A Durability and Cross-Language Transfer Benchmark for a Validated Teaching-Feedback Classification Protocol
Can old teaching-feedback systems still work with today's AI?
A validated system for automatically sorting teacher feedback comments by topic and sentiment, built in 2019, still works well in 2026 even with newer AI models. Surprisingly, the newest frontier models don't improve sentiment classification over much cheaper alternatives, and the system transfers successfully to English feedback, suggesting the original protocol is robust across time and languages.
Universities accumulate massive amounts of teaching evaluations that go unread because processing them by hand is impractical. This work shows institutions can deploy automated feedback classification that stays reliable as AI tools improve and as their campuses grow multilingual—without needing to redesign or retrain expensive systems. The finding that cheaper models perform as well as frontier models on this task could cut operational costs significantly.