When AI Does the Homework, Who Does the Learning?

Artificial intelligence is quickly becoming part of the college classroom, whether instructors are ready for it or not. We talk about how we’re using AI to save time as teachers, from writing better quiz questions to helping us prepare course materials, and how we’ve experimented with bringing it directly into class. But the bigger question is what happens when students use these tools to replace the struggle that’s supposed to lead to learning. We dig into where AI can make students stronger, where it might create shortcuts that hurt them, and how all of this is changing the way we think about teaching.In this episode, we talk about:* How LLMs can save instructors time on quiz questions, distractors, announcements, and other teaching prep* Bringing AI into class to work through game theory and other economics problems alongside students* The growing divide between students who use AI to build skills and those who use it to avoid building them* Why struggling with a problem is often part of learning, not an obstacle to it* How AI is forcing us to rethink assignments, assessments, and what we actually want students to get out of collegeIf you liked this conversation, you might also enjoyThis Week’s Drinks 🍻We recorded this one in the morning, so the drinks looked a little different than usual. Jadrian had coffee with a splash of Buffalo Trace Bourbon Cream, but only because he wasn’t heading straight to work after recording. Matt kept things technically non-alcoholic with Diet American Dew, served in a Michelob Ultra FIFA World Cup cup for a little extra flair. Name That Stat 📊Since we’re talking AI in the classroom, Jadrian shared a stat that looked at how common artificial intelligence use was among college instructors. We know the students are using it to help in the classroom, but what about the professors? Matt went in an entirely different direction, with a focus on Broadway revenue. Despite concerns about ticket prices and struggling shows, Broadway is still generating a pretty remarkable amount of money.Show NotesAI is already in the classroom, but “using AI in the classroom” can mean a lot of different things. We’re both using LLMs as part of our jobs, especially for the repetitive work surrounding teaching. They’re great for cleaning up clicker questions, generating new versions of questions, suggesting plausible wrong answers for multiple-choice questions, and helping draft announcements. Anyone who has spent too much time trying to invent a fourth believable distractor knows how useful that can be. The important distinction is that much of this happens outside the classroom. But Matt has actually experimented with putting LLMs in front of students. One example came from his game theory course, when he introduced the classic beauty contest game. Everyone chooses a number, and the winner is the person closest to a fraction of the group’s average. After playing the game with students, he asks the LLM to play along, too. What makes the response interesting isn’t simply whether it lands on the “right” answer. It can explain the equilibrium, recognize that choosing the equilibrium probably won’t actually win against a room full of humans, and reason through how many steps ahead the other players are likely to think. Matt has also done a similar in-class experiment with a coordination game, showing how an LLM could distinguish between the factually correct answer and the answer most likely to be chosen by everyone else. The harder part of this conversation is what we’re seeing from students. There’s a potentially huge difference between using AI to help you learn and using AI to avoid learning. Strong students can use these tools to go further than they could before. They are asking more questions, exploring new ideas, improving