提示工程实现AI助教Jill Watson的实时个性化
原标题：A Prompt-Engineering Approach to Develop Scalable, Flexible, and Real-Time Hybrid Micro-Level Personalization in a General Purpose AI Teaching Assistant
AI 摘要
佐治亚理工学院的研究人员提出了一种基于提示工程的框架，用于个性化通用LLM/RAG驱动的AI助教Jill Watson。该框架结合六种学习者维度和布鲁姆分类法，生成96种学习者画像，在不重新训练模型的情况下实现实时响应个性化。实验和人工评估表明，基于提示的个性化能产生可感知的响应差异，为LLM教育代理的适应性行为提供了初步证据。
正文节选
A Prompt-Engineering Approach to Develop Scalable, Flexible, and Real-Time Hybrid Micro-Level Personalization in a General Purpose AI Teaching Assistant. Abstract 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 assistant such as Jill Watson across academic discipl