student-safety

This paper presents a hybrid framework for detecting alarming or distressed student verbal responses by combining a text classifier (content-based) and an audio classifier (prosodic features), aimed at expediting human review in Automated Verbal Response Scoring systems. The approach addresses a safety gap in automated scoring pipelines where at-risk student responses may otherwise go unnoticed.