AI across the curriculum

The course. A critical essay-writing course built around found families: forms of kinship that go beyond biological relations, read through queer literature and culture, neurodiversity, feminism, and migration. Students write and revise analytical essays, work with critical sources, and respond to one another’s drafts.

AI in the course. The class opens by negotiating its own agreement on AI use in writing, with one fixed condition: nothing that circumvents the critical writing skills the course exists to practice. Students close-read an AI-generated critical essay beside a human-written essay on the same text, as a lesson in the limits of AI’s critical thinking. If the class agrees, students also ask a language model for counterarguments to their own drafts, then assess how good the critique is.

The course. How did American conservatism get from Edmund Burke to Steve Bannon? The course follows classical conservatism and classical liberalism through four traditions in American conservative thought: libertarianism, neo-conservatism, white supremacism, and evangelical Christianity. Students read dense theoretical texts and apply them to current events.

AI in the course. Students examine how political campaigns, particularly on the American right, use AI to build models that target finely grained demographic groups, and they consider whether disinformation or deception is part of those strategies.

The course. An advanced calculus course, centered on infinite series, taught through Euler’s original papers. Euler’s collected works run to 81 volumes and more than 35,000 pages, most of them never translated out of Latin. Students can take the course at an intermediate or advanced level.

AI in the course. Students use AI to find their way around a body of work too large to read and poorly served by keyword search. They ask which papers take up a given topic, such as natural logarithms, and what the main result of each one is. The course also leaves room for questions about Euler’s works that nobody yet knows how to ask.

The course. An introduction to AI for students with no programming background, taken three ways: technically, critically, and creatively. The course runs from the origins of AI in the 1950s, through symbolic AI and neural networks, to the large models available today.

AI in the course. AI is the whole subject. Students learn what is happening when a language model generates text or an image model turns a prompt into a picture. They argue over ownership, bias, and regulation from concrete cases. They also practice prompting language and image models, learning where the tools fail and how to push them toward results that are their own.

The course. An advanced biology course on how hormones shape behavior and how behavior, in turn, shapes hormones, drawing on physiological, neural, and evolutionary perspectives. Students work with primary scientific literature, laboratory experiments, and data interpretation.

AI in the course. Students study what happens to scientific knowledge as it passes through AI systems. In one assignment they summarize textbook material for a lay audience, compare their version with an AI-generated summary, and analyze what each simplified or abstracted away. In another, they give an AI the primary literature behind a textbook chapter, have it write explanations for middle-school, undergraduate, and professional readers, and examine how the science is reconstructed for each.

The course. An interdisciplinary course on AI ethics that needs no background in philosophy or computer science, only comfort with elementary algebra. Students approach AI from computer science, where they learn about neural networks and deep learning, and from philosophy, where the question is how one ought to act. Readings run from Turing and Searle to Shannon Vallor.

AI in the course. AI is the whole subject. Students build a neural network with backpropagation from first principles, without libraries, so that the ethical arguments rest on an understanding of how AI works. They apply ethical theories to emerging technologies and discuss the alignment problem. Students may use AI for the weekly article summaries and reading précis, with full citation. The examinations are written in class.

The course. An advanced computer science course on the core techniques of AI, for students who already program. Students work through search algorithms, evolutionary algorithms, swarm intelligence, and machine learning, and implement each in Racket.

AI in the course. AI is the whole subject, but generative tools stay out of the assignments unless explicitly allowed. Students design, implement, and evaluate basic AI systems themselves, and compare paradigms to decide which approach suits which problem. The premise is that AI can generate code, but it cannot decide which problems are worth solving or judge whether a solution is effective, ethical, or just.