AI RESEARCH
Detecting Alarming Student Verbal Responses using Text and Audio Classifier
arXiv CS.CL
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ArXi:2604.16717v1 Announce Type: new This paper addresses a critical safety gap in the use Automated Verbal Response Scoring (AVRS). We present a novel hybrid framework for troubled student detection that combines a text classifier, trained to detect responses based on their content, and an audio classifier, trained to detect responses using prosodic markers. This approach overcomes key limitations of traditional AVRS systems by considering both content and prosody of responses, achieving enhanced performance in identifying potentially concerning responses.