用专家规则+大模型分析课堂对话，提升教学评估效率
Enhanced Classroom Dialogue Sequences Analysis with a Hybrid AI Agent: Merging Expert Rule-Base with Large Language Models
- 融合专家规则与大语言模型，实现对话分类的灵活准确
- 基于30余项研究构建完整对话分析框架，验证结果可靠
- 适合教育研究者与教师发展支持者使用
课堂对话在促进学生参与和深度学习中至关重要。然而，传统对话分析多依赖理论框架或经验描述，二者整合不足。本研究通过综合30余项研究，构建了全面的对话序列规则体系，并开发了一个结合专家知识与大语言模型（LLM）的AI代理。该代理在保留理论依据的同时适应自然语言复杂性，实现对课堂对话序列的精准、灵活分类。经人类专家编码验证，其具备高精度与高可靠性。结果表明，该代理能有效提升课堂对话分析的效率与可扩展性，在改进教学实践和支撑教师专业发展方面具有重要潜力。
原文摘要 · Abstract (English)
Classroom dialogue plays a crucial role in fostering student engagement and deeper learning. However, analysing dialogue sequences has traditionally relied on either theoretical frameworks or empirical descriptions of practice, with limited integration between the two. This study addresses this gap by developing a comprehensive rule base of dialogue sequences and an Artificial Intelligence (AI) agent that combines expert-informed rule-based systems with a large language model (LLM). The agent applies expert knowledge while adapting to the complexities of natural language, enabling accurate and flexible categorisation of classroom dialogue sequences. By synthesising findings from over 30 studies, we established a comprehensive framework for dialogue analysis. The agent was validated against human expert coding, achieving high levels of precision and reliability. The results demonstrate that the agent provides theory-grounded and adaptive functions, tremendously enhancing the efficiency and scalability of classroom dialogue analysis, offering significant potential in improving classroom teaching practices and supporting teacher professional development.
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