用定制提示和领域知识提升编程辅导系统个性化能力
Enhancing tutoring systems by leveraging tailored promptings and domain knowledge with Large Language Models
- 通过RAG增强提示工程，让大模型生成与技能水平匹配的反馈
- 在三个不同复杂度任务中实现学生分层与实时上下文反馈
- 适合需要自适应编程教学的教育科技开发者参考
人工智能与机器学习的发展重新激发了对计算机辅助学习（CBL）影响的关注。以ChatGPT和智能辅导系统（ITS）为代表的AI工具通过个性化与灵活性提升了学习体验。ITS可依据学生表现、认知状态和学习路径提供定制化反馈。然而，仍面临适配多样学习风格及实现实时上下文感知反馈的挑战。本研究通过将技能对齐反馈引入大语言模型（LLM）的提示工程，并结合检索增强生成（RAG），开发了一套个性化编程辅导系统。小规模试点研究采用可读性评分、响应时间和反馈深度三项量化指标，在三个不同复杂度的编程任务上评估系统性能。结果表明，系统能有效将模拟学生分为三个技能层级，并提供上下文感知反馈，相比通用方法展现出更高有效性和适应性。
原文摘要 · Abstract (English)
Recent advancements in artificial intelligence (AI) and machine learning have reignited interest in their impact on Computer-based Learning (CBL). AI-driven tools like ChatGPT and Intelligent Tutoring Systems (ITS) have enhanced learning experiences through personalisation and flexibility. ITSs can adapt to individual learning needs and provide customised feedback based on a student's performance, cognitive state, and learning path. Despite these advances, challenges remain in accommodating diverse learning styles and delivering real-time, context-aware feedback. Our research aims to address these gaps by integrating skill-aligned feedback via Retrieval Augmented Generation (RAG) into prompt engineering for Large Language Models (LLMs) and developing an application to enhance learning through personalised tutoring in a computer science programming context. The pilot study evaluated a proposed system using three quantitative metrics: readability score, response time, and feedback depth, across three programming tasks of varying complexity. The system successfully sorted simulated students into three skill-level categories and provided context-aware feedback. This targeted approach demonstrated better effectiveness and adaptability compared to general methods.
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