Date
Publisher
arXiv
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.
Study at a glance
- Question: How can generative artificial intelligence be integrated into an introductory college astronomy course?
- Setting and data: An introductory undergraduate astronomy course at Harvard; student course evaluations.
- Method: Comparison of student course evaluations between the AI-integrated course and the previous year's non-AI course.
Why it matters
Fully integrating generative artificial intelligence into introductory courses requires understanding how to balance AI-encouraged and AI-discouraged assignments while still incentivizing student mastery of the material.
Key findings
- In an introductory Harvard undergraduate astronomy course, student evaluations showed no reduction after integrating generative AI compared to the previous year.
- Students perceived generative AI as most useful in instructional coding notebooks, followed by course assignments and presentation preparation.
- Students rated generative AI acting as a participant in a course discussion channel as the least useful implementation.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
What is the application?
Who age?
Why use AI?
Study design
