Which AI Tool Best Analyzes Student Feedback? A Guide for Education Researchers

What is it about?

When students provide written feedback about their courses, instructors and researchers need efficient ways to identify common themes. This study compared five artificial intelligence tools (topic models) to see which ones best match how human experts would categorize student comments about faculty, teaching assistants, and peer support. We found that no single AI tool works best for all situations. NMF (Non-Negative Matrix Factorization) performed best overall, but simpler methods like k-means worked well when students used distinct vocabulary. Importantly, we discovered that how researchers set up the comparison matters: when humans code data after seeing AI results (machine-led), performance appears inflated compared to when experts code independently (human-led). This means researchers should not fully automate the analysis of student feedback - human oversight remains essential for accurate, actionable insights.

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Photo by Deng Xiang on Unsplash

Photo by Deng Xiang on Unsplash

Why is it important?

Education researchers increasingly collect open-ended student feedback but lack clear guidance on which AI tools to use for analysis. Choosing the wrong method can lead to misidentified themes, wasted resources, and misguided teaching improvements. This study provides the first systematic comparison of topic modeling techniques specifically for education data, revealing that the widely used machine-led evaluation approach may overestimate AI performance. Our practical recommendations help educators and researchers select appropriate tools while maintaining the human judgment necessary for meaningful interpretation of student voices.

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