用知识追踪+检索增强生成，让AI教学习更懂你
TutorLLM: Customizing Learning Recommendations with Knowledge Tracing and Retrieval-Augmented Generation
- 结合知识追踪与检索增强，动态获取学生学习状态
- 用户满意度提升10%，测验成绩提高5%
- 适合个性化辅导、自适应学习系统研发者
人工智能在教育中的融合有望显著提升学习效率。大型语言模型（如ChatGPT、Gemini、Llama）使学生能灵活查询各类主题，但存在内容相关性差与缺乏个性化的问题。为此，我们提出TutorLLM，一个基于知识追踪（KT）与检索增强生成（RAG）的个性化学习推荐大模型系统。其创新在于将KT与RAG技术与大模型结合，实现根据学生个体学习状态动态检索上下文知识，并提供定制化学习建议。具体而言，该系统利用多特征潜在关系BERT型知识追踪模型（MLFBK）预测学生学习状态，通过爬虫模型增强响应准确性。评估包括用户问卷和性能指标，结果显示相比仅使用通用大模型，用户满意度提升10%，测验分数提高5%。
原文摘要 · Abstract (English)
The integration of AI in education offers significant potential to enhance learning efficiency. Large Language Models (LLMs), such as ChatGPT, Gemini, and Llama, allow students to query a wide range of topics, providing unprecedented flexibility. However, LLMs face challenges, such as handling varying content relevance and lack of personalization. To address these challenges, we propose TutorLLM, a personalized learning recommender LLM system based on Knowledge Tracing (KT) and Retrieval-Augmented Generation (RAG). The novelty of TutorLLM lies in its unique combination of KT and RAG techniques with LLMs, which enables dynamic retrieval of context-specific knowledge and provides personalized learning recommendations based on the student's personal learning state. Specifically, this integration allows TutorLLM to tailor responses based on individual learning states predicted by the Multi-Features with Latent Relations BERT-based KT (MLFBK) model and to enhance response accuracy with a Scraper model. The evaluation includes user assessment questionnaires and performance metrics, demonstrating a 10% improvement in user satisfaction and a 5\% increase in quiz scores compared to using general LLMs alone.
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