用知识图谱动态调整ChatGPT学习指导，分层反馈更精准
How Good is ChatGPT in Giving Adaptive Guidance Using Knowledge Graphs in E-Learning Environments?
- 基于学生交互记录动态构建学习上下文，注入知识图谱信息
- 根据掌握程度分三档：优则进阶、中则复习、差则补基础
- 适合教育AI研究者与智能辅导系统开发者参考
在线学习环境正越来越多地利用GPT-3.5和GPT-4等大语言模型提供个性化教育支持。本研究提出一种将动态知识图谱与大语言模型结合的方法，通过分析学生过往及当前互动，识别并附加最相关的学习上下文至提示词。知识图谱用于评估学生对先修知识点的掌握情况，依据分类结果（良好、一般或较差），模型相应提供进阶指导、基础复习或深入前提解释。初步结果显示，该分层支持可提升学生理解力与任务完成度。然而也发现大语言模型可能产生错误，存在误导风险，需人工干预以降低影响。本研究旨在推进人工智能驱动的个性化学习，同时揭示其局限性与潜在问题，为未来技术与数据驱动教育研究提供方向。
原文摘要 · Abstract (English)
E-learning environments are increasingly harnessing large language models (LLMs) like GPT-3.5 and GPT-4 for tailored educational support. This study introduces an approach that integrates dynamic knowledge graphs with LLMs to offer nuanced student assistance. By evaluating past and ongoing student interactions, the system identifies and appends the most salient learning context to prompts directed at the LLM. Central to this method is the knowledge graph's role in assessing a student's comprehension of topic prerequisites. Depending on the categorized understanding (good, average, or poor), the LLM adjusts its guidance, offering advanced assistance, foundational reviews, or in-depth prerequisite explanations, respectively. Preliminary findings suggest students could benefit from this tiered support, achieving enhanced comprehension and improved task outcomes. However, several issues related to potential errors arising from LLMs were identified, which can potentially mislead students. This highlights the need for human intervention to mitigate these risks. This research aims to advance AI-driven personalized learning while acknowledging the limitations and potential pitfalls, thus guiding future research in technology and data-driven education.
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