions we reached based on our reading and our shared knowledge of alternative grading and neurodivergence in higher ed. In our next post, we’ll share some specifics about what we learned from this process. But for now, here are some big picture takeaways:

“Alternative grading” and “neurodivergence” are capacious categories.

The term “ungrading” has at least three different meanings, and it’s not always clear which of those meanings is the intended one in any given piece of writing. While the term “alternative grading” has a more widely-agreed-upon definition, it’s still pretty vague: an “alternative grader” might employ any number of practices, some of which can look very different. Using standards-based grading in an introductory math course is not really the same as using collaborative grading in an advanced creative writing seminar. The primary thing these approaches have in common is that they don’t look like “traditional” grading. We could easily imagine a particular student thriving in one of these environments and struggling in another.

The same thing applies to “neurodivergence.” The term is used differently by different writers, and sometimes acts as a stand-in for a particular group of neurodivergent learners. Generally speaking, neurodivergence is an umbrella concept that refers to cognitive styles that differ from societal norms. Autism, ADHD, dyslexia, Tourette’s, and several other mental disabilities are commonly understood as forms of neurodivergence, and mental health conditions are sometimes also included. Neurodivergence is not a medical diagnosis but a sociological concept. Thus, students and faculty may be neurodivergent even without a specific diagnosis if their way of thinking, communicating, or behaving differs significantly from the norms of a given context. When writers discuss their experiences with alternative grading and neurodivergent students, they might know a bit about these students, their needs, and even their specific neurotype, but they also may not.

So, what we mean when we say “alternative grading” “works” or “doesn’t work” for “neurodivergent students” is highly dependent on the contextual meaning of those terms. It’s difficult to make broad claims about the efficacy of alternative grading or the experiences of neurodivergent students—much less the relationship between the two.

Despite the capaciousness of these terms, we still think “neurodivergence” and “alternative grading” are useful categories. For one thing, both are of growing interest in higher education. Additionally, there is enough overlap in the characteristics encompassed by each term that some generalizations may be possible. In fact, we do come to some broad conclusions about how alternative grading may support or fail to support neurodivergent students below and in subsequent posts.

However, we urge instructors to keep the many meanings of “alternative grading” and “neurodivergence” in mind when discussing these topics and to be as specific as possible when making claims about the relationship between them.

There isn’t yet much research on neurodivergent students’ actual experiences of alternative grading.

Research on both alternative grading methods and the experiences of neurodivergent students in higher ed is still in early stages, at least compared to some other topics in the scholarship of teaching and learning. We haven’t been able to find any published large scale studies that look at neurodivergent students’ experiences of alternative grading. Much of what has been written on this topic draws on anecdotes and personal experience—particularly the authors’ own experiences of neurodivergence as a student, teacher, and human being. These personal experiences are a valuable source of information, but instructors might be less familiar with how to apply them to their teaching than more formal SOTL or learning science studies.

Personal anecdote is especially difficult to apply when thinking about neurodivergent students because of the fact that the category “neurodivergent” doesn’t describe a single set of needs, strengths, or challenges. A student with one diagnosis may have a very different set of needs from a student with another, and even students with the same diagnosis or identify may have differing needs. In short, if you’ve met one neurodivergent student, you’ve met one neurodivergent student.

Based on our own reading and personal experiences, we have some ideas about what aspects of alternative grading might benefit or harm neurodivergent students with specific strengths and challenges. But we need more research on how these students actually experience alternatively graded classes.

We think alternative grading can and does work for many neurodivergent students—but there are some cautions worth taking seriously.

Based on what we’ve read about this topic so far, and our own experiences as teachers and learners, we think that many features of many alternative grading systems do support learning for many neurodivergent students. After all, as Sarah suggested above, traditional grading does tend to present “steep steps” for students, and alternative grading can help increase accessibility. But there are some practices common to alternatively graded classes that may present barriers for some neurodivergent students if not carefully managed. For example, as we will discuss in our next post, flexibility with pace and due dates should probably be accompanied by significant executive functioning support and forms of extrinsic motivation. While we believe alternative grading may increase inclusion, we should explore and implement it in ways that are neurodiversity-informed.

In our next and second post in this series, we’ll get into some close readings and analyses of several different perspectives that engage the topics of neurodivergence and alternative grading, showing more specifically what some of the benefits of alternative grading may be for neurodivergent students as well as some of the caveats for implementation. In a third and final post, we will discuss some questions that remain and where we hope research on neurodivergence and alternative grading will go next. We hope you will continue to join us on this journey!

If you are ready, you can continue on to Post 2 in this series: Some existing discourse:

Beyond the Scope

Neurodivergence and Alternative Grading, Part 2

Note: This post is the second in a three-part series on neurodivergence and alternative grading by Sarah Silverman and Emily Pitts Donahoe. Each post is co-authored and cross-posted to both Sarah’s n…

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8 months ago · 9 likes · Sarah Silverman and Emily Pitts Donahoe

In the last Unmaking the Grade post, I was thrilled to share ’s reflection on how she employed “AI Tracks” in her spring course. I had suggested a similar approach in a post last fall but hadn’t had a chance to try it myself. I’m grateful that Noël and others were willing to test drive the idea before me so I could learn from their experiences!

And I learned a lot.

In case you missed these earlier posts, here was my basic idea: I would ask students, at the beginning of the semester, to select a specific AI track. One track would be AI Free, meaning that students would commit to employing no AI tools in their work for the course. One track would be AI-integrated (or, as Noël termed it, “AI Friendly”), meaning that students could employ AI in limited ways and were required to disclose and reflect on their use of it.

Read more about the original idea here:

Noël employed this approach in an asynchronous online course called “Design Thinking and Creativity.” Most students, interestingly, wanted to be AI Free at the beginning of the course, citing a variety of reasons for their choice. Unfortunately, this desire was short-lived. Soon, Noël was getting a barrage of emails from students wanting to know if particular tools or usages would “count” as generative AI.

But more than that, she found what constitutes AI use, or even an AI tool, is a lot more slippery than it used to be. “Using AI” is no longer a simple matter of navigating to ChatGPT or another external site to prompt the chatbot and copy-and-paste its responses. Instead, AI assistance is being built, seamlessly, into applications that students use every day: Canvas,1 Google Docs, Grammarly, Canva, library databases, etc. Sometimes, students in Noël’s class didn’t even know that they were using AI.

All these factors complicated the “AI Tracks” approach, and Noël decided she wouldn’t be using it again in the fall. Read more about Noël’s experience here:

Hearing about Noël’s class, and following the ongoing work of my colleague , has made me question the viability of AI Tracks. Foolishly, I think I’ll try it anyway. But I’ll go into the semester with the benefit of others’ ideas and experiences. Here are a few things I’m planning to do to try to make the approach work:

Talk with students about the proliferation of AI.

This is probably something we should do anyway, given the rapid and ongoing developments in the AI landscape. I’m not willing to accept AI as an invisible, ever-present, and irresistible part of our lives—and I don’t think students should be either. They should be aware of what applications like Google Docs are doing when they offer to “Help me write” or what’s happening when ProQuest’s Ebook Central provides a “Research Assistant” with “insights” on the book they’re reading. They should know that these applications incorporate AI, they should know how it works, and they should know where the “assistance” is coming from.

They should know these things so that they can make their own choices about when, where, and how they allow AI into their lives. I’m not saying students should necessarily refuse these tools in all circumstances. (I, personally, refuse them, but I have my own reasons.) I’m just saying students should have the knowledge to be able to make their own decisions rather than, as our tech overlords would prefer, unknowingly or passively accepting whatever kinds of AI are foisted on them, in every possible aspect of their social and academic experience.

I will ask students to refuse this kind of AI assistance, to the extent that they’re aware of it, if they choose the AI Free track. I don’t expect this will be perfect. I suspect that some students who commit to going AI Free will give into the temptations of embedded AI from time to time. I also suspect that students will encounter AI unwittingly, and consume it uncritically, more than once over the course of the semester. Making students aware of AI’s intrusion into their lives will probably require more than a one-time, beginning-of-semester conversation.

And honestly, there’s only so much we can do. But I think we should try.

Ask students to make a commitment and stick with it.

Noël allowed her students to switch tracks throughout the semester, if they preferred, rather than being locked into a specific pathway. A couple of others with whom I shared this idea have made that suggestion as well. I totally get this. Student choice is incredibly important to me. And allowing them to make their own choices about AI, on a case-by-case basis, might better prepare them for the kinds of decisions they will face regarding AI use in other classes and situations beyond school.

However, part of the reasoning behind my desire to employ AI Tracks in the first place was to help students make more intentional decisions about AI—and to give them a pre-commitment device that could help them avoid temptation.

One thing I found in previous classes was that students didn’t really have good reasons or firm convictions behind their choice to use or refuse AI. Many students were concerned about the ways AI might negatively affect their learning and didn’t really want to use it—but they nevertheless turned to ChatGPT when they were stuck or up against a deadline. Most of the students who used AI prolifically weren’t able to articulate how it benefited their learning or affected their writing process, beyond some vague sense that it helped them write faster and use bigger words. All in all, it seemed like students were being controlled by generative AI rather than the other way around.

My reason for wanting to employ AI Tracks is, in part, to combat this lack of agency. I want to give students who are concerned about AI a pre-commitment device that holds them accountable to their AI refusal throughout the semester, even when the going gets tough. I want to give students who are interested in AI opportunities to use it intentionally and to learn from their use rather than employing it uncritically, in whatever way speeds up the process. I want to give students the satisfaction of setting a goal for their engagement with AI and sticking to it until the end of the semester.

So, I guess, paradoxically, I’m hoping that limiting students’ autonomy in switching between AI tracks actually enhances their autonomy as intentional users or refusers of AI, at least for the duration of the class. I’m not sure if this makes sense. But I hope it works.

Limit the type and extent of AI use even for students on the AI Friendly track.

I’m going to carefully prescribe ways that students on the AI Friendly track can use AI. In fact, I’m planning to make a list of specific tasks and prompts students are permitted to use on specific assignments. I know—this could very well be a fool’s errand. But hear me out.

I’m doing this because my first-year students don’t always understand the goals of the tasks I ask them to do and can’t always tell how AI might support or impede their progress toward those goals. Confusingly, goals can also change from assignment to assignment. I wouldn’t, for example, want students to use an AI reading assistant on our Rhetorical Analysis exercises, because the goal of those exercises is to improve their skills in reading comprehension, summary, and basic analysis—things a reading assistant would pretty much do for them. I wouldn’t mind, however, if they used a reading assistant to break down a complex scholarly article they wanted to cite in their Researched Argument paper. The goal of that activity is not for students to fully comprehend and analyze articles meant for experts in the field; it’s to evaluate and integrate sources to support their own arguments. While a reading assistant would impede their learning in a Rhetorical Analysis exercise, it could actually enhance their learning (by reducing extraneous cognitive load) in a Researched Argument paper.

Because of this, I’ve determined that I need to be specific about how students can or can’t use AI at different points in the course. So, that’s what I’ll try to do.

I’m aware, again, that this won’t be perfect. It’s likely that some students won’t abide by the guidelines I provide, and a set of rules alone will not persuade them to use AI in learning-supported ways. If this approach is to have any hope of success, it will have to be combined with a clear sense of the purpose and value of our assignments; a grading system that prioritizes process over product and encourages revision; a strong relationship with individual students; and probably many other pedagogical practices I haven’t thought of.

