用AI同学提问能提升学生专注力，尤其在复杂课程中效果明显。
Examining the Role of LLM-Driven Interactions on Attention and Cognitive Engagement in Virtual Classrooms
- 全由大模型扮演师生的虚拟课堂，测试提问对学习影响
- 学生看课时注意力更集中，尤其在难懂内容上
- 适合设计沉浸式教育VR系统的研究者与开发者
通过将大语言模型（LLMs）与虚拟现实（VR）结合，构建全由大模型驱动的虚拟学习环境，研究师生角色均由大模型扮演时对学生行为的影响。重点考察了由大模型驱动的同伴提问行为对学生注意力、认知负荷和学习成效的作用。结果发现，在同伴提问条件下，学生表现出更聚焦的视觉扫描路径，注意力更多集中在学习内容上，尤其在复杂学科中更为明显。认知负荷与对学习材料的关注度呈强相关，说明提问并未带来额外认知负担。基于此，提出优化虚拟教学空间的设计建议。
原文摘要 · Abstract (English)
Transforming educational technologies through the integration of large language models (LLMs) and virtual reality (VR) offers the potential for immersive and interactive learning experiences. However, the effects of LLMs on user engagement and attention in educational environments remain open questions. In this study, we utilized a fully LLM-driven virtual learning environment, where peers and teachers were LLM-driven, to examine how students behaved in such settings. Specifically, we investigate how peer question-asking behaviors influenced student engagement, attention, cognitive load, and learning outcomes and found that, in conditions where LLM-driven peer learners asked questions, students exhibited more targeted visual scanpaths, with their attention directed toward the learning content, particularly in complex subjects. Our results suggest that peer questions did not introduce extraneous cognitive load directly, as the cognitive load is strongly correlated with increased attention to the learning material. Considering these findings, we provide design recommendations for optimizing VR learning spaces.
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