Three courses across two levels — from a first-year prompt engineering seminar to a graduate research course on AI for software engineering.
A first-year seminar, a hands-on GenAI course, and a graduate research seminar.
A first-year seminar that introduces students to computing through prompt engineering — using natural language to direct advanced AI systems like ChatGPT, Copilot, and DALL-E. Emphasis on creativity, accessibility, and critical reflection on AI ethics, bias, and "tech for good."
Foundational and technical skills for developing and applying Deep Learning — especially Generative AI methods — to enhance software development tasks like code generation and documentation. Graduate students additionally learn to critically evaluate research and propose novel solutions.
A graduate course on how recent advances in AI lead to innovative automated practices in software engineering. Participants investigate AI techniques for SE process automation, study transformative impacts on the software lifecycle, and are introduced to research at the AI×SE intersection.
Every concept lands in working code or a working prompt before we abstract it. No live coding spectacles; every exercise has a runnable artifact you keep.
Coursework looks like research. You submit PRs, write evaluations, defend choices — the same loop you’d use shipping a paper or a release.
A ladder across the curriculum: a first-year seminar introduces critical AI literacy; an upper-division course teaches the engineering; a graduate seminar pulls students into active research at the AI×SE intersection.
