AI for Analyzing Student Course Evaluations

Course evaluations often combine rating scales with open-ended comments. Text analysis can organize comments by topics such as pacing, workload, clarity, or classroom climate, allowing instructors to review large sets more efficiently. A language model may summarize themes, but it can merge distinct concerns, miss sarcasm, overstate a minority view, or assign sentiment based on wording rather than context. Course evaluations also have limitations as evidence: response rates vary, comments may reflect a particular assessment or expectation, and students do not all interpret rating scales similarly. Bias can affect who responds and how instructors are perceived. AI summaries can amplify these patterns if they present a handful of comments as representative. Reviewers should examine original comments, quantify how many responses support a theme, compare with enrollment and response rates, and avoid attributing a theme to an individual when responses should be confidential. Reports should describe uncertainty and separate student observations from an evaluator’s conclusions. Institutions should protect student data and apply local rules for access and retention. Course evaluations are one source of feedback, not a standalone measure of instructor effectiveness. Instructors can combine them with peer observation, learning evidence, and reflective notes. AI may assist with organization, but decision-makers need context and fair processes before using summaries for employment or promotion decisions. Include multiple forms of evidence before drawing conclusions.