How My Students Think About AI

Context: I am an instructor at a public university in the United States.  This reports how students at my institution appear to be thinking about AI as of spring/summer 2026.   This is drawn mostly from interaction with my own students (both in spring semester classes and a summer class) as well as from a day-long workshop on AI that I moderated for a student organization.  Input from my students took the form of universal, written, pre-class submissions plus self-selected participation into discussion. What I present below mostly takes the form of a synthetic consensus from these discussions.  There were obviously a range of views on any given issue. Student Background: The students from my courses who participated in these discussions have moderate exposure to AI agents via those courses.  All of them had nearly completed a Claude Code project by the time of the discussions and had extensively used AI for other coursework (in addition to whatever personal use predates that).  They had done readings (which varied across the courses) establishing baseline knowledge on AI, the geopolitics of AI, and AI risk.  I had also lectured on these topics.  The students participating in the workshop had self-selected into a day-long intensive event on AI but I can’t speak to their exact level of background knowledge or exposure to ideas. Biases: My courses are all in the general area of global politics, and AI (inclusive of AI risk) fits into them as a topic of geopolitical (and especially national security) importance, taking over the “topical” slot at the end of the semester where in another world we might be talking about Ukraine or Iran or the Trump tariffs.  This, and my other views, probably have some influence on the students.  The frames they likely have picked up from me: AI is very important.  If nothing else, dedicating multiple course periods to the topic shows this via revealed preference.  This is also something I have stated to them in an unqualified way. Given overall course content, students will naturally think that international competition is important, that tools like the prisoner’s dilemma are an important way of thinking about situations like AI, and will have absorbed a healthy skepticism about international law. My general approach telegraphs a receptiveness to “sober” forms of argument, an interest in counterintuitive strategic thinking, a distaste for wild speculation, an aversion to nakedly ideological perspectives, and a sense that the past is important for understanding the future.  My students probably make some effort (implicit or explicit) to speak in ways that respond to this. Perspective #1: There has not been rapid AI progress My students do not have any intuitive sense that there has been rapid AI progress in recent years or really have much of a framework for thinking about that issue.  They have been using AI chatbots regularly since shortly after the launch of ChatGPT, see them as a major aid in doing schoolwork, and have not noticed much improvement over the last few years.  With the exception of image/video generation, GPT-4 could do most of what they were looking for from a chatbot.  Three years is a long time in their world, and their sense is that chatbots have been a mature technology over roughly that amount of time.   They are an imperfect technology — students are well-aware of hallucinations — have been one, and will continue to be one.   When we talk to employers about skills in the AI age, they are are all hungry for “AI native” graduates.  I think they are mostly going to be disappointed.  Students do not use AI particularly well, and most of them in my classes have apparently never heard suggestions like “if you’re using AI to study, feed it everything you can from the class first — the syllabus, slides, readings, etc.” This is not true of everyone, but many of them are using AI in crappy ways, getting crappy results, and blaming the model. My students (who overwhelmingly are non-technical and rarely have done any kind of coding) were relatively unimpressed with Claude Code as a measure of progress.  Some of this may be on me for the way I set up our Claude Code lessons, but they seem to have the sense that this is roughly how software engineering has been done for a long time.  They mostly came away from Claude Code with the sense that this is a genuinely useful tool that they didn’t previously know, but the gut-level reaction here is: “I never knew that building software was so easy” rather than “It’s very impressive AI can do this.” I worked pretty hard to push back against this set of intuitions because they are objectively wrong.  We spent some time interacting with GPT-2, and students were willing to acknowledge that there has been progress since that era (which they grudgingly conceded might not seem like a long time ago to someone as old as me, but note that GPT-2 launched when they were in middle school). Historical context I gave them made this worse.  T