Generative AI for Research: Tools and Judgment
This seminar examines how generative AI can be integrated into graduate research in ways that are effective, transparent, and intellectually responsible. Designed as a discussion-centered forum, the course explores practical uses of generative AI for coding, literature synthesis, data analysis, and writing, alongside broader questions about judgment, authorship, skill development, academic integrity, and the role of human expertise. Readings, case studies, and tool demonstrations will support weekly discussion. Readings, case studies, and short tool demonstrations will support weekly discussion. The overall goal is to help students develop a thoughtful personal framework for when and how generative AI should, and should not, be used in their own research practice, while cultivating an adaptable approach that can evolve as AI capabilities, norms, and expectations continue to change.
Topics
- Demonstrate practical fluency with current generative AI tools relevant to research workflows, including coding, writing, analysis, and ideation.
- Evaluate the benefits, limitations, and risks of generative AI outputs, including issues related to accuracy, bias, reproducibility, and overreliance.
- Articulate a personal, defensible approach to responsible AI use in one’s own research program, including around questions of authorship, disclosure, integrity, bias, and cognitive offloading in research settings.
- Reflect on how generative AI may reshape graduate training and research or engineering careers.
Official roster learning outcomes
Sources
- Course catalogue ↗2026-2027