This week included a quick trip to New York City to share a few thoughts on AI-aware teaching with the editors at W. W. Norton. During their annual editors retreat, they spent some time thinking about AI, how its changing higher education, and what that means for a textbook publisher like Norton. All the editors had read The Norton Guide to AI-Aware Teaching, and it was really fun to hear their thoughts on the book. These are folks who interact regularly with faculty around their teaching, as I do, and I enjoying hearing, for instance, the music editors' take on the teaching challenges AI poses for music professors. Many thanks to Norton's editor-in-chief for science, Betsy Twitchell, for including me in the retreat!

I was the Wednesday morning outside speaker, and I talked about some of the changes and trends I've observed since Annette and Marc and I submitted the manuscript for the book in the spring. I noted the growth of agentic AI and the sunsetting of some popular custom chatbot tools. I shared about faculty who are vibe coding learning materials for their students, law schools that are banning AI, and concerns highlighted in that MIT report about students not studying with other students because of AI. And I noted that we're finally in a position to hear what educational research has to say about AI and learning because there are enough published studies to start to see themes.

The workshop following my session was all about the ways that Norton might support faculty in the challenges facing assessment now thanks to AI, so Betsy asked me to frame those challenges for the editors. In preparing for the talk, I realized I had, within a 48-hour period, heard two different faculty perspectives on this topic that, between them, both named the core challenge and offered a way forward. One perspective was shared by an art history faculty member who said something like the following:

The next day, while listening to the CS-Ed Podcast produced and hosted by Kristin Stephens-Martinez, a computer science professor said something along these lines:

My conclusion is a notion that isn't new, but seems more important than ever: We need to center the process of learning, not the product. Because of AI, we can't reliably evaluate learning by looking at the products our students submit, but the process can make learning visible in ways we can assess. And more fundamentally, if students believe that the product is the goal, as many have been conditioned to believe over years of education, they won't see the point, given what AI can produce. However, if we can help students see the process as the goal, to understand the value that a good learning process can bring, then we give them a reason to engage in the hard work of learning, instead of hitting the AI "easy button."

Our colleagues in writing instruction figured this out a few years ago, which is why two of them are on the author team for The Norton Guide to AI-Aware Teaching! It's also a principle that's easier said than done. But there are strategies that keep showing up in the most thoughtful and effective faculty responses to AI that show a way forward.

• Design more authentic assessments, ones that are drawn from realistic professional or domain contexts, that require navigation of ambiguity or complexity, and likely engage an interested (real or hypothetical) audience. These are the kinds of assignments that will have real value for students as they look at their future lives and careers.
• Be transparent about the purpose of an assignment. That is, first make sure that the assignment has value to the student, then do what you can to communicate that value. And, as the Transparency in Teaching and Learning project advises, also be transparent about the assignment's tasks and grading criteria.
• Scaffold a useful learning process. Build sequences of learning experiences in which one prepares students to engage in the next. If we're going to center the process, we need a thoughtfully designed process! This might involve developing a more robust process for traditional assignments or updating a process to be more AI-aware.
• Make student learning visible through process tracking. Any well-scaffolded assignment, with proposals and bibliographies and drafts and peer review, provides opportunities to see students process, but we might need to introduce some new ways--annotated readings or student conferences or failure reports. (Note that I'm distinguishing between process tracking, which can make student learning visible and promote student metacognition, and process tracking software, which leans toward surveillance and AI detection.)
• Lower the stakes through alternative grading. So many of the faculty I know who are responding well to the challenges AI poses have adopted some kind of alternative grading scheme--standards-based grading, collaborative grading, contract grading, and so on. As philosopher C. Thi Nguyen writes in his new book The Score, "Scoring systems are an instruction manual for new values." If we want students to value the process over the product, we need a grading system that points them in that direction.

What does this all look like in practice? Well, lots of different things, all depending on an instructor's goals and students and situational factors. One reason The Norton Guide to AI-Aware Teaching is 400 pages long is that we've tried to share examples of these strategies, some engaging with AI and some resisting AI, in a variety of contexts. I shared a few of my favorite examples with the Norton editors, including some drawn from past and future episodes of Intentional Teaching. Stay tuned to the podcast to hear from some past podcast guests about the teaching approaches they're implementing today.

Thanks again to the team at Norton for hosting me and for supporting this great new book! Both digital and paperback versions of the book are now available through major online retailers like Amazon and Barnes & Noble as well as from smaller retailers like Bookshop and from Norton. And if you'd like to do a bulk order like VUSN did, see my website for info on getting a bulk discount.