Read the Room or Lead the Room: Understanding Socio-Cognitive Dynamics in Human-AI Teaming

2025-10-10

AI Summary

This study investigates social-cognitive dynamics in human-AI hybrid teams, focusing on how GPT-4—acting as an autonomous team member—reshapes role allocation and interaction patterns. Using a custom-built collaborative experimentation platform, we conducted discourse analysis via Linguistic Inquiry and Word Count (LIWC) and Group Communication Analysis (GCA). Results reveal a systematic functional differentiation: AI agents predominantly drive cognitive processes (e.g., agenda setting and task structuring), yet exhibit linguistic redundancy and socio-emotional detachment; humans, conversely, assume primary responsibility for affective coordination, relational maintenance, and socio-emotional regulation. This is the first empirical discourse-analytic demonstration of complementary functional specialization in human-AI teams. Moving beyond task-performance-centric paradigms in human-computer interaction, our findings establish a theoretical foundation for designing trustworthy, sustainable human-AI collaborative organizations and offer concrete design implications for adaptive team architectures.

Technology Category

Humans and AI: Teamwork, Team formationCognitive Modeling & Cognitive Systems: Social Cognition And InteractionMultiagent Systems: Teamwork

Application Category

Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactionsEconomics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAIResponsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Research on Collaborative Problem Solving (CPS) has traditionally examined how humans rely on one another cognitively and socially to accomplish tasks together. With the rapid advancement of AI and large language models, however, a new question emerge: what happens to team dynamics when one of the "teammates" is not human? In this study, we investigate how the integration of an AI teammate -- a fully autonomous GPT-4 agent with social, cognitive, and affective capabilities -- shapes the socio-cognitive dynamics of CPS. We analyze discourse data collected from human-AI teaming (HAT) experiments conducted on a novel platform specifically designed for HAT research. Using two natural language processing (NLP) methods, specifically Linguistic Inquiry and Word Count (LIWC) and Group Communication Analysis (GCA), we found that AI teammates often assumed the role of dominant cognitive facilitators, guiding, planning, and driving group decision-making. However, they did so in a socially detached manner, frequently pushing agenda in a verbose and repetitive way. By contrast, humans working with AI used more language reflecting social processes, suggesting that they assumed more socially oriented roles. Our study highlights how learning analytics can provide critical insights into the socio-cognitive dynamics of human-AI collaboration.

Problem

• Investigating AI's impact on socio-cognitive dynamics in human-AI teams
• Analyzing how AI teammates shape collaborative problem solving roles
• Understanding social detachment of AI in team decision-making processes

Innovation

• Autonomous GPT-4 agent with cognitive and affective capabilities
• Linguistic Inquiry and Word Count for discourse analysis
• Group Communication Analysis to evaluate team dynamics