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

2025-01-15

AI Summary

This study investigates how large language models (LLMs), such as ChatGPT, influence student learning outcomes, collaborative patterns, and independent thinking in university-level design history classroom debates. Addressing the dual-edged nature of AI integration—offering cognitive scaffolding while risking information overload and diminished critical reasoning—the research conducts three iterative empirical debate sessions. Methodologically, it employs multimodal data collection: video ethnography, interaction log analysis, semi-structured interviews, and reflective group discussions. Findings reveal distinct cognitive division-of-labor patterns in real-time human–AI collaboration and introduce the novel construct of “collaborative dependence” to reconcile AI’s pedagogical affordances (e.g., anxiety reduction, scaffolding) with its risks. The study identifies six prototypical team–AI collaboration strategies and distills three evidence-based human–computer interaction (HCI) design principles for AI-augmented classroom discourse, thereby contributing both a theoretical framework and actionable guidelines for responsible educational AI deployment.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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.

Problem

• AI Assistants in Education
• Debate-based Learning
• Impact on Cognitive Skills

Innovation

• AI in Classroom Debates
• Educational Robotics
• Impact on Learning