分析学生向AI提问的类型及其变化，揭示学习中的互动模式。
Analysis of Types of Inquiries in Student-AI Interaction: A case study of two CS2 tasks
- 用18类问题框架分类学生与AI的交互
- 830次交互中少数问题类型占主导
- 问题类型随任务进展显著变化
背景与情境：提问是知识获取与学习的核心部分。尽管如此，学生在课堂中提问却往往不足。然而，研究显示学生在学习和解决问题时会大量与生成式AI系统互动。本文旨在更深入理解学生向AI提出的问题类型，以及这些问题在解题过程和不同任务间的演变。方法：我们采用Graesser等人的分类体系，将学生提问划分为18种类型，并开发了一种少样本学习方法，自动将学生与AI的交互归类到这些类别中。利用该系统分析了830次来自两个编程任务的CS2学生互动数据。结果表明，少数几类问题占据了学生提问的绝大部分，且随着任务推进，学生提出的问题类型发生显著变化。
原文摘要 · Abstract (English)
Background and Context: Question and inquiry are integral parts of knowledge seeking and learning. Despite their importance, students tend not to ask enough questions in the classroom. However, studies have shown that students interact extensively with generative AI systems for learning and problem solving. Objective: In this paper, we seek to better understand the types of questions that students ask AI systems, and how those questions evolve during problem solving and across tasks. Method: We use the Graesser et al. taxonomy to classify students' inquiries into 18 types. We develop a few-shot learning approach to automatically classify students' interactions with AI into these categories. We use this system to analyze 830 interactions of CS2 students across two programming tasks. Findings: Our results suggest that a small subset of question types accounts for the majority of student inquiries, and that the types of questions students ask change substantially as the task progresses.
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