Leveraging Large Language Models for Actionable Course Evaluation Student Feedback to Lecturers

TL;DR

This paper investigates using open-source generative AI to synthesize concise, factual, and actionable summaries from large volumes of student feedback, aiming to improve teaching practices efficiently.

Contribution

It demonstrates the feasibility of leveraging large language models to generate insightful and actionable course evaluation summaries from extensive student responses.

Findings

AI-generated summaries are accurate and actionable

The approach reduces manual effort in feedback analysis

Potential to enhance teaching quality through AI-assisted feedback

Abstract

End of semester student evaluations of teaching are the dominant mechanism for providing feedback to academics on their teaching practice. For large classes, however, the volume of feedback makes these tools impractical for this purpose. This paper explores the use of open-source generative AI to synthesise factual, actionable and appropriate summaries of student feedback from these survey responses. In our setup, we have 742 student responses ranging over 75 courses in a Computer Science department. For each course, we synthesise a summary of the course evaluations and actionable items for the instructor. Our results reveal a promising avenue for enhancing teaching practices in the classroom setting. Our contribution lies in demonstrating the feasibility of using generative AI to produce insightful feedback for teachers, thus providing a cost-effective means to support educators' development. Overall, our work highlights the possibility of using generative AI to produce factual, actionable, and appropriate feedback for teachers in the classroom setting.