How to Grade: Alternative Models for the College Classroom

I have more book news to share! Y'all know I'm a co-author on The Norton Guide to AI-Aware Teaching coming out this summer. I had such a great experience writing that book with Annette Vee and Marc Watkins that when Emily Donahoe reached out to see if might be interested in collaborating with her on a new book about grading, I said most definitely. The book's working title is How to Grade: Alternative Models for the College Classroom, and it will be published by Princeton University Press as part of their Skills for Scholars series.

Emily and I worked together at the Center for Excellence in Teaching and Learning at the University of Mississippi for a couple of years. We have similar personalities, which is probably good for writing together? We definitely have the same birthday. You might know Emily from her Unmaking the Grade blog, where she has been chronicling her experiences with alternative grading practices, or her forthcoming solo-authored book, Collaborative Grading: A Practical Guide, part of the Teaching, Engaging, and Thriving in Higher Ed series from the University of Oklahoma Press. Emily has also been on the Intentional Teaching podcast three times: twice as a "Take It or Leave It" panelist (in 2024 and 2025) and once helping me interview Leonard Cassuto about doctoral education.

Emily and I have another co-author on the new book, Lindsay Masland, executive director of the Center for Excellence in Teaching and Learning for Student Success at Appalachian State University. Lindsay is doing fantastic work at App State supporting teaching at the institutional level, work she shared on the Intentional Teaching podcast back in 2024. I appreciate the blend of strategic thinking and compassion and care that Lindsay brings to her educational development work. And her enthusiasm! Just read her LinkedIn post about the new book project.

Emily, Lindsay, and I worked together last fall on the Alternative Grading Institute sponsored by the Center for Grading Reform, and I'm excited to be working again with both of them on the new book. I should note that Josh Eyler, another University of Mississippi colleague, was Emily's original co-author on the book. Josh had to step away from the project, but his envisioning of the book with Emily is part of the book's DNA.

When will How to Grade: Alternative Models for the College Classroom be available? Well, we have to write the book first! I'm expecting a 2028 publication date, but it's too early to tell.

Not My First EdTech Rodeo

My Norton Guide co-authors Annette and Marc and I were the guests yesterday on Bryan Alexander's Future Trends Forum, a long-running live video conversation series exploring the future of higher education. We covered a lot of ground in the conversation, and the text chat was a firehose. At one point, Bryan asked us about institutional responses to generative AI, I said something comparing AI to prior technological disruptions of higher ed, and Forum participant Arthur Fridrich made a comment in the chat that's stuck with me: "The scale is different, but institutional reactions the same." There's a lot of truth in that comment.

When I compare AI's impact on teaching and learning in higher ed to other technologies of the last couple of decades, like classroom response systems ("clickers"), virtual worlds like Second Life, and massive open online courses ("MOOCs"), it seems clear to me that AI is much harder to ignore. It offers something of potential value to just about every academic discipline, students are using it with or without our guidance, and there are astronomical financial investments going into the technology. The scale of AI's impact on higher ed is orders of magnitude larger than past edtech disruptions, but I'm seeing very similar reactions at the institutional and individual level. You've got your moral panics, your gold rushes, your hyperbolic predictions--all things we've seen before.

Having weathered some of those other disruptions has, I think, equipped me with some useful tools and lenses for making sense of this current AI moment. Here, then, are three lessons learned that seem relevant to higher ed's response to generative AI, all helpful for lowering the temperature just a bit on this hot topic...

