AI动态生成个性化学习例题，随学习状态实时调整。
ExaCraft: Dynamic Learning Context Adaptation for Personalized Educational Examples
- 根据学习者行为与背景动态生成例题
- 支持五类学习上下文信号的实时适应
- 适合个性化教学与自适应学习系统研究者
学习效果最佳时，内容能与学习者个人经历相关联。然而现有教育AI工具缺乏针对学习者理解变化、困难或技能提升生成或调整例题的能力。我们开发了ExaCraft，一个通过谷歌Gemini AI和Python Flask API（可通过Chrome扩展访问）实现的AI系统，能够基于用户定义的背景信息（如地理位置、教育水平、职业、难度偏好）及实时学习行为分析，生成兼具文化相关性与个体适配性的个性化例题。其核心创新在于可动态适应五类学习上下文：困难指标、掌握模式、主题进展历史、会话边界与学习进展信号。演示将展示例题如何从基础概念逐步演进至高级技术实现，响应主题重复、重生成请求及主题推进模式，在不同使用场景中展现适应能力。
原文摘要 · Abstract (English)
Learning is most effective when it's connected to relevant, relatable examples that resonate with learners on a personal level. However, existing educational AI tools don't focus on generating examples or adapting to learners' changing understanding, struggles, or growing skills. We've developed ExaCraft, an AI system that generates personalized examples by adapting to the learner's dynamic context. Through the Google Gemini AI and Python Flask API, accessible via a Chrome extension, ExaCraft combines user-defined profiles (including location, education, profession, and complexity preferences) with real-time analysis of learner behavior. This ensures examples are both culturally relevant and tailored to individual learning needs. The system's core innovation is its ability to adapt to five key aspects of the learning context: indicators of struggle, mastery patterns, topic progression history, session boundaries, and learning progression signals. Our demonstration will show how ExaCraft's examples evolve from basic concepts to advanced technical implementations, responding to topic repetition, regeneration requests, and topic progression patterns in different use cases.
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