Tell Me Why: Designing an Explainable LLM-based Dialogue System for Student Problem Behavior Diagnosis

ArXi:2604.22237v1 Announce Type: cross Diagnosing student problem behaviors requires teachers to synthesize multifaceted information, identify behavioral categories, and plan intervention strategies. Although fine-tuned large language models (LLMs) can this process through multi-turn dialogue, they rarely explain why a strategy is recommended, limiting transparency and teachers' trust. To address this issue, we present an explainable dialogue system built on a fine-tuned