This paper explores how AI can detect hidden student misconceptions, even when they provide a correct answer, and proposes a system for targeted feedback and teacher alerts.

In plain terms

Students sometimes get the right answer in math but use flawed reasoning. Traditional automated feedback systems often miss these 'hidden misconceptions' because they only check for answer correctness. The researchers investigated how well AI could spot these issues using over 20,000 real student responses. They found that standard machine learning classifiers were only moderately effective, while more advanced 'open-weight reasoning models' (similar to large language models) were better but often produced many false alarms. To address this, they propose a 'detect-verify-escalate pipeline': the system first flags potential misconceptions, then uses follow-up questions to confirm, and only involves a teacher if necessary. This approach could improve both autonomous AI tutors and teacher dashboards by providing more pedagogically sound feedback.