arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2407.17429cs.CYcs.AI

学生在不同教育场景中如何与基于LLM的虚拟教学助手交互?

How Do Students Interact with an LLM-powered Virtual Teaching Assistant in Different Educational Settings?

Georgia Institute of Technology(佐治亚理工学院)

Pratyusha Maiti, Ashok K. Goel

AI总结:

本文分析了学生与LLM驱动的虚拟教学助手Jill Watson在多课程中的交互,发现其支持广泛认知需求并鼓励高阶认知提问,但使用频率受课程环境影响,为未来AI教育工具开发提供了启示。

摘要:

Jill Watson是一款由LLM驱动的虚拟教学助手,能够回答学生问题,并基于教师提供的课件与学生们展开延伸对话。本文分析了多门课程和学院中学生与Jill的交互情况,基于Bloom修订版分类法重点关注学生问题的类型与复杂度,以及工具使用模式。研究发现,通过支持广泛的认知需求,Jill鼓励学生提出复杂的高阶认知问题。然而,不同部署环境下的使用频率存在显著差异,且提问类型取决于课程特定情境。这些发现为未来开发适应个人学习风格和课程结构的AI驱动教育工具铺平了道路,有望提升课堂的教学与学习体验。

英文摘要:

Jill Watson, a virtual teaching assistant powered by LLMs, answers student questions and engages them in extended conversations on courseware provided by the instructors. In this paper, we analyze student interactions with Jill across multiple courses and colleges, focusing on the types and complexity of student questions based on Bloom's Revised Taxonomy and tool usage patterns. We find that, by supporting a wide range of cognitive demands, Jill encourages students to engage in sophisticated, higher-order cognitive questions. However, the frequency of usage varies significantly across deployments, and the types of questions asked depend on course-specific contexts. These findings pave the way for future work on AI-driven educational tools tailored to individual learning styles and course structure, potentially enhancing both the teaching and learning experience in classrooms.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