There will be hype, and you probably shouldn't believe it. Back in 2012, referring to the impact of MOOCs on higher education, Stanford University president John Hennessy told The New Yorker, "There's a tsunami coming." That same year, Sebastian Thrun, founder of MOOC provider Udacity, told Wired Magazine that in 50 years there would remain just 10 higher education institutions. Did MOOCs have that kind of disruption on higher ed? No, of course not. And it was actually pretty clear at the time that MOOCs wouldn't be that transformative, at least to those of us who understood teaching and learning. The video-and-quiz approach to learning that MOOCs popularized works well for some students, particularly those with sufficient background knowledge and a lot of motivation (like adult learners wanting to upskill for a new job), but most students need the social context of learning--working with peers, developing a relationship with their instructor--to persist and succeed in college. I read a lot of hype about generative AI that makes the same assumptions about the power of personalized learning that the MOOC proponents made back in the 2010s, but I see no reason to believe that the social context of learning is any less important to today's students. This lesson has particular importance for higher education leaders tempted to make big changes in their institutions because of AI.

Faculty will make a variety of informed, intentional decisions about the technology. Did I see any use for Second Life in my mathematics teaching? No. Did my colleagues in the School of Nursing seem very excited about the potential of Second Life for clinical simulations? Yes, and I respected that. They knew their teaching context way better than I did, and I knew them to be thoughtful, caring instructors. For most institutions building virtual campuses in Second Life in the early 2000s, the potential benefits didn't seem to balance the upfront costs in time and energy (something many of then eventually figured out), but there were fields, like nursing, where faculty and instructional designers did see the cost-benefit ratio working in their favor. It was fine for, say, a historian to determine that Second Life simulations didn't have a place in their teaching, while other faculty leaned into the technology and its potential for creating novel and meaningful learning experiences. These days, I find some commentators overly dogmatic about the utility of generative AI as a teaching or learning tool. But I work at a center for teaching and learning and get to see the full spectrum of choices faculty make regarding AI's role in their teaching. Faculty who care deeply about student learning can make very different decisions about a technology's place in their course design, and that's okay.

Implementation choices matter to a technology's impact on student learning. Back in the day, I would regularly read educational research about the use of classroom response systems--"clickers" we called them, back when the systems involved dedicated hardware. One of my pet peeves was getting to the end of an article making some claim about how clickers did or did not enhance student learning and realizing that at no point in the article did the author actually explain how they used clickers in their teaching. What kinds of multiple-choice questions did they pose? Did students answer the polling questions individually or did they discuss them in groups? Were the clicker questions factored into student grades in some way? These were implementation choices that mattered a lot, and yet there were too many studies that didn't detail them. I couldn't generalize a darn thing from studies like that. Or take the classic 2014 Freeman et al. meta-analysis of active learning research. It's true that students in active learning conditions learned more than those in traditional lecture conditions on average, but it wasn't true in every single study considered by Freeman and team! Implementation choices matter. Which is why headlines like "Study finds that students using ChatGPT learned and remembered less than students who did not use it" (something I saw on Bluesky a few weeks ago) aren't actually informative. As Leon Furze said on my podcast when I asked him about that headline, "Read the paper."

The Norton Guide to AI-Aware Teaching

My new book, The Norton Guide to AI-Aware Teaching, co-authored with Annette Vee and Marc Watkins, is now available to pre-order! The ebook is expected to be available July 1st, and print copies are expected to start shipping on September 24th. Here's how you can get a copy:

1. Our publisher Norton is pleased to offer the guide as a free ebook for all instructors currently using a Norton textbook. If that's you, you'll receive access from the Norton team when the ebook is available July 1st and can contact your local Norton representative with any questions.
2. If you would like to pre-order the ebook so that you have it July 1st, you can now do so through Amazon and Barnes & Noble and perhaps other retailers.
3. If you would like to pre-order the paperback version of the book, you can now do so through Norton, Amazon, Barnes & Noble, and likely other retailers. If you go through Norton, be sure to use the code AIFREESHIP at check out to get free shipping!
4. If you would like to order multiple copies for a campus reading group or some other faculty development effort, Norton has an option for you: On orders of 10 or more print copies, we offer 50% off the list price and free domestic shipping. (Such orders must be on a nonreturnable basis.) To take advantage of this offer, contact Peter Wentz at pwentz@wwnorton.com with subject line “Norton Guide to AI-Aware Teaching.”