Making Sense of Student Feedback in Large Classes: A Practical Guide Using AI-Based Text Analysis

In large-enrollment courses, instructors often receive hundreds of student comments that are difficult to synthesize, and traditional Likert-scale evaluations rarely capture the nuances of students’ learning experiences. This study introduces an accessible, evidence-based approach that uses open-source artificial-intelligence text analysis to interpret qualitative feedback quickly and systematically. Using 678 comments from 270 students in lecture and active-learning sections of an undergraduate course, the analysis identified clear contrasts in how students described engagement, learning, and enthusiasm - differences that were not visible in the numerical ratings. The AI results extended the insights from standard evaluations, highlighting aspects of classroom climate and instructional design that instructors could address directly. Because the procedure runs on common computers with freely available software, it enables faculty to examine large sets of comments in minutes rather than hours. This paper offers a practical guide for applying this approach to course evaluations, helping instructors turn extensive qualitative feedback into concise summaries for teaching improvement.