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

TL;DR

This paper details the integration of generative AI into an undergraduate astronomy course at Harvard, exploring its use in assignments, presentations, and communication, and assessing its impact on student learning and perception.

Contribution

It presents a comprehensive implementation of GAI in a university course and evaluates its effects on student engagement and learning outcomes.

Findings

Student perception of utility was high for GAI tools.

Course evaluations showed no decline in student satisfaction.

GAI was effectively incorporated without harming exam performance.

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.