用提示工程实现AI助教实时个性化，不需重训练
A Prompt-Engineering Approach to Develop Scalable, Flexible, and Real-Time Hybrid Micro-Level Personalization in a General Purpose AI Teaching Assistant
- 通过六维学习者特征构建96种个性化配置
- 响应风格随学习者属性变化，人类评估认可差异
- 无需重训练，适配多学科、实时交互场景
基于大语言模型（LLM）的AI教学助理虽具可扩展性，但个性化不足。本研究提出一种提示工程框架，用于通用型LLM/RAG教学助理（如Jill Watson）的微调个性化，支持跨学科与课程应用。框架根据六类学习者特征——自我评估、抽象偏好、冗长偏好、感知倾向、信息处理方式及理解层级——生成96种学习者画像，并结合布卢姆认知分类法分析学生提问的认知复杂度。学习者属性与认知评估以结构化提示形式嵌入，不需模型重训练即可动态调整输出。通过自然语言指标与5人用户研究验证，结果显示不同个性化条件下响应风格与结构存在感知差异，统计分析确认学习者特征与响应变化显著相关。结果初步表明，提示工程可有效支持LLM教育代理的自适应行为。
原文摘要 · Abstract (English)
Artificial intelligence (AI) teaching assistants powered by large language models (LLMs) offer scalable educational support but often provide limited personalization. This study presents a prompt-engineering-based framework for personalizing general-purpose LLM/RAG-based AI teaching assistants such as Jill Watson across academic disciplines and courses. The framework adapts responses using six learner-specific dimensions: self-assessment, abstraction preference, verbosity preference, perceptual orientation, information processing style, and level of understanding, yielding 96 distinct learner profiles. Student queries are additionally analyzed using Bloom's Taxonomy to estimate cognitive complexity at the interaction level. Learner attributes and cognitive assessments are encoded in structured prompts that condition the LLM without requiring model retraining. The framework is evaluated through experiments using NLP metrics and a human study with five participants. Results show perceived differences in response style and structure across personalization conditions, with statistical analyses identifying learner attributes associated with measurable response changes. These findings provide preliminary evidence that prompt-based personalization can support adaptive behavior in LLM-powered educational agents.
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