学生-AI交互中查询类型的分析：两项CS2任务的案例研究
Analysis of Types of Inquiries in Student-AI Interaction: A case study of two CS2 tasks
- University of Houston（休斯顿大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文以两项CS2任务为案例，采用少样本学习方法结合Graesser等人的分类法，分析830次学生-AI互动，发现少数问题类型占比高且问题类型随任务推进显著变化。
AI中文摘要：
背景与情境：问题与查询是知识获取和学习的核心组成部分，尽管其重要性突出，但学生在课堂上往往提问不足。然而，研究表明，学生会与生成式AI系统进行大量互动以开展学习和解决问题。目标：本文旨在更好地理解学生向AI系统提出的问题类型，以及这些问题在解决问题过程中和不同任务间的演变情况。方法：我们采用Graesser等人的分类法将学生查询划分为18种类型，开发了一种少样本学习方法，用于自动将学生与AI的互动分类至这些类别，并使用该系统分析了CS2学生在两项编程任务中的830次互动。结果：我们的研究结果表明，少数问题类型占学生查询的大部分，且学生提出的问题类型会随任务推进发生显著变化。
英文摘要：
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.
