发表机构
Georgia Institute of Technology（佐治亚理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出基于提示工程的框架，通过六个学习者维度和布鲁姆教育目标分类法，在无需重新训练LLM的情况下实现通用AI助教的个性化，经实验验证可产生自适应响应。
AI 中文摘要
由大语言模型（LLM）驱动的人工智能（AI）助教提供可扩展的教育支持，但往往个性化程度有限。本研究提出一种基于提示工程的框架，用于在通用的基于LLM/RAG的AI助教（如Jill Watson）中实现跨学科和课程的个性化。该框架通过六个学习者特定维度调整响应：自我评估、抽象偏好、冗长偏好、感知定向、信息处理风格和理解水平，产生96种不同的学习者画像。学生查询还通过布鲁姆教育目标分类法（Bloom's Taxonomy）分析，以评估交互层面的认知复杂性。学习者属性和认知评估被编码为结构化提示，用于条件化LLM，无需模型重新训练。该框架通过使用NLP指标的实验和包含5名参与者的人类研究进行评估。结果显示，在不同个性化条件下，响应风格和结构存在感知差异，统计分析确定了与可测量响应变化相关的学习者属性。这些发现提供了初步证据，表明基于提示的个性化可支持LLM驱动的教育智能体的自适应行为。
英文摘要
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.
Comments7 pages, 9 figures, IAAI27 conference