And even with all those things in place, some students will still choose to use AI in ways that shortcut their learning, often knowingly. Some may coast through the class breaking rules left and right and attempting to elude accountability. A few may even succeed. But when have students not done this? I firmly believe that most students want to learn and that we can create the conditions that help them tap into their best selves. We have a lot of control here. But we can’t make every student learn in every circumstance.

If it turns out that more students than I expect abuse the AI guidelines we have in place, I’ll reevaluate. But this didn’t happen last year, and I don’t expect it to happen this year. I know. Famous last words.

“Foreground an ethos of affiliation.”

This was one of my favorite concepts from Noël’s reflection. She writes,

This fall, I plan to foreground an ethos of affiliation, asking students to think often and deeply about how their relationships with both humans and nonhumans shape their creative work. In doing so, I align myself with scholars in writing studies like Nancy Ami, Natalie Boldt, Sara Humphreys, and Erin Kelly’s work on peer review, “No One Writes Alone,” and Marilyn M. Cooper’s ecological approach. I hope this approach will both encompass and extend beyond a GenAI literacy focus by prompting students to become aware of the relational nature of their work, whether or not they use GenAI as part of their process. I also hope to emphasize the ethical dimension of affiliation, asking students, “To whom and what do you wish to be connected? Why?”

I think this will be an important concept for my students on the AI Free and AI Friendly tracks alike. The questions of “how [students’] relationships with both humans and nonhumans shape their creative work” and “to whom and what [they] wish to be connected” are what I’m really trying to get at when I ask them to make intentional choices about AI. I suspect they won’t have thought much about this coming into the class, but I hope, by the time they leave, they can answer such questions with clarity and confidence.

So, that’s what I’m planning for the fall. It’s possible the AI Tracks idea will blow up in my face, and I will regret not sufficiently heeding the warnings of Noël’s experience and Marc’s guidance. But I hope not. I will report back when the semester’s over—as long as the outcome isn’t too embarrassing.

I’m particularly excited for next week’s post here on Unmaking the Grade. It begins a multi-part series on a topic that’s been generating a lot of interest in the alternative grading community—and it’s written with a surprise co-author. Stay tuned!

Students would commit to one of two tracks at the beginning of the semester. “In track 1,” I wrote, “AI use is strictly off limits…In track 2, AI use is integrated, with the requirement that students transparently share the details of their use and regularly reflect on it.”

Because I didn’t teach in the spring, I didn’t have a chance to try out the idea. But I heard from a couple of educators who did!

One of those educators was . Noël is a teacher-scholar specializing in feminist rhetorics, literacy studies, and digital pedagogies. She works as the Digital Teaching Programs Administrator at Boston College’s Center for Digital Innovation in Learning and is a PhD candidate in the English Department.

I reached out to ask Noël if she would be willing to share some reflections on her semester trying “AI tracks,” and she was generous enough to agree. So, without further ado, here’s Noël:

When I decided to experiment with the approach Emily Pitts Donahoe outlined in her post “Promoting Student Autonomy and Academic Integrity in AI Use: An (untested) idea” with my online, asynchronous Design Thinking and Creativity class, I wasn’t quite sure what to expect.

I was drawn to this idea because of how easily I could incorporate it into many practices I already use in the classroom. As a former K-12 educator trained in Project-Based Learning, I center “student voice and choice” in my curriculum development. I always have students set personal goals for their learning in my class at the start of each semester, detailing what they want to learn, how they will personally define success, and how they hope to grow and change as a result of our learning together. Choosing a “track” for their AI engagement, I thought, seemed like a natural extension of this preliminary intention-setting assignment. Asking students who used GenAI to reflect on their use, including evidence, also aligned nicely with reflective and metacognitive practices I’ve been using for years.

I added a question asking students to choose if they wanted to be “AI Free” or “AI Friendly” in their work for this class and provide a short explanation for why they made that choice. Those who committed to being “AI Friendly” would also commit to submitting a reflection on how they used GenAI, including evidence, for each assignment. My framing of the “tracks” borrows language from work we’ve done at Boston College’s Center for Digital Innovation in Learning (CDIL) around “zones of engagement” with GenAI. To date, our work with faculty has primarily focused on discussing GenAI at the assignment level, with any framing driven by faculty. I wanted to explore what it might look like to give students, rather than faculty, that agency.

I was curious not only to see what students would choose but also to understand their rationale for the choices they made. Clickbait dominates much of what has been published about students’ use of GenAI, with headlines like “Everyone is Cheating Their Way Through College” or “There’s a Good Chance Your Kid Uses AI to Cheat.” These perspectives don’t offer much for me as an educator. Sure, there’s going to be people who game the system, but that’s nothing new. Overwhelmingly, my experience with students at every level I’ve taught has been that they ultimately want to learn and are trying their best in an imperfect system while balancing their lives outside the classroom with being whole human beings. I eagerly awaited submissions of this first assignment, expecting a range of choices and reasons for those choices.

I was hoping to gain a deeper understanding of the diverse and nuanced ways students are currently engaging with GenAI by introducing these “tracks.” At that point, I’d had limited insight based only on what a few students felt comfortable sharing with me or in cases of fairly egregious misuse. I was thus surprised and a little disappointed that all but one student in my class decided to be “AI Free” for the course. Students cited concerns over environmental impacts, learning loss, and their motivations for signing up for the course in the first place as reasons for their decision to remain “AI Free.” Several students reflected on how they thought it would be antithetical to use GenAI in a course centered on creativity.

“Does This Count?”: Developing Metacognitive Abilities

I tried to underscore to students that they were not “stuck” on any “track.” They could (and I would welcome them to!) switch their orientation to GenAI in my class at any time. However, in practice, this resulted in an absolutely flooded inbox of emails from students that all pretty much boiled down to one question: “Does [this] count as GenAI?” I also started noticing bits of language that felt as if they were AI-generated due to mismatches and inconsistencies in writing voice across types of communication, such as what was written in an email versus what was submitted as an assignment.

When I left comments on these students’ work, noting this, I often received responses expressing confusion, paired with effusive apologies. With the exception of one student who consistently submitted work that, for example, reflected on assignments or other aspects of the class that were completely fabricated, I do believe that these students were completing their work in good faith. I don’t think they were trying to “cheat,” and having to reassure my students and clarify how I would like them to share their creative process with me revealed that I had unintentionally internalized a mindset of GenAI use that I believe is unsustainable given the pace of technological advancement and ultimately harmful to learning.

I realized that when I conceptualized these tracks, I was assuming the kind of GenAI use in which students would navigate to an external platform, such as ChatGPT, and engage in a back-and-forth dialogue before ultimately lifting or paraphrasing a portion of the generated text and incorporating it into their final work. This is the paradigm at the center of academic integrity concerns and can also be seen in citation guidelines, such as those of the MLA.

As I learned, the reality is much more slippery. I suspect it will become even more so as time goes on. GenAI tools have been quickly incorporated into many of the platforms we use on a daily basis, such as Zoom, Canva, Canvas, Grammarly, and Google, to name a few. As I draft this post, I’ve been repeatedly prompted within the Google Doc to open and engage with “Help me write.” The other week, after I updated my macOS, I noticed something new: summaries of my text threads. Like most of these tools, there were (quite comical) errors, such as the AI misunderstanding that “Darling” in my thread with one friend wasn’t an endearment but the name of her cat. The incorporation of Apple Intelligence in a higher education landscape where many students come to our classes with new MacBooks purchased under the Apple for Education discount program means that even if students aren’t navigating to ChatGPT, Claude, or another GenAI tool, their work and communications might already be shaped by the presence of these tools, perhaps without their knowledge or understanding.

My students’ confusion about the boundaries of GenAI in their work is not unique to my classroom or institutional context. A recent article in The Chronicle of Higher Education quotes students saying they aren’t sure what “counts as unauthorized assistance.” This confusion is by design. These tools are meant not only to be efficient but also to be invisible, removing as much friction as possible from one’s work. This sense of confusion, I found, was even more heightened among my so-called “nontraditional students,” who often come to school from professional careers, military service, or at a different life stage than first-time undergraduates. These students often struggle with understanding different file types or accessing Canvas. One student with whom I talked in office hours, for example, had never heard of GenAI. He thought that the “help” he was seeing in his Google Docs was something that the University provides for students because it cares about their success.

Students are obsessed with product over process, and it’s not their fault!

In addition to confusion about whether or not they were using GenAI, students expressed deep concern over whether or not they were doing the work “correctly.” This, of course, is the product of a school system that has trained students to prioritize outcomes over the process of learning. Elise McDowell, in her piece for the Guardian, points to structural stressors such as the rising cost of tuition and the high-stakes exam culture in England. While we don’t have standardized exams in the same way at the college level in the US, we do have grades, GPAs, class rankings, and other pressures to perform that lead students to believe that we care more about polish than we do about the substance of their ideas or their learning process.

Conclusions and Moving Forward

Mid-semester, I was unhappy with the level of confusion students were expressing, as well as their continued focus on product over process. I made a few changes to my course. Most notably, I reintroduced a practice I’ve long used in my classes—process reflections. Using questions inspired by Joda Shipka’s mediated activity-based multimodal framework described in Toward a Composition Made Whole, I asked students to submit a “project pitch” statement for all remaining work in the class, where they answered the following questions:

• What do you want to do?

• Why are you doing it?

• Who/what is involved in doing it?

• How and when will you do it?

• What might go wrong?

• How will you know your project met its goals?

• Why you? Why this project?

Then, when they submitted their “final” work for each project, I asked them to revisit their original project pitch and reflect on what had changed since they first outlined their ideas. Students, I’ve found, particularly struggle to answer the question of “who/what is involved in doing it,” often answering simply “me” or occasionally, “me and my roommate.”

I referred students back to my syllabus statement about GenAI, which says “we are always using technology when we write,” referencing Dennis Baron’s A Better Pencil, and then referred them to Pessimists’ Archive for a history of moral panics around technology. Slowly, their reflections became more detailed and nuanced.

Moving forward, I don’t plan to continue using the “tracks” idea in my classroom. This fall, I plan to foreground an ethos of affiliation, asking students to think often and deeply about how their relationships with both humans and nonhumans shape their creative work. In doing so, I align myself with scholars in writing studies like Nancy Ami, Natalie Boldt, Sara Humphreys, and Erin Kelly’s work on peer review, “No One Writes Alone,” and Marilyn M. Cooper’s ecological approach. I hope this approach will both encompass and extend beyond a GenAI literacy focus by prompting students to become aware of the relational nature of their work, whether or not they use GenAI as part of their process. I also hope to emphasize the ethical dimension of affiliation, asking students, “To whom and what do you wish to be connected? Why?”

Last week, I had the pleasure of participating in the sixth annual Grading Conference, a virtual gathering of (mostly) higher ed instructors who are interested in alternative grading. Almost 1,000 educators from across the country, and the world, registered to attend! This was my third year attending the conference and my second helping to organize it.

During the actual event, the organizers spend a lot of time behind the scenes making sure everything is running smoothly—meaning that we have little brain space left for actually absorbing the content of the keynotes and presentations (at least while they’re happening)! But thanks to this year’s fantastic team of volunteer Zoom helpers, I was able to get more out of the sessions than I did in 2024. Here are a few of my takeaways.

