Lessons Learned from Integrating Generative AI into an Introductory Undergraduate Astronomy Course at Harvard

Lessons Learned from Integrating Generative AI into an Introductory Undergraduate Astronomy Course at Harvard
Christopher W. Stubbs, Dongpeng Huang, Jungyoon Koh, Madeleine Woods, Andr’es A. Plazas Malag’on
arXiv:2602.04389v1 Announce Type: cross
Abstract: We describe our efforts to fully integrate generative artificial intelligence (GAI) into an introductory undergraduate astronomy course. Ordered by student perception of utility, GAI was used in instructional Python notebooks, in a subset of assignments, for student presentation preparations, and as a participant (in conjunction with a RAG-encoded textbook) in a course Slack channel. Assignments were divided into GAI-encouraged and GAI-discouraged. We incentivized student mastery of the material through midterm and final exams in which electronics were not allowed. Student evaluations of the course showed no reduction compared to the non-GAI version from the previous year.arXiv:2602.04389v1 Announce Type: cross
Abstract: We describe our efforts to fully integrate generative artificial intelligence (GAI) into an introductory undergraduate astronomy course. Ordered by student perception of utility, GAI was used in instructional Python notebooks, in a subset of assignments, for student presentation preparations, and as a participant (in conjunction with a RAG-encoded textbook) in a course Slack channel. Assignments were divided into GAI-encouraged and GAI-discouraged. We incentivized student mastery of the material through midterm and final exams in which electronics were not allowed. Student evaluations of the course showed no reduction compared to the non-GAI version from the previous year.