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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.22237 |
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| _version_ | 1866910162005000192 |
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| author | Fan, Zhilin Wang, Deliang Chen, Penghe Lu, Yu |
| author_facet | Fan, Zhilin Wang, Deliang Chen, Penghe Lu, Yu |
| contents | 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 support 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 LLM. The system uses a hierarchical attribution method based on explainable AI (xAI) to identify dialogue evidence for each recommendation and generate a natural-language explanation based on that evidence. In technical evaluation, the method outperformed baseline approaches in identifying supporting evidence. In a preliminary user study with 22 pre-service teachers, participants who received explanations reported higher trust in the system. These findings suggest a promising direction for improving LLM explainability in educational dialogue systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_22237 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Tell Me Why: Designing an Explainable LLM-based Dialogue System for Student Problem Behavior Diagnosis Fan, Zhilin Wang, Deliang Chen, Penghe Lu, Yu Computation and Language Artificial Intelligence 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 support 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 LLM. The system uses a hierarchical attribution method based on explainable AI (xAI) to identify dialogue evidence for each recommendation and generate a natural-language explanation based on that evidence. In technical evaluation, the method outperformed baseline approaches in identifying supporting evidence. In a preliminary user study with 22 pre-service teachers, participants who received explanations reported higher trust in the system. These findings suggest a promising direction for improving LLM explainability in educational dialogue systems. |
| title | Tell Me Why: Designing an Explainable LLM-based Dialogue System for Student Problem Behavior Diagnosis |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2604.22237 |