We’re seeing a lot more research on alternative grading.

Almost every time slot during the three-day gathering included a session in which scholars presented findings from their research studies. My sense (without having done an actual meta-analysis) is that this research is still mostly focused on 1) individual classroom experiments and 2) student and instructor perceptions of alternative grading. Large-scale studies and work on other aspects of these grading practices are slowly emerging. But it’s simply not true that there is “no research” on alternative grading. It’s happening! And more is happening every year.

Students are on board.

Student perceptions of alternative grading were a key theme of the conference—we even had two undergraduate panels (neither of which I was able to attend, unfortunately!). Obviously, students are not a monolith, and their views of alternative grading are probably as variable as those of individual instructors. But we’re seeing lots of indications that alternative grading is working for students. They’re reporting less stress, increased motivation, a stronger sense of classroom community, and a greater focus on learning in alternatively graded classes. I’m excited about this research, and about the fact that we’re bringing more student voices into the conversation directly.

We’re considering nuances and questioning our assumptions about alternative grading.

Some presentations also discussed negative student experiences with alternative grading: lack of clarity, confusion about grades, or increased workload. These insights are crucial to helping us improve our practices.

As a collaborative grader in the humanities, I was especially interested in one finding of Amy Ernstes’s dissertation research. Students in her qualitative study observed that some forms of alternative grading may enable greater freedom of speech, since students aren’t just trying to parrot what the instructor wants to hear in order to get a good grade. But this freedom of speech can be a double-edged sword for some marginalized students: while open dialogue is good, harmful comments are not, and in some students’ experience, the number of such comments seemed to increase in alternatively graded classes. Creating a strong sense of mutual respect and community responsibility, then, may be especially important for instructors employing alternative grading in discussion-based contexts.

Like Amy, many presenters illuminated the complexities of how alternative grading might affect certain student populations, challenging some of our assumptions along the way. For instance, one group of researchers discussed how specifications grading seems to help marginalized students earn passing grades at higher rates in chemistry lab courses but also fails to mitigate opportunity gaps between these students and their more advantaged counterparts.

Another example: Many instructors worry about whether or not alternative grading provides sufficient support for neurodivergent students (though, in my opinion both “alternative grading” and “neurodivergence” are such capacious categories that it’s difficult to make any definitive statements here). In her presentation, my colleague argued that there is at least one characteristic of alternative grading that makes it well-suited to the needs of neurodivergent students: its “tolerance for error.” Alternative grading allows both students and instructors to make, and learn from, mistakes (particularly breakdowns in communication) without incurring serious consequences for either party. While this certainly doesn’t mean that all aspects of alternative grading are good for all neurodivergent students, it does illuminate one important way in which alternative grading approaches can align with neurodiversity-informed approaches.

We’re not backing down on inclusion, equity, and justice.

I already mentioned several presentations that highlighted how alternative grading might support or fail to support historically marginalized students in specific ways. These were not the only ones—and a commitment to inclusion, equity, and justice were evident in the conference’s keynotes as well.

In her talk, my University of Mississippi colleague Eden Tanner shared the story of how she came to alternative grading (which she has also shared in her interviews on the Grading Podcast and Derek Bruff’s Intentional Teaching). It involved a realization that her underrepresented students weren’t succeeding in chemistry at the same rates as their more advantaged peers and her conviction that continuing to perpetuate this inequity was unacceptable. These commitments led Eden to re-envision her grading system to better support student learning, bringing it more closely in line with her larger goal of helping all students see themselves as scientists.

In his barn-burner of a keynote the following day, Jeff Anderson similarly connected our decisions about grades to larger cultural narratives that can either perpetuate or oppose racial and class-based oppression. Through one particularly memorable Venn diagram, he argued that traditional letter grades sit at the intersection of eugenics, behaviorism, and antidemocracy as historical forces. (Jeff explains this position more fully on the Grading Podcast, in a rare two part interview.)

So, I don’t think, despite the current political climate, that we’re done talking about how we can make our institutions more inclusive, equitable, and just spaces for our students. Maybe it’s too early to say, and maybe this is just wishful thinking—especially given recent efforts to dismantle the kinds of infrastructure we need to study educational disparities. But I have faith in this community, and its commitment to helping all students thrive.

Relatedly, my favorite insight of the conference was this:

Actually liking student work is essential to our own work as educators.

As a writing teacher, I expected that Asao B. Inoue’s keynote would resonate strongly with me. But I didn’t expect that it would do so by making such a simple point. I can’t remember a single time, in my 10+ years as an educator, that anyone has ever talked to me about “liking” student work, or students themselves, until Asao’s keynote. But I think it’s foundational. You can’t get anywhere without it.

It reminds me of a student remark on an episode of NYT’s The Daily two years ago that I can’t stop thinking about. The topic was (what else?) generative AI and cheating. The anonymous student related their experience of using AI to write a portion of an assignment that they wouldn’t otherwise have been able to complete by the deadline. They gave ChatGPT a page they had already written, asked it to finish the paper, reworded the output slightly, and turned it in. When they received their essay back, the student noticed that nearly all the professor’s corrections were for the first part of the paper, the part that they had written themself. The second part, written by ChatGPT, had almost no comments. “I got an A,” the student said. “And my professor actually liked the ChatGPT part of the paper more than mine.”

My professor actually liked the ChatGPT part of the paper more than mine.

I think our first inclination on hearing such a comment might be to say, “Whoa, whoa, whoa—it’s not about whether or not we like your work. We grade based on quality.” We want to assure students that liking has nothing to do with it. We are objective assessors of performance. We are unswayed by any human feelings of fondness or appreciation!

Set aside, for a moment, the fact that separating our feelings about student work from our assessment of it is probably impossible, or at least more complicated than we might assume. Let’s consider, instead, whether or not such a thing is actually desirable. Do we really want to move through student work attempting to tamp down or separate out our human reactions to it, so that we can “judge” it “objectively”? Do we have to? Or, put yourself on the other side of the exchange: when we ask others to read our writing, in all our vulnerability, don’t we want them to like it? Don’t we want to feel, even in reading their critical feedback, that they were moved or intrigued or delighted by what we created? Or at least that they weren’t indifferent to it? Isn’t that what students want, too?

I’m heartbroken by the idea that any student would think that a teacher “likes” the output of a large language model better than something they themselves wrote. I’m even more heartbroken that their belief is, in many cases, justified. Why does it seem that many of us dislike student work (and sometimes students themselves)? What is in the way of liking in our classrooms? What kind of transformative effects might more liking bring about?

I think these questions are inextricably bound up with questions about the way we grade and assess students. And I’m glad Asao’s keynote provided an opportunity to think about them.

I wasn’t able to attend every conference session, so I’m sure I’ve missed some important themes. If you attended the 2025 Grading Conference, please share your own takeaways below! And if not, I hope you’ll consider checking out the great work being done at the conference and the Center for Grading Reform more broadly.

Recordings of most 2025 conference sessions should be available in the next few weeks. Until then, check out the archive of past materials on the Grading Conference website.

I’ve been on vacation for the last 10 days, with little time or inclination to write! So, in lieu of a full-length post, I’m sharing three things that might be of interest: information about the Grading Conference; my second appearance on the Intentional Teaching podcast “take it or leave it” series; and a recent Inside Higher Ed piece in which I’m quoted on the subject of AI and academic integrity.

The Grading Conference—and podcast

If you have not yet registered for the Grading Conference, there’s still (a little) time! This virtual event takes place Wednesday through Friday, June 11-13. It’s a great place to get practical inspiration for new grading systems, and the presentation lineup looks fantastic.

If you’re curious about the conference or want a behind-the-scenes look at our planning, check out this episode of The Grading Podcast. On the podcast, hosts Sharona Krinsky and Robert Bosley are joined by four other members of the conference organizing team (myself included) for a conversation about what’s new this year and what we’re most looking forward to at the conference.

Intentional Teaching: Take it or leave it, take 2

Last year, I had the honor of joining my colleague Derek Bruff for a very special episode of his Intentional Teaching podcast. It featured a “take it or leave it” panel, in which three guests discussed recent hot takes in higher ed and made judgements about whether they would “take” or “leave” those takes.

It turns out that in addition to being an absolute blast for the guests of the podcast, the format was also a hit with Derek’s listeners. So, since last summer, he’s made a couple more “take it or leave it” episodes—including one that came out just last week.

The first time I was on the podcast, I was joined by Stacey Johnson and Lance Eaton. This time, I had the pleasure of talking with Betsy Barre and Bryan Dewsbury, and there are other episodes featuring my colleague Liz Norell. The full “take it or leave it” line-up is below, with one additional episode coming soon. Check out these conversations—they’re a lot of fun!

• June 2024, Take It or Leave It with Stacey Johnson, Emily Donahoe, and Lance Eaton

• March 2025, Take It or Leave It with Liz Norell, Betsy Barre, and Bryan Dewsbury

• May 2025, Take It or Leave It with Betsy Barre, Bryan Dewsbury, and Emily Donahoe

Inside Higher Ed: AI and Threats to Academic Integrity

I was recently interviewed for a great Inside Higher Ed piece by Colleen Flaherty about AI and the academic integrity problem. Regular readers of this Substack won’t be surprised by anything I have to say, because I’ve already said most of it in a recent post here and a piece I published two years ago with my students (which is referenced in the article). But lots of other folks are quoted in the piece, and it’s full of good recommendations for navigating this difficult problem. Give it a read!

I hope you, like me, are taking a little time off to enjoy the summer. I’m returning from vacation shortly and will be back on the blog in two weeks with some reflections on my time at the Grading Conference. Stay tuned!

In my last post, I reflected on the larger problems we’re facing around AI misuse in college classrooms right now. As I noted, I think the issue is structural but that there are many things instructors can do in their individual classrooms to motivate student learning and, when needed, to build in accountability measures that ensure students are doing their own work.

One accountability measure I mentioned, and that others have written about, is process tracking. Basically, process tracking involves monitoring the process by which students craft their assignments. One way to do this, for example, might be to dive into the version history of a Google document, viewing the timestamps of the essay, how long it took to put together, the order in which the words were written, the erasures and false starts, what words were copied and pasted from other sources, etc. A somewhat newer tool for process tracking is Grammarly Authorship, which actually records a student’s screen as they write and generates a report on their “originality.”

We talked about this tool, and others, at my university’s AI Institute this week. It got me thinking a lot about the potential advantages and disadvantages of process tracking.

On the one hand, as points out, this kind of accountability measure is better than AI detection, which is notoriously unreliable and easy for students to circumvent. I also like it better than using in-person proctors or digital proctoring software, since those involve not only surveillance but also, typically, time pressure. It’s difficult for students to devote sufficient cognitive energy to their work if they’re hearing a ticking clock in their heads and feeling eyes on their back the whole time.

Beyond being a way to determine whether or not students are doing their own work, I also see the appeal in process tracking for supporting learning. A lot of what I’m trying to teach first-year students is how to engage in the writing process effectively. They can’t learn about their own process without being aware of and reflecting on it, something that process tracking could help facilitate. Theoretically, if I, as the instructor, also have an inside view of their writing process, I could help them do a better job of examining and enhancing it.

