Exploring The Impact Of Proactive Generative AI Agent Roles In Time-Sensitive Collaborative Problem-Solving Tasks

2026-02-19

AI Summary

This study addresses the challenges teams face under time pressure in real-time collaboration—particularly in ideation, coordination, and progress tracking—and investigates the yet-unclear mechanisms through which proactive generative AI agents influence collaborative dynamics. Through a digital escape room experiment, the research empirically compares team performance and interaction patterns across three conditions: no AI, peer-like AI (offering suggestions and answering questions), and facilitator-like AI (providing summaries and structural scaffolding). Findings reveal that while peer-like AI offers immediate cues and memory support, it often disrupts workflow, increases cognitive load, and fosters overreliance. In contrast, facilitator-like AI provides lightweight support with minimal interference but yields limited impact on task outcomes. The study thus uncovers the dual-edged effects and underlying mechanisms of different proactive AI agent types in co-located collaborative settings.

Technology Category

• Humans and AI: Human-AI Collaboration / Human-AI Teaming
• Multiagent Systems: Teamwork
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Collaborative problem-solving under time pressure is common but difficult, as teams must generate ideas quickly, coordinate actions, and track progress. Generative AI offers new opportunities to assist, but we know little about how proactive agents affect the dynamics of real-time, co-located teamwork. We studied two forms of proactive support in digital escape rooms: a facilitator agent that offered summaries and group structures, and a peer agent that proposed ideas and answered queries. In a within-subjects study with 24 participants, we compared group performance and processes across three conditions: no AI, peer, and facilitator. Results show that the peer agent occasionally enhanced problem-solving by offering timely hints and memory support; however, it also disrupted flow, increased workload, and created over-reliance. In comparison, the facilitator agent provided light scaffolding but had a limited impact on outcomes. We provide design considerations for proactive generative AI agents based on our findings.

Problem

• collaborative problem-solving
• time pressure
• proactive generative AI
• teamwork dynamics
• human-AI collaboration

Innovation

• proactive generative AI
• collaborative problem-solving
• time-sensitive tasks
• AI agent roles
• human-AI collaboration