Monthly Archive: September 2026

Now that I’ve started, I can’t stop seeing new angles on this topic.

I just started Adam Gopnik’s The Real Work. It’s a book-length meditation on how to find mastery in your life.¹ He’s talking about mastery on page 2, but he could have been talking about effort and satisfaction in learning:

What really moves and stirs us is accomplishment, that moment of mastery when suddenly we feel that something profoundly difficulty, tenaciously thorny, has given way, and we are now the Master of It, instead of us being mastered by it.

That’s what we need to inspire in our students, a feeling of mastery, that they are masters of the subject, not the other way around.

1. “The real work” is a phrase magicians use when talking among themselves about their craft. It is, in Gopnik’s words, “what makes a magic effect magical.” ↩

Education is like the gym. No one lifts weights because they from the floor because they are trying to move them. They lift weights because they’re strengthening their body to meet challenges they don’t know about yet. Education strengthens the mind for mental challenges we haven’t anticipated.

That’s not a new analogy. I’m sure you’ve read it before. I mention it because Dan Rockmore¹ has a nice essay in The New Yorker making the point again: “Should the classroom be more like the gym?”

The great challenge of teaching is not to transmit information—machines do that cheaply now—but to engineer those moments of genuine understanding reliably enough that students come to crave them. The intellectual equivalent of the body’s feedback is real and, when it arrives, more powerful than any endorphin.

1. Rockmore is the William H. Neukom 1964 Distinguished Professor of Computational Science at Dartmouth. https://faculty-directory.dartmouth.edu/daniel-rockmore ↩

OpenAI released ChatGPT on November 30th, 2022. I knew immediately that it would change how educators. I thought I would encourage students to use ChatGPT help them write code in R for my population genetics coursein Fall 2023. Fall 2023 arrived and I didn’t know how to tell them to use it, so I didn’t even mention it. Fall 2023 was also the last time I taught in a classroom. I retired in July 2024. ChatGPT, Claude, and other large language models (LLMs) had little effect on classroom instruction before I retired.

I know from talking with colleagues and from reading the Chronicle of Higher Education, Inside Higher Ed, and Times Higher Education that college faculty are struggling to adapt to a new world. Some are embracing LLMs. More are banning them. Some are encouraging students to use them and helping them understand how to use them. More are returning to in-class assignments and blue book exams.

Maybe it’s because I’m retired, but I’m less worried about LLMs than many of my colleagues who teach. I remember the ruckus that exploded when hand calculators became common. People were sure that students would no longer be able to do arithmetic.¹ But the world hasn’t fallen apart. Engineers still build bridges that don’t fall down, and pharmaceutical companies still make drugs with the right composition and concentration. No one uses a slide rule.² We need to make a similar transition with LLMs.

Students should learn subjects, not regurgitate answers. Learning means reasoning independently, evaluating evidence and argument, and applying that knowledge in new contexts. I presume that students still learn how to add, subtract, multiply, and divide in grade school even though most of them will use calculators or spreadsheets when they do calculations later in life. In the same way, students must learn the principles of philosophy by grappling with them, not by asking ChatGPT to produce an essay about them. Education should not be collecting facts. It should be learning how to think. As instructors we’ve put too much emphasis on the products our students produce and too little on the processes they used to produce them.

We must focus on evaluating the processes. Term papers and take-home exams were once a way to do that. We were at our best when we evaluated how students reasoned, not only what they produced. Doing that in an age of LLMs requires a new approach. Let’s flip the classroom more completely.

Set aside class periods where students write papers or solve problems—with pen (or pencil) and paper. For homework have students transcribe their solutions into an LLM and prompt the LLM to critique the solution. When the student returns, have them annotate a paper copy of the LLM critique with pen (or pencil) and evaluate that.

This approach will require colleges to change scheduling practices. The class periods for problem solving will need to be longer than the typical 1-hour lecture, and it will probably be necessary to add the problem solving period to the existing lecture schedule rather than reducing the number of lectures.³

I wrote the first draft of this post before I happened across this post on Substack by Liz Dubelman. I highly recommend that you read it. Dubelman describes an approach very similar to what I describe here, but she describes it in more detail and much more effectively than I did.

1. To be sure, some younger people don’t recognize non-sensical results from a calculator, spreadsheet, or statistical package, which often arise because of input errors. ↩
2. But I learned to use one in college, and I still have the one my father used in college in my office. ↩
3. This is an example of how scheduling practices might be changed. The details will depend on the subject and the academic calendar of the college or university. ↩