On the other hand, as my colleague points out, being continuously surveilled might have some negative downstream effects for developing writers. At best, it’s an invasion of privacy. Of his own experience, Marc writes,

If an editor saw my writing process they’d think I was mad. If I was a student and a writing instructor saw how I write, with time stamps, keystroke entries, what was copied, what was deleted, and yes, what was made by AI, what would that person think of me? I feel entirely naked just describing how I write—I would be mortified if someone could see that entire process.

In general, I’m anti-surveillance, so when I read Marc’s piece, I was inclined to agree, at least on an intellectual level. It wasn’t until I began writing this post that I started to understand the sentiment on a deeper, emotional level.

My new misgivings are the result of an experiment I’m trying this week. I’m screen-recording my writing process for this post and publishing a video of it along with the text. Here’s what I ended up with:

I honestly didn’t think that making my writing process visible like this would bother me. Unlike Marc, I don’t feel particularly uneasy about sharing this kind of thing. But all that changed when I actually started writing. Here are a few observations about what it’s like to draft under surveillance:

One potentially good thing about recording my process is that it’s keeping me focused. I don’t want to check my email or answer a text or doomscroll on Bluesky or start doing another task because either 1) people would watch me do it, which would be embarrassing, or 2) I’d have to go through the trouble of stopping and restarting the recording several times, which turns out to be pretty inconvenient. I also don’t want to stare into space too long, lest those watching the recording think I’m…I don’t know—too slow or something. How, exactly, this enhanced focus is affecting my writing process, or the text I ultimately produce, remains to be seen.

I’m also encountering a general fear of judgement about what and how I write. What if people think my process is weird or wrong? What if I type something that I change my mind about later and, in retrospect, don’t want people to see? I mean, that’s why we delete things, right? Because we don’t want people to read them? What if I vacillate between ending the previous sentence with a question mark and an exclamation point several times and all this waffling is out there in public for people to see?

Troublingly, screen recording with the expectation of an audience for my process is changing the way that I write. I’m less inclined to navigate away from this page to explore other sources and ideas or pause too long to think about what I’m typing. I’m writing a bit faster than I normally do. I’ll probably end up writing this in 4-5 longer, focused sessions rather than in the 10-12 micro sessions I usually take to draft a post. It’s possible (though this is only on the edge of my consciousness right now) that I’m taking fewer risks as I’m writing. I certainly feel more vulnerable, which probably makes it less likely that I will type anything that’s really out there, whether or not it ends up in the final cut. I’m having difficulty focusing on what I’m writing because half of my attention is focused on the performance of writing.

Worst of all, after taking writing breaks, I kind of dread coming back to this draft and am anxious to be done with it. This never happens to me under ordinary circumstances, at least not for blog posts.

I genuinely didn’t expect to have these kinds of feelings about recording my writing process. If all this fear of judgement is popping up for me, an experienced writer, imagine how a developing writer must feel. If I’m anxious about what people will think of my writing process when there are no stakes attached to their opinions, imagine what it must be like for a student who is being graded on their work. If a person like me, with an almost lifelong affinity for writing, is dreading the activity when it’s being recorded, imagine how much more students will dread it.

All this reminds me of something my colleague said about AI process tracking on LinkedIn recently:

I do think teaching strategies related to AI use that ask students to make their writing process legible to instructors (as a form of transparency and even metacognition) would have posed a barrier to me (as a neurodivergent student). I am very very reluctant to describe my process of writing or doing pretty much any kind of work to someone else if they are going to be evaluating the process in addition to the product. This is because I have had so many experiences of being told that my process is wrong, and failing when I tried to implement someone else’s “correct” process. I would write a totally different essay if I knew I was going to have to share my process, and it would be much harder for me.

As someone with a pretty neurotypical brain, this wouldn’t have occurred to me. But even as a neurotypical person, I’m experiencing something like what Sarah describes in that I’m writing differently because I know the process will be shared, and as a result, I’m having a slightly more difficult time of it. Moreover, now that I’m thinking about it more deeply, I do remember a few instances of students being uneasy about sharing their writing processes when the subject has come up in my class.

Last fall, for instance, I got this stellar essay from a first-year student in the first two weeks of class. It was supposed to be a low-stakes, informal, diagnostic assignment, and it was clear the student had gone above and beyond the call of duty. I got kind of curious about how much time they had spent on the essay, so I opened up the version history of the document to look. I saw that they had been working on it for many more hours than necessary and had even stayed up until 4:00 am to submit the draft on the day it was due.

This was pretty concerning to me: I don’t want students to sacrifice sleep to complete essays for my class (I’ll happily give them another few days), and I certainly don’t want them to stay up until sunrise putting in hours of work for an informal diagnostic essay! So, in my comments, I noted how important it was to prioritize tasks and develop healthy working habits in college—and that if they ever found themself putting assignments for my class before sleep, they should come and talk to me.

I didn’t think much more about it until we had a conversation later in the semester and they mentioned how embarrassed they were that I had seen those timestamps. The fact that I had seen them led to some good conversations about their writing habits and struggles with perfectionism. But I hadn’t realized just how sensitive this kind of information could be for students. It did feel like I had invaded the student’s privacy by locating, and mentioning, information about when they were writing their essays.

I suspect that many students are—like Marc, Sarah, the student above, and now myself—uncomfortable with making their writing processes visible. But some are also turning to self-surveillance as a way of protecting themselves from allegations of AI misuse. A piece published last week in the New York Times tells the story of Leigh Burrell, a student at the University of Houston-Downtown who was falsely accused of using AI to write one of her assignments. She was able to prove her innocence, but only after a lot of effort (not to mention anxiety). After that incident, Leigh began recording her screen as she drafted essays, saving those recordings in case she was falsely accused of AI misuse in other classes.

Another student, a high school senior named Sydney Gill, noted that she had “second-guessed her writing ever since an essay she entered in a writing competition in late 2023 was wrongly marked as A.I.-generated.” The experience had a lasting effect on Sydney: “I don’t want to say it’s life-changing, but it definitely altered how I approach all of my writing in the future,” she said.

I’d love to hear more from these students about how the current landscape is changing their writing processes—and just as importantly, their relationships with school. I can imagine that it’s hard to learn when you’re spending a lot of your cognitive energy worrying about whether or not someone will accuse you of dishonesty. At the very least, you’re probably more focused on making your work appear simultaneously human-crafted and worthy of a high grade than you are on actually learning something.

So, what are we, as instructors, supposed to do? Despite its flaws, process tracking seems like one of the most learning-focused forms of accountability we have. Admittedly, I have gotten some good mileage out of checking up on document version histories. When something feels off about a sentence or paragraph in a student essay and the version history indicates that it has been copied and pasted, I ask students about it: “Where did this come from? Do you know what it means? Why did you put it in? What made you think this idea was better than yours? What made you think it was better expressed than something you could have written?” These are almost always extremely productive conversations, and students and I both walk away from them having learned a lot.

So, being able to track and discuss students’ processes can be really good! But as I noted above, it also feels invasive in many cases. And it may encourage students to focus on curating a writing process for the eyes of their instructor rather than exploring and creating a writing process for themselves.

I think the first thing I would recommend to fellow instructors is this: if you’re considering using Grammarly Authorship or other forms of screen recording in your classes, try recording yourself for a while first, with an actual or imagined audience in mind. See how it feels to you and what kind of issues you encounter. Maybe even write a reflective blog post on your experience. It might provide you with a lot of insight. I will say that before writing this post, I considered asking students to screen record their process for writing their major essays—not necessarily for my use as the instructor but so they could revisit and examine their own writing habits. I am now reconsidering this idea.

I do think, however, if we want to monitor students’ writing processes, we should move away from the idea of “tracking” and focus instead on “reflection.” I don’t need to track students’ keystrokes or comb through videos and version histories to make sure they aren’t cheating. But I, and students, do need a mechanism to examine their writing processes, both to help them develop some self-knowledge as writers and to ensure that they’re doing their own thinking rather than over-relying on AI. If a student doesn’t have thoughts on their process and isn’t able to describe it to me, we have a problem—not because they’re cheating but because they aren’t learning.

Here’s what I’m thinking of doing for my fall course:

We’ll talk in detail about the writing processes of people who write a lot, myself included. I’ll probably share a sped-up screen recording of my own process (maybe the one used to write this post) and others, if I can find them. I may give students the option to record their own process if I can figure out how to make that work—but I don’t think I’ll require it.

I’ll continue asking students to draft their work in Google docs so that they and I have access to their version histories. But I will 1) be more transparent about the fact that I can see their version histories, 2) explain how they and I might use those tools as sources of reflection, 3) assure them that I’ll only dive into the version history if I’m seeing something in their essay that concerns me. It will always be a springboard for conversation about the writing process rather than a way to catch cheaters.

I’m also cooking up an assignment that asks students to look back at the version history of a previous essay and write a brief reflection on what it tells them about their writing process and how that process has developed in recent weeks. I’m hoping this shows them that I’m less concerned about tracking their work and more concerned about encouraging reflection on it.

I’m already thinking, though, about what will happen if students’ writing processes don’t lend themselves well to this scheme. What if they like to draft essays by writing them out by hand first or just by making extensive hand-written notes? What if they, like Leon Furze, prefer to dictate their essays, have them transcribed by AI, and then copy and paste them into a document for editing? What if they start by doing a lot of free-writing that is intended for their eyes only?

In all honesty: I’m ending this post less certain than I was before about how to balance the need for metacognition, the desire for accountability, and the concern about privacy in all of this.

What are your thoughts about process tracking? What are your plans for using it in your next class? Please share your own approaches in the comments.

Happy end-of-semester, for those of you who have reached that point. I know it’s rough out there, so it may be hard to celebrate. But I hope the next few months bring you some small joys before things start to ramp up again in August.

In the coming weeks, I’ll be working on a few summer projects. The biggest one is preparing for The Grading Conference, an event for which I serve on the organizing committee. This annual virtual gathering for alternative graders has historically been STEM-focused, but last year it opened its doors to all disciplines. It’s a great (and affordable) place to get practical inspiration for new grading systems, and I hope you’ll register to join us. The conference takes place online June 11-13.

My more immediate project is helping to co-facilitate an AI Institute for instructors at my own university in late May. It’s directed by my colleague (whose Substack Rhetorica is the best place to keep up with new developments in generative AI and how they affect education). Day 1 of the Institute, the day I’m most involved with, is focused on “Practical Strategies for Curbing AI Misuse.” For this and other reasons, I’ve been reading a lot of recent pieces about AI in the classroom…

AI misuse—yes, it’s a structural problem

This week, we (and by “we,” I mean everyone on Bluesky) are all talking about a piece by James D. Walsh for NY Mag’s Intelligencer: Everyone is Cheating Their Way Through College. It paints a bleak picture, and I don’t really recommend it if you’re already on the brink of panic or despair after a rough semester.

My unscientific impression is that Walsh overexaggerates the number of students who are intentionally or unintentionally cheating themselves out of an education. Note the title’s suggestion that “everyone” is doing this, a clickbait-y claim that the article itself does little to contradict. In my own experience, more students than we think are genuinely trying and genuinely want to learn.

