Breaking Barriers or Building Dependency? Exploring Team-LLM Collaboration in AI-infused Classroom Debate

要旨

Classroom debates are a unique form of collaborative learning characterized by fast-paced, high-intensity interactions that foster critical thinking and teamwork. Despite the recognized importance of debates, the role of AI tools, particularly LLM-based systems, in supporting this dynamic learning environment has been under-explored in HCI. This study addresses this opportunity by investigating the integration of LLM-based AI into real-time classroom debates. Over four weeks, 22 students in a Design History course participated in three rounds of debates with support from ChatGPT. The findings reveal how learners prompted the AI to offer insights, collaboratively processed its outputs, and divided labor in team-AI interactions. The study also surfaces key advantages of AI usage—reducing social anxiety, breaking communication barriers, and providing scaffolding for novices—alongside risks, such as information overload and cognitive dependency, which could limit learners' autonomy. We thereby discuss a set of nuanced implications for future HCI exploration.

著者
Zihan Zhang
Southern University of Science and Technology, Shenzhen, China
Black Sun
Aarhus University, Aarhus, Denmark
Pengcheng An
Southern University of Science and Technology, Shenzhen, China
DOI

10.1145/3706598.3713853

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713853

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Classroom Technology

G302
7 件の発表
2025-04-29 23:10:00
2025-04-30 00:40:00
日本語まとめ
読み込み中…