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

TL;DR

This study explores how integrating LLM-based AI like ChatGPT into classroom debates influences student collaboration, highlighting benefits such as reduced anxiety and scaffolding, as well as risks like dependency and overload.

Contribution

It provides empirical insights into real-time team-AI interactions in classroom debates and discusses implications for HCI design and educational practices.

Findings

AI reduces social anxiety and communication barriers.

Team-AI collaboration enhances critical thinking and teamwork.

Risks include cognitive overload and dependency.

Abstract

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.