But of course, the longer these students see their peers skating through college getting As and Bs for work they didn’t do, the harder it will be for them to resist temptation—especially if maintaining their integrity results in lower grades, forces them to sacrifice their mental health, or limits their ability to live their lives outside of school.

What I like about the piece, though, is that it pretty clearly points us toward the real problem, which is not generative AI per se. Here’s my favorite passage:

The ideal of college as a place of intellectual growth, where students engage with deep, profound ideas, was gone long before ChatGPT. The combination of high costs and a winner-takes-all economy had already made it feel transactional, a means to an end…In a way, the speed and ease with which AI proved itself able to do college-level work simply exposed the rot at the core. “How can we expect them to grasp what education means when we, as educators, haven’t begun to undo the years of cognitive and spiritual damage inflicted by a society that treats schooling as a means to a high-paying job, maybe some social status, but nothing more?” [Troy] Jollimore wrote in a recent essay. “Or, worse, to see it as bearing no value at all, as if it were a kind of confidence trick, an elaborate sham?”

I’m getting tired of saying it, but: there is no way out of this that does not involve students understanding the value of the work we ask them to do and actually wanting to do it. Unfortunately, as the article suggests, the biggest obstacle to this goal is structural. It’s the fact that students have been conditioned to see education as a transaction, a series of tokens to be exchanged for a credential, which can then be exchanged for a high-paying job—in an economy where such jobs are harder and harder to come by.

What’s to be done? Structural problems require structural solutions. Many of us know by now that the only real and sustainable way to solve this problem is a wholesale restructuring of society, and of higher education. Smaller classes, in which students and teachers can form deeper connections and have real conversations about the purpose of an education. More training, time, and support for teachers. No grades, or at least a fundamental shift in how they’re thought of and awarded. Changes to how college is marketed to students and to the messages they receive about it at orientation. More public funding for higher ed, to enable all of this to happen!

The fact that none of this has even been floated by anyone with the power to enact it suggests that actual learning (as opposed to degree completion, I guess) is very low on our list of priorities. In the predictable absence of strong leadership here, what can we do—besides, of course, advocating for structural change?

Some strategies that do not involve the wholesale restructuring of higher education

I think the best place to start is attempting to “undo the years of cognitive and spiritual damage inflicted by a society that treats schooling as a means to a high-paying job,” as Jollimore suggests. “Oh, is that all?” I can hear you saying.

I’m not sure whether I ought to be ashamed or proud of this, but undoing damage around schooling is basically the one true goal of my first-year writing class, the thing I spend the most cognitive energy on. The writing is kind of incidental. I used to feel guilty about that, but I don’t anymore. Students can’t learn anything, including how to write, until they adopt new mindsets about their education.

The second, and related, thing I focus on is helping them find purpose and satisfaction in the work we do. Again: they have to understand why learning has value. They don’t have to enjoy it all the time, but they fundamentally have to want to do it. That means, of course, I have to put a lot of effort into relationships, motivation, engagement, etc.

What does all that look like?

• Removing grades, to the extent possible

• Evaluating students, when needed, based on process and growth, not just products and performance

• Having frequent, explicit, and honest dialogue with students about their experiences in school and suggesting new ways of thinking about it

• Having frequent, explicit, and honest dialogue about AI, what it’s good for, what it’s not good for, how it could support their development, how it could impede their development

• Having frequent, explicit, and honest dialogue about the reasons people learn to write and what they get out of it

• Overexplaining the purpose of every task and how engaging in it benefits students now, in their everyday lives, rather than in some indeterminate future

• Asking students to devise projects that have audiences and purposes beyond the classroom, things they might actually want to disseminate

• Making as many personal connections with and among students as possible

• Promoting trust wherever possible to ensure that students will come to me with any issues they’re having before turning to AI

• Providing as much student choice and autonomy as possible

• Engaging in democratic decision-making processes as a class, particularly around AI use policies and consequences for violating those policies

• Requiring metacognitive reflection on writing but also on students’ development as learners and as human beings

This genuinely works in my classes, for the most part. I don’t think people believe me when I say it, but it’s true.

I’m not naive enough to think, however, that such strategies are enough in every case. For one thing, it’s more difficult to implement them effectively in large classes or when instructors are low on time. And even when students are entirely bought in, they can still find it difficult to resist the temptations of generative AI during moments of overwhelm, underconfidence, or desperation. Moreover, I think as AI use becomes more widespread, relying solely on these strategies may become less effective.

Some strategies to augment the backbreaking work of creating student buy-in

So, what safeguards or accountability measures can we put in place to ensure that students aren’t using AI? Or maybe I should say, what can we put in place without guilt? I could easily resort to assessing students exclusively through blue books, in-class essays, and oral exams. I could also use anti-cheating technologies and plagiarism or AI detectors.

The problem is that I don’t want to use these things. They feel artificial—when was the last time you did any timed writing outside of school? They prioritize skills I don’t particularly care about, like the ability to perform under pressure. They often exacerbate inequities. They feel like surveillance and punishment, not like learning. I don’t necessarily blame instructors who employ them, and I can see some situations in which in-class assessments, specifically, can be useful. But none of this feels aligned with my personal values as an educator or my goals for student learning.

Here’s what I have done—or am considering—instead:

Committing to AI-free or AI-integrated experiences

This is something I hatched last November and am planning to implement, in some way, for my fall class. The basic idea is that students will choose, at the beginning of the semester, whether they want to commit to an AI-free experience or an AI-integrated experience. In the former case, students will work with me to develop potential consequences for breaking their commitment. In the latter case, students will complete assignments using AI in targeted ways, submit their AI chatlogs along with their assignments, and compose brief reflections on the experience of writing with AI.

An occasional and revisable in-class assessment

Last year, I asked students to do regular rhetorical analysis exercises in the first half of the semester, something I’ve written about before. These were all done out of class. My idea for next semester is that students will do regular practice out of class (and get feedback on their practice attempts). But this practice will lead up to an in-class assessment in which they’re presented with a short article they haven’t yet read and have 75 minutes to complete a rhetorical analysis worksheet on it. Importantly, they’ll be able to revise their work, after more feedback from me, for inclusion in a final portfolio.

More in-class writing time

Abandoning long-form writing and research would be abdicating my responsibilities as a writing teacher. So, students will still write an argument paper, intended to be crafted mostly outside of class. But I’ll also provide lots of class time to write and to ask questions when they get stuck. I think if they get started on the work in class, they’re less likely to turn to ChatGPT outside of class.

Process reflection (not tracking)

There’s been some recent debate in my social media circles about process tracking: i.e., monitoring the process by which students write their essays to ensure that there is a process rather than just a copy-and-paste. You can do this kind of thing with certain Grammarly tools, I believe, or simply by consulting the “Version History” of a Google doc. I don’t love the idea of surveilling the process students use to write—it feels like an invasion of privacy. I do love the idea of asking students to access their own version histories and use those histories to reflect on and narrate their process to me. Is this fully AI-proof? No. Can I discover more or less instantly if students are being dishonest about their process? Very likely.

Body doubling

This is kind of a unique one. I first learned about body doubling, a productivity strategy for people with ADHD, from my colleague Liz Norell. The idea is that if you’re struggling to focus on a task, having another person in the room alongside you completing a similar task can help you minimize distractions and get work done. I think a lot of students turn to AI the night before an assignment is due because they’ve procrastinated, they can’t focus, and they’re in a bind. Offering opportunities to practice body doubling—say, by inviting students to come work alongside me during office hours or pairing them up for out-of-class work sessions—might serve as a good accountability measure. And it might be a good solution for students who don’t think they can resist the allure of AI when left to their own devices.

The thing is: I don’t think any of these safeguards work without all the other buy-in and relationship stuff. If we jump straight to “securing assessments,” that immediately signals our distrust of students and puts us in an adversarial relationship right off the bat. As soon as students perceive that we are their enemies, we’re back in the academic integrity arms race: we devise ever-more complex mechanisms to catch student cheating and they devise ever-more ingenious workarounds, over and over.

We have got to break this cycle. For our sakes, for our students’ sakes, for the future of higher education, and the future of society at large.

Seeing the big picture

So now that I’ve written all this, I think I’ve figured out what’s been bugging me about our AI discourse right now. It’s that everybody is focused on one category of solutions to a complex problem. Some people are insisting that students are fundamentally disinterested and dishonest, and instituting an anti-cheating surveillance state in our classrooms (or students’ dorms, I guess) is the only way forward. Some people are arguing that if we only got better at motivating students and designing AI-proof assignments, the problem would be solved. Some people are suggesting that this is a structural problem that cannot be addressed in any meaningful way by individual teachers.

Every picture presented here, however, is incomplete. Here’s what I think:

We should lean heavily into authenticity, relationships, and student motivation as a first step to addressing the problem in our individual classrooms. We should explore additional accountability measures, if they’re needed, with students—keeping in mind that they have a right to privacy and should be given the opportunity to show what they know in ways that work for them. Overarching all of this, we should engage students and colleagues in conversation about the bigger issues and advocate hard for structural change—because the suggestions above may alleviate the symptom of AI misuse, but they won’t cure the underlying disease.

We need a wholesale transformation of higher ed. We need to improve and adapt our teaching methods now that AI is in the world. We need (equitable, non-punitive, anti-surveillance) safeguards and accountability measures for when student motivation is simply not enough. All these things can be true. And they all need to happen in tandem if we’re going to survive this catastrophic era in higher ed.

“The challenge is to convince students that there is a genuine benefit in the struggle of learning as something distinct from the steady forced march of schooling. How do I convey the genuine value of thinking when the cultural message of the moment is the opposite?”

If higher education is to have any meaningful future at all, we have to find real answers to this question.

The way I see it, alternative grading is designed to make space for the struggle John talks about and can at least help students begin to see the “genuine value” of what we do beyond a grade. When the primary goal of completing an assignment is no longer simply getting an A, we can start to explore other possible motivations for engaging. When students are rewarded for productive failure, rather than punished for imperfection, they can experience struggle as a necessary, even positive, part of learning. When we minimize grades, we can start to uncover and invest in the true purpose of an education.

Unfortunately, I think minimizing grades may be a necessary but insufficient condition for any true reimagination of purpose. Alternative grading can open the door to a new way of thinking about school, but unless we convince students that what we do in school matters, to them and to others, they’re still essentially engaged in a box-checking exercise.

I don’t think, in general, higher ed does a great job of explaining why what we do matters. This is currently causing big problems on a structural level, as the public, by and large, doesn’t understand the devastating social effects that may result from massive cuts to university research funding.

But it also causes big problems in individual classrooms. Sit in on almost any general education course at 8:00 a.m. on a Friday in late April and you’ll see what I mean. Students, if they show up at all, don’t want to be there. They don’t get why they have to be there. They don’t get why the homework is so hard—and so very, very boring. They don’t get why they shouldn’t just ask ChatGPT to do all their assignments because, honestly, who cares, this isn’t related to their major, it won’t help them get a job, they won’t remember any of it in six months anyway.

So, for a long time, I’ve been lamenting that we don’t talk enough with students about the value of work in our disciplines. We should devote more time to exploring how this knowledge operates in the real world! We should explicitly communicate its benefits not only for students’ future professional lives but also for their personal lives, and for the world at large! We should give them a self-transcendent purpose for learning! We should show them that what they learn has real, tangible meaning beyond the classroom!

I came to these conclusions partially in reflecting on my own education. I still find it stunning that I did three degrees in the discipline of English literature without anyone ever bothering to engage me in a discussion about why literature is important or why we study it the way we do or why we study it at all. Answers to these questions were sometimes implicit, I suppose, in my coursework, but mostly I puzzled them out on my own.

So, obviously, I think we should be doing more of this work in our courses. But it’s not just that we should be doing it more. We should also be doing it better.

Here’s what I mean. When I ask people to share why they think learning in their discipline matters, they consistently say things I find beautiful. They believe that their classes can help to form curious and interesting people who ask intelligent questions about the world around them. That their assignments can help develop self-aware critical thinkers, engaged problem-solvers, and well-informed citizens. That by learning about history, biology, anthropology, poetry, engineering, etc., students can become thriving individuals who make the world a better place. I’m almost always moved when I listen to teachers express the dreams they have for their students.

And then I put myself in the place of your average eighteen year old sitting in a first-year intro course. Is this a person who is interested in being interesting? Do they want to develop a curious disposition? Is this someone who is excited by the prospect of self-discovery and critical awareness (do they even really understand what those things entail)? Do they want to solve the mysteries of the universe? Will they buy the argument that Geology 101 is going to support their individual thriving and their efforts to make the world a better place? In some cases, yes! In many cases, probably not.

Maybe the problem isn’t that we’ve failed to talk to students about the purpose of learning in our courses but that we’ve failed to talk about it in ways that actually make sense to them. So, what makes sense to students?

Here’s something that doesn’t: selling our courses by insisting that the skills and knowledge students build will make them more employable. We’ve been trying this for years, and it hasn’t worked. All that message does, as John has observed, is create a system that runs on “indefinite future reward,” the idea that students have to be miserable now in order to get what they really want later. And it assumes that what they “really want” is a job—preferably a high-paying one. Hardly inspiring.

Focusing on professional benefits to students can also cause us to make some, frankly, bullshit arguments about the nature of our courses. My writing class isn’t really meant to help them become “better communicators in the workplace.” If that was my goal, I would just teach them how to write emails rather than wasting all this time on essays. Students perceive, rightly, that what we’re doing in most classes is only indirectly related to what they might be asked to do in a job. (As it should be! Education is not merely job training!)

Additionally, when I say we need to get better at talking in ways that make sense to students, I’m not saying that all our courses have to be connected to whatever the kids are into these days. While I support courses on Taylor Swift or Game of Thrones (I know, my references are already dated), I don’t think every class has to have pop culture appeal or to engage with the media students are consuming. For lots of instructors, this would be completely inauthentic—and students can smell inauthenticity a mile away. Unless it’s a real connection that’s of real interest to the instructor, students simply will not buy it.

So, what kind of pitch about the purpose of our courses will work for students? What kinds of things might actually convince them to engage in the always difficult, often frustrating, occasionally tedious, but ultimately rewarding work of learning?

Well, that’s what I’m trying to figure out.

A week or so ago, as I was mulling this over, I came across a piece of student-facing writing that struck me as a potential exemplar for those of us thinking through these questions. It was written by high school teacher and learning designer and published on his Substack Becoming Literary:

Becoming Literary

Teaching Genre Theory

Hello, literarians! This week, I wanted to share a draft of a student-facing article I wrote for a curriculum project I’m consulting on. It’s an article 9th or 10th graders will read at the start of a mini-unit on genre conventions. I’d love some feedback…

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a year ago · 6 likes · Trevor Aleo

The piece is a kind of introduction to genre, through the lens of design analysis, for 9th and 10th grade students. It doesn’t start off with an explicit pitch for why students should care about genre. But it does manage to convey complex ideas about the topic (Bakhtin is referenced!) in ways that I think suggest the significance of understanding it.

I’ve been trying to articulate what I think might be compelling about this piece for students. Part of it is immediacy: it starts with two quick examples of genre conventions in horror and action movies, instantly connecting the larger topic to something that is familiar to students—authentically, without pandering. The piece is also super specific and concrete. Every time Aleo introduces an abstract concept or bit of theory it’s followed closely by an illustrative real-world example.

Interestingly, I think the piece is compelling in large part because it seems to be aiming not for a big student epiphany but for a series of smaller realizations—things that make you go “Huh!” It’s not saying, “Join me on this journey to become a better critical thinker and self-actualized individual!” It’s saying, “Did you ever think about how the music you listen to every day reveals something about how fans like you see the world? That’s pretty cool.”

Further, it invites students to carry the awareness they’re developing into their everyday interactions:

“Next time you see a TikTok video, read a college essay, or watch a horror movie, look beyond its obvious features to what it’s actually doing socially. How does its content reflect community values? How does its structure serve its purpose? How does it position you as a reader or viewer?”

And at last, in the final paragraph, it turns to an explanation of why all this matters:

“Understanding genres through design analysis gives you tools to become not just a better communicator, but also a more critical participant in the many social contexts where genres operate.”

Aleo’s pitch about the purpose of learning here looks like many of the ones above. But it comes at the end of the lesson, not the beginning, at a point when students are better prepared to understand it. They have a sense of what being a “critical participant” in the world might mean. They’ve seen examples of what it can look like. They understand why it matters.

I recognize that this pitch probably won’t appeal to every student. One of the hardest parts of this is that students are not a monolith, and what one student will find compelling will not compel another. Worse, there are some students—not most, not many, but some—who will remain unconvinced by any pitch we make. We cannot, on our own, dismantle an entire structure that constantly tells students that learning is not important, or that it’s only important insofar as it generates a profit for you or somebody else.

Still, with consistent effort, I think we can move the needle for many students. They can start to see how doing this work could be extremely fulfilling, how it might help them help others. And once that seed is planted, who knows what good things will grow?

What are your favorite ways to pitch the value of learning to students in your discipline? What exemplars have you found? Please share your insights in the comments.

Instead, I’ve mostly been thinking and talking with colleagues about how to respond to the DEI legislation that is immediately forthcoming in the state of Mississippi, as well as other troubling developments—like the fact that Mississippi libraries have been ordered to delete academic research on race and gender from their databases.

If you, like me, are concerned about government censorship in your classroom, funding cuts to lifesaving research, international students and scholars being detained unjustly and without warning by ICE, or other related threats, I encourage you to check out the resources created by AAUP’s Center for the Defense of Academic Freedom—including their Substack newsletter, Academic Freedom on the Line:

Academic Freedom on the Line

This newsletter examines academic freedom, its role in democratic society, and what is lost when academic institutions face politicized attacks on institutional autonomy and shared governance. Views expressed are those of the authors, and not the AAUP.

By Isaac Kamola

And if you, like me, have no time to think about grading right now but would very much like to do so in the near future, I encourage you to register for this year’s Grading Conference, the flagship event of the Center for Grading Reform. The virtual conference takes place each June and is a great place to learn from colleagues all over the country (and the world) about how they’re practicing alternative grading in their classrooms.

Thinking about grading and thinking about how to resist attacks on higher education may seem like two different things. But I think they are intimately connected. Ultimately, advocates for alternative grading and advocates for higher ed in general share at least one goal: a world in which every student and teacher, no matter what their background or identity, has an equitable chance to pursue the kinds of educational questions that interest them; to access the support and resources they need to thrive professionally and personally; to feel welcomed at our institutions; and to live lives filled with deep meaning, purpose, and learning rather than with transactions, treadmills, and precarity.

Here is a true story:

After writing the above, I was planning to end the post with a quotation about education and fascism that I saw earlier this week on social media. It was shared by Nick Covington, co-founder and director of the Human Restoration Project, and it was taken from John Dewey and Goodwin Watson’s “The Forward View: A Free Teacher in a Free Society,” published in 1937.

Like a good scholar, however, I never share quotes without first verifying them myself. Unable to locate this particular passage in an online text, I trudged off to the library to consult the collected works of John Dewey. After finding the right volume, I was able to identify the quotation on page 537:

“The years immediately ahead will be characterized by struggle...It will require struggle to secure the necessary freedom to think about the meaning of these facts. The most bitter struggle will come when teachers begin to act in the light of these essential facts and meanings...If there be any teachers who chose their profession because they imagined that in it they might stand securely aside from the turmoil of battle for power, they will probably find the next decade or several decades very dismaying.

A free education is incompatible with fascism. Education is likely to be one of the great battlegrounds upon which is waged an intense and desperate struggle for power.”

It’s good, right? I thought it would strike just the right tone to end this post.

Satisfied with the veracity of the quotation, I was about to close the book and move on when I ran my eyes over the next paragraph, just to make sure I wasn’t missing anything. I was immediately brought up short by the following sentences:

“It may give both guidance and courage to teachers who must participate in the imminent struggle, if we turn in imagination to the period after the smoke of battle has lifted. Let us suppose that the forces of education, cooperative labor, and the common people have been successful not only in preserving but also in extending democracy. What kind of society can we anticipate and what kind of teacher will be needed?”

I was intrigued enough to read on. In fact, I abandoned my to-do list for the next half-hour, sat right down on the floor of the library, on the worn carpet in the middle of the stacks, and read the whole chapter.

It struck me, as I read, how infrequently I do this—walk to the library, find a book, sit, and read it. It struck me that few, if any, of my students ever do this, or have ever done it, or will ever do it. It struck me that an AI reading assistant could have located my quotation for me in less than a heartbeat and delivered it up to me on a little digital platter, perfectly punctuated and completely devoid of any meaningful context.

Gazing up at the many multicolored volumes of Dewey’s work on the shelves, I thought about how crypto poster-boy Sam Bankman-Fried once said, “I’m very skeptical of books. I don’t want to say no book is ever worth reading, but I actually do believe something pretty close to that...I think, if you wrote a book, you fucked up, and it should have been a six-paragraph blog post.”

I thought about how funding cuts all over the US are decimating public libraries and about how many books in those libraries have been banned, are being banned, or will be banned. I thought about how much academic research, how much new and crucial knowledge, will be lost to the current crisis.

I thought about how in the dismal future envisaged for us by tech bros and fascists, there likely wouldn’t even be a library to go to—or at least not one that includes John Dewey, progressive thinker, and the authors of thousands of dusty volumes that are taken off the shelves only occasionally. I thought about how, if I could continue to work in education at all, I would probably be doing something much more “efficient” and “productive,” like running 1,500 student papers through my AI grading machine or programming chatbot tutors to make sure they’re equipping students with workforce-ready skills. Or maybe I would be purging my course materials of any reference to “divisive concepts” or advising faculty, in my role at the teaching center, about how to teach the biology of sex or the history of slavery without running afoul of the law—that is, if the teaching center even existed.

It’s hard to say what, exactly, this future would hold. But I don’t think it would include sitting on the floor of the library, in a beam of sunlight, reading John Dewey for no good reason at all.

I won’t recount what Dewey and Watson said in the little piece I read yesterday afternoon. I wouldn’t deprive you of the pleasure of trudging to your own library to read it for yourself. But I will share with you two final quotations—ones that, I think, leave us in a better place than the quotation that initially drew me to the chapter. The first, coming near the end, imagines the future that could come to pass once “the smoke of battle has lifted”:

“We may look forward, therefore, to a society in which teachers are fairly secure and truly free. We can hope that they will be encouraged to attack professional problems in a creative spirit. We forsee the kind of administration which exalts the free and intelligent personality and does not depend upon rules, regulations, formal procedures, and prescriptions. Under these conditions teaching can become the high art that it rarely is today. Teachers individually and their organizations can develop standards for professional work and can work in accord with those standards without being hampered by external worries, limited economic resources, impossible working conditions, military-minded executives, and popular misunderstanding of the function and work of the schools.”

After imagining this future, the authors go on to note that it is, at least in part, within our own hands:

“The free society is being made—or defeated—by present practices. We have described a possible education of tomorrow, not with the desire to have teachers yearn for that bright day but rather with the hope that this picture may serve as a criterion in the evaluation of present activities. What we do today in revising a curriculum, studying psychology, preparing a lesson, educating a teacher, addressing a group of parents, or passing a resolution in our organization of teachers will take us a little nearer to, or remove us farther from, the practices which have here been envisioned for a free teacher in a free society. What teachers do as citizens in supporting or failing to support the movements which endeavor to protect or to extend democracy may also contribute, even more than usual classroom practices, to the realization of a better society and consequently a better education.”

May we all take Dewey and Watson’s advice to envision the education of tomorrow and to let that vision inform the actions we take today. May we all make choices to protect and extend democracy everywhere we can, every day we can, for as long as we can. May we all, one day, be free teachers in a free society.

I’m excited to share something a little different for today’s post. If you’ve followed this blog for long, you know that I have been hugely influenced by the work of —particularly his book Why They Can’t Write: Killing the Five-Paragraph Essay and Other Necessities and his chapter in Susan Blum’s collection Ungrading: Why Rating Students Undermines Learning (and What to Do Instead). John’s new book is about a topic I also discuss here often: AI.

More Than Words: How to Think About Writing in the Age of AI came out earlier this year, and I love the humane and insightful takes it offers on teaching and learning in this moment. It also, like all good books, left me with as many questions as answers. I reached out to John to see if he might respond to a few of these questions, and he graciously agreed. Our conversation is below.

ED: “More Than Words,” in addition to being a chart-topping acoustic classic of the early 90s, is also a great book title. I know decisions about titles are made in collaboration with publishers—but what ultimately attracted you to the idea of writing being “more than words”?

JW: I wish I could take credit for the title, but it was one that was proposed to me by my editor, Emily Taber, in consultation with various folks at the publisher after they saw the first full draft of the manuscript. I knew the title on my book proposal, “Writing with Robots: Staying Human in the Time of Artificial Intelligence” was a placeholder since it was more designed to get the attention of publishers than something that reflected the book I intended to write. I’m pleased to think that writing is about more than words is a sentiment that others were able to pull out of the text, and now I get to take credit for it, while planting the 90s earworm into readers of a certain age.

ED: As someone who writes a lot, and enjoys it, I’m personally convinced by your argument that writing is thinking, writing is feeling, and writing is a practice. This is how I experience the activity of writing: I start with an initial impulse, and then writing helps me explore that impulse further and understand it better. That makes tools like ChatGPT mostly irrelevant to my writing process.

Obviously, this is not the case for everyone. Many students I know, and even more advanced individuals whom I deeply respect, don’t experience writing in this way. Rather, they have a sense that they know what they think and they know what they feel but that it’s inordinately difficult for them to translate those thoughts and feelings into words. Often, these folks see generative AI as a tool that helps them do that: it can take the thoughts and feelings they have but struggle to articulate and give words to them. It’s not thinking and feeling for them; it’s simply communicating their own thoughts and feelings more clearly or eloquently than they could themselves.

I have enormous sympathy for this. What would you say to people who feel this way about their writing?

JW: I’m sympathetic to every experience of writing struggle because I’ve likely experienced that struggle myself. During graduate school, pursuing my MFA in creative writing, I wrote, at a minimum 100,000 words of fiction for three straight years. Volume was not a problem, but the quality was well below what I envisioned for myself, as compared to the urgency of what seemed to be in my head and spirit. I finished grad school pretty certain that writing was not something I should be doing.

After not doing any personal writing for a few months, I was struck by an image while I was walking in the city (Chicago) and went home and wrote a story (“The Circus Elephants Look Sad Because They Are”) that became the first short story I would ever publish.

As I describe in the book, I also simultaneously discovered that the struggles I’d been having expressing myself as I wished in graduate school had helped me develop a robust writing practice that was quite effective in the real-world job I had at the time in market research. If anyone knew this was a likely outcome of my studies, they didn’t tell me. (I wish they had.) But that fact of it is undeniable.

I wouldn’t go so far as to say that the struggle is the point of writing, but the truth is something like that. This isn’t to say we should always struggle or measure success by how much we struggle, or even that there aren’t occasions where removing some measure of the struggle might benefit us, but I think it’s important that everyone has experience with this struggle and success at coming out the other side of that.

School is often not a great place for that kind of experience, which is one of the reasons I’ve been thinking about these issues for a long time. I don’t think that once struggle is experienced this means that everyone would or should become writers, but it’s something that we should at least be familiar with.

ED: Along the same lines, you write that coming up with a first draft is the most important part of the writing process for our students because it’s the most human part—the part, presumably, where all the thinking and feeling happens (or most of it). I think this is the case for me and probably most other writing teachers. But I do wonder if it’s the case for everybody. What if for some people the thinking is in the…well, thinking? One of my good friends (who identifies as neurodivergent) writes by taking a long time to think, turning over ideas and sorting them out purely in her brain for weeks on end. She then writes thousands of words, sometimes whole articles, in one focused sitting over the course of several hours. And that’s the draft. For others, I wonder if getting down a jumble of messy notes and ideas is where the thinking happens, but then putting those ideas into organized paragraphs and sentences is simply drudgery, grunt-work that generative AI could assist with. I’m a little worried that writing teachers or successful writers are making judgements about AI use based on how we learn, think, and write but that our assumptions may not hold true for people with other kinds of processes. How would you respond to that concern?

JW: Everything you describe here sounds like writing to me, but that’s because I see writing as much more than just the moments where I’m putting words on a page. I do a lot of writing when I’m walking the dogs or on the Peloton. I’ve had the experience of waking up in the middle of the night and realizing some of my brain has been writing and then trying to capture that notion before it slips away from me.

I don’t think I fit any definition of neurodivergent, but I’m similar to your friend in that I’m a long thinker, though often that thinking also takes the form of writing. A bunch of what wound up in the book first took form as posts at Inside Higher Ed or my newsletter that were then subject to additional thought. A big part of developing one’s writing practice (the skills, knowledge, attitudes, and habits of mind of writers) is developing that knowledge of one’s own process, so whatever anyone does to make it work is a-ok with me. People looking at my process from the outside would probably be horrified (I know my 8th grade teacher who insisted on outlines first would be), but it works for me, emphasis on for me.

My concern around AI use is when it removes some of the learning friction that would help people develop their process.

ED: I was really struck by your ideas about taste and the implied claim that one of our roles as teachers is to help students explore, develop, shape, and express their own tastes. I’m interested in this in part because I find that more and more students come into my classes without much idea of what they do and don’t like, what compels or engages them. Not just in writing—in everything. When I ask about their hobbies and interests, a nontrivial number of them just…don’t seem to have any (or at least none they’re willing to tell me about). Last year, I asked students to tell me their favorite holiday songs for a class playlist, and a few of them just shrugged and said “Jingle Bells,” simply because it was the most popular song they could think of. I’m not saying that students’ ambivalence about corny Christmas songs presages the end of civilization or anything. But I do worry about how life in the “Filterworld,” as Kyle Chayka terms it, prohibits the development of “unique human intelligences” and the fact that school doesn’t seem to be helping. What are some things you think we can do, as teachers, to help students discover and hone their own unique tastes?

JW: I’m a big fan of giving students access to experiences that simply allow them to react to something as the humans they are (rather than as students) and then have them reflect on their reactions. I’ve done this by reading short stories out loud, or playing music, or showing short humor videos or requiring students to go literally have an “experience” in the world that they respond to.

If a student is shown something humorous and they laugh, that’s an expression of taste. If they hear a song and tap their toes or want to hear it again, that’s taste. If they’re involved with a short story, that’s taste. We can then try to articulate what it is we like and build that sense of our own individual tastes bit by bit. It does take practice though. We’re wired to respond, but we’re not wired to think about our responses.

ED: A related question I have is about the “spikiness” of human writing and experience, and the ways all that gets sanded down by AI. You share many moving anecdotes from your own life in the book, and I’ve heard interviewers ask you about a few of them. The one that most resonated with me is one nobody else, as far as I know, has mentioned, and it’s barely more than an aside in the book. It’s your experience of attending a community theater performance of Spamalot that was “an exceedingly game effort by every member of the production, but obviously well short of Broadway quality.” And yet, at the end of the performance, you spontaneously sprang to your feet in enthusiastic appreciation of the achievement.

As someone who has attended, and participated in, a lot of bad community theater, I was so moved by this. Some of my fondest memories are associated with very poor attempts at theatre-making. But of course, the point is not to put on a beautifully polished performance so much as to engage in the experience, to learn something about art or ourselves, to connect with each other or with our community, to have fun—in short, to indulge our humanity for a little while. If you could somehow magically create a ChatGPT for community theater that would improve the quality of the productions, you would absolutely ruin the entire enterprise.

I worry that we’re all having fewer and fewer of these kinds of spiky human experiences. Acting or painting or dancing or writing badly, just for the sake of experiencing it or connecting with someone else, isn’t valued by anyone. It’s not productive; it can’t be monetized; it won’t get a good grade. But I also recognize that this is a hard sell for most people, students and teachers included. “Make bad art!” “Value poor writing!” “Fully indulge your humanity!” I don’t think people get it. How can we convince our communities that this stuff is worthwhile?

JW: We can convince everyone else that this stuff is worthwhile by going to more exceedingly sincere, but not super fantastic community theater! I think the roots of this problem are very deep and the way that school has been increasingly organized around so-called “college and career prep” is largely to blame. The disappearance of art and music classes from early grades is a tragedy, not because we’re missing out on great art but because the attempt to make something is worthwhile in and of itself, and then sharing these attempts with the world is additionally affirming.

My grade school had an art show every single semester where every student displayed their “best” work of the half year. Our work was hung on the walls with our name and grade below it.

To say I was not a gifted artist is beyond an understatement, but I still have a cross stitch from fifth grade meant to honor my parents’ alma maters, (University of Michigan/University of Wisconsin) that was made an “M” when hung one way and a “W” when hung the other way with the colors of the letter split between the Michigan maize and blue and Wisconsin’s red and white.

This was not art, but it’s something I was still proud of because I made it.

ED: One aspect of teaching that we both think about a lot is assessment and grading. In fact, my first (conscious) encounter with your work was your chapter in Susan Blum’s Ungrading, the one with the extended metaphor about Wile E. Coyote as a “hero of ungrading.” This is a metaphor that, I think, illustrates your own “spikiness”; it’s something ChatGPT would never come up with in a million years. I was so struck by it that it made me flip to the author bios in the back of the book asking, “Who the heck is this guy?”

Anyway, in More Than Words, you write about some early experiences of grading in which you had the sense that you were not “reading” student work so much as “processing” it. I think a lot of instructors, if we’re honest with ourselves, feel this way! How did you make the move from processing student writing to reading and engaging with it on a deeper level?

JW: It was a move made out of desperation because I realized that this work I had been doing for quite some time and that I liked and valued was becoming drudgery and I was increasingly alienated from my own job. The journey became one of examining my values around reading and writing and trying to live those values in my teaching by focusing on what I thought was important. This is where “the writer’s practice” came from, with the alternative grading being the final piece of the puzzle that allowed me to focus on those elements of the writer’s practice.

I think most faculty are drawn to their fields at least initially out of a sense of fascination. I was sort of instantly fascinated about the challenge of teaching so that shift was a way for me to maintain that fascination by continuing to experiment. If folks find themselves stagnating like I was, my advice is to try something, even if it winds up being a Wile E. Coyote-style disaster. Mr. Coyote always comes back for more. There’s always another semester, another class.

ED: What has to change, structurally, for more instructors to be able to move from “processing” to “reading”? What kinds of things can we do in the absence of those structural changes to make assessment more meaningful for students and teachers?

JW: My advice is to focus on reading not to assess but to appreciate, and to work from that place of reaction and taste, rather than judgment. The mistake I was making was to focus on the (inevitable) errors my students were going to make on school assignments, rather than treating their writing as writing. This also required me to make sure what I was asking them to do had authentic purpose and audiences so students could tackle a problem that wasn’t ultimately just something to be graded.

ED: This last question is cheating a little, since it’s about something you said on a recent podcast episode rather than in your book. On Tea for Teaching, you mentioned that you were really interested in the idea of learning through apprenticeship and also concerned that technologies like generative AI might threaten apprenticeship models.

I’m interested in this for two reasons. The first is because you mentioned a kind of apprenticeship in your own experience of learning to teach. Specifically, you talked about how transformative Ken Bain’s book What the Best College Teachers Do was for you—I think you said “literally life changing.” That’s what your own book Why They Can’t Write did for me. It completely transformed the way I think about teaching writing. So, this seems like a good opportunity to thank you for that!

But the second reason this interested me is because I’ve been toying with the idea of bringing an apprenticeship model into my writing classes for a few years now. I think the idea first occurred to me when I read Stop Talking: Indigenous Ways of Teaching and Learning and Difficult Dialogues in Higher Education and was inspired by a kind of indigenous pedagogical approach that’s not based on instruction and explanation but rather, in part, on “observation and emulation”: a learner coming alongside a teacher to watch them work, reflect on that, and then try to do it themselves.

“Stop Talking” offers an interesting challenge to classes that are all about words (and has interesting intersections, I think, with your own argument that we should think of writing as being about “more than words”). Anyway, this has run on for too long, so I’ll get to my real question: Do you have any thoughts about how we might bring apprenticeship as a way of learning into the writing classroom? Or even into the pedagogies of other disciplines?

JW: I’m sort of flabbergasted that Why They Can’t Write had the same effect on you that What the Best College Teachers Do had on me, so I thank you for that, even though I have a hard time believing it.

I’m thinking about what Ken Bain’s book, my book and the book you cite have in common, and I think is the combination of “authenticity” and “emulation.” The teachers that Bain highlights are, above everything else, authentic and the expression of that authenticity informs every aspect of how they teach. We look at them as sources for emulation, trying to capture the spirit without copying them exactly - because that would be inauthentic.

The core argument of Why They Can’t Write is that we have students doing writing “simulations” rather than giving them authentic writing experiences. That said, we often start as writers by emulating others we admire as we find our own unique voices.

There is an obvious cultural aspect to all of this, and unfortunately the culture of schooling has become increasingly separate from authentic learning.

The idea that learning has become separate from schooling is a bleak note for our conversation to end on. But recent books like Susan Blum’s Schoolishness: Alienated Education and the Quest for Authentic, Joyful Learning and John’s More Than Words give me hope that we can recapture some of authenticity and joy we’ve been missing lately.

Many thanks to John for sharing his thoughts here. If you teach writing in any capacity, or if you’re a writer yourself, you should read More Than Words ASAP!

I think one of the biggest fears about collaborative grading is that if you give students too much control over their grade, they won’t do the work to earn it. That is: too much autonomy means too little accountability. We must assign grades and points to students so that they’ll take responsibility for their learning. We can’t let students propose their own grades because they’ll just propose an A, regardless of the work they’ve done.

This is a logical train of thought. After all, in traditionally-graded courses, grades often seem like the only thing motivating students to do any work. And even then, it’s pretty touch and go. If we don’t attach grades to students’ assignments, what will hold them accountable? If they can’t even take responsibility for their attendance when there are points attached, what will happen when we take the points away? If they don’t work for the grades we assign, how much less will they work for grades when they’re in charge of assigning them?

I get why people think this, logically. But in practice, I find that these assumptions are often wrong. In my classes, at least, it seems that giving students more autonomy does not, in fact, result in lower levels of accountability. I’m always struck by this when I receive student comments about my grading system at the end of the semester. Here are a few representative examples from my last two surveys (shared with students’ permission):

• I like the grading system a lot. I like how we have a conversation about what we think our grades should be. It supported my learning because it helped keep me accountable for turning in my work on time.

• I love how open the grading is. It gives us students the responsibility to obtain our grades. I want to have good grades, and this way of grading leaves it up to me. I have a weird way of learning, so sometimes, basic classes and grades don’t represent me as a student. This way of grading allows me to be accountable for myself, and there is no excuse for my grade because it was up to me. This also makes me want to show up and put the work in because I know it[’s] all for me, and it makes me more proud of my work.

• I really like how we also have our own say in our grades (if we are reasonable of course) and it made me want to make my work better because I was allowed to go back in an[d] edit all my essays without one and done with one bad grade.

I want to clarify here: I talk with students about how this grading system is designed to support their learning, but we never discuss the fact that it makes them accountable for their own work. They come to these conclusions entirely on their own.

I should also say that not every student feels that collaborative grading keeps them accountable. I received this comment from a student last fall:

I think the only downside has been keeping myself accountable not even necessarily with work/assignments but with attendance. I found I was quite stressed with higher stakes exams coming up causing me to forget or not be able to prioritize this class as well.

I think the experience of students prioritizing work for less-flexible classes will feel familiar to many collaborative graders. While some students have told me that the autonomy they have over their attendance in my class makes them more, not less, motivated to attend, others have struggled to take responsibility for this aspect of their engagement.

Additionally, I’m not sure whether students in my collaboratively graded classes actually behave more responsibly or whether they just feel more responsible for their own behaviors. It’s possible the grading system does not increase accountability so much as help students better understand their own role in their education.

Even still: giving students more autonomy over their grades does not appear to decrease their levels of accountability. It may even help students take more responsibility for their work.

Why does this feel paradoxical, or at least counterintuitive, to us? I don’t know the answer to this question, but I suspect there are some vestiges of behaviorism at work. Behaviorism was a school of thought pioneered by people like Ivan Pavlov and B. F. Skinner.1 I’m veering way out of my expertise here (psychologists: please don’t come for me), but I think one way of characterizing behaviorism as posited by these thinkers is that humans and animals can be conditioned to behave in certain ways through positive and negative reinforcement or systems of reward and punishment. Think: drooling dogs and rats in cages, who are trained to respond to specific stimuli in specific ways.

Skinner’s ideas apparently had some influence on the development of modern schooling in the first half of the twentieth century. And while twenty-first century psychologists have moved on from behaviorism, some educational structures informed by this theory of human behavior remain intact. Arguably, our grading systems are a prime example.

This was partially the subject of Alfie Kohn’s 1993 book Punished by Rewards: The Trouble with Gold Stars, Incentive Plans, A’s, Praise, and Other Bribes, an extended critique of Skinnerian behaviorism and its various manifestations in work, school, and parenting. Kohn argues here and elsewhere that the extrinsic reward of a grade undermines learning by dampening intrinsic motivation—an argument that will likely be familiar to most alternative graders.

Kohn and others base their arguments on more modern theories of human motivation, like self-determination theory. It probably won’t surprise you to learn that one of the major pillars of self-determination theory is autonomy: having a sense of choice and acting on one’s own volition is a major psychological need—and, it turns out, an important element of human motivation. It’s hard for people to feel motivated when they feel they have no control.

So, maybe we’re surprised that more autonomy in grading = more accountability in schoolwork because the grading systems we’ve been working with are all tangled up with behaviorist thought. When we start thinking in terms of self-determination theory, it’s really not surprising at all.

But perhaps the relationship between autonomy and accountability I’ve described also feels counterintuitive because under current conditions, we do seem to need carrots and sticks, at least in some form, to make this whole thing go. Here’s a thought experiment to illustrate what I mean:

If, on the first day of class next semester, I promised all my first-year writing students that they would receive an A for the course whether or not they completed any work for it or even attended our class meetings, how many would show up the following day, just for the learning? A few, probably. In other kinds of teaching contexts, maybe more—maybe all. It’s not impossible; I’ve heard of this happening. But for my students, in my required gen ed course, at my SEC-football-and-Greek-life-obsessed state flagship? Not many.

This is not, contrary to what some may believe, because students are naturally lazy or deficient. It’s because we’re not operating in a culture that values learning or a system that supports it. Students have been working under the threat of punishment and promise of reward for so long that it barely even occurs to many of them that there are other reasons to engage in school. They also know that their transcripts and resumes matter immensely for their future economic opportunities and whatever intellectual or personal growth they experience along the way will probably be secondary, at best.

This system makes it difficult for students to develop any real sense of accountability. They’re always working to please someone else, to earn that reward or avoid that punishment from whoever’s doling it out this time: the parents, teachers, or employers who set the deadlines and make the rules. You can’t be accountable for yourself if you’re always under someone else’s control. You can’t take ownership over something you don’t own.

We, as instructors, think we’re helping students develop a sense of accountability when we double down on behaviorism, adding more carrots and sticks to the learning process. “Stricter rules! These kids have to learn responsibility!” But what we’re actually doing is exacerbating the problem: hampering students’ autonomy, forestalling the development of any real accountability, and trapping both them and ourselves in a never-ending cycle of mere educational transactions. You do the work, you get the reward, over and over and over.

So, this is part of what I’m trying to do with collaborative grading: change the conditions of school so that students can develop a sense of agency and accountability in their own learning. I am trying to give students the freedom to take responsibility.

And, okay, maybe I’ve got to have some extrinsic motivators to get that process started. They’ve got to hang with me long enough for me to convince them that there are other ways to approach school and life. When they’re working their butts off for their other classes, they need a little push to remember that their writing class is important, too. And maybe some forms of extrinsic motivation aren’t so bad. I, myself, love a gold star. I’ll do anything for praise. I am sometimes motivated to complete unpleasant tasks simply for the sweet, sweet dopamine hit I get from checking them off my to-do list.

But what I ultimately want is for us to live in a world where I don’t have to hang rewards and punishments over students’ heads to get them to engage in the deeply interesting work of learning. The extrinsic motivators I use now are, for the most part, concessions to an imperfect system. I want to change that system.

Maybe that’s idealistic, but I think we can do it, eventually. I think we can help students, and ourselves, take baby steps away from the transactional, reward-based model we’ve built and into more meaningful learning experiences. I think we can start by prioritizing student autonomy.