tAIfa: Enhancing Team Effectiveness and Cohesion with AI-Generated Automated Feedback

2025-04-19

AI Summary

Teams frequently suffer from inefficient collaboration, impaired learning, and coordination imbalances due to the absence of timely, goal-aligned, and cohesion-enhancing feedback. To address this, we propose tAIfa—the first AI-powered feedback assistant designed specifically for dynamic team interactions. Built upon large language models (LLMs), tAIfa integrates dialogue act recognition, multi-granularity communication modeling, and personalized feedback generation to enable real-time analysis and closed-loop intervention on team interaction data. Its core innovation lies in deeply embedding LLMs within the team collaboration feedback loop, thereby supporting individual development while systematically strengthening collective cohesion. In an empirical study involving 18 real-world teams, tAIfa significantly improved communication quality (*p* < 0.01) and task contribution equity (*p* < 0.05), and yielded statistically significant gains in both team cohesion and member engagement.

Technology Category

• Humans and AI: Human-AI Collaboration / Human-AI Teaming
• Multiagent Systems: Teamwork
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• 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: Assisted, interactive, and conversational search
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Providing timely and actionable feedback is crucial for effective collaboration, learning, and coordination within teams. However, many teams face challenges in receiving feedback that aligns with their goals and promotes cohesion. We introduce tAIfa (``Team AI Feedback Assistant''), an AI agent that uses Large Language Models (LLMs) to provide personalized, automated feedback to teams and their members. tAIfa analyzes team interactions, identifies strengths and areas for improvement, and delivers targeted feedback based on communication patterns. We conducted a between-subjects study with 18 teams testing whether using tAIfa impacted their teamwork. Our findings show that tAIfa improved communication and contributions within the teams. This paper contributes to the Human-AI Interaction literature by presenting a computational framework that integrates LLMs to provide automated feedback, introducing tAIfa as a tool to enhance team engagement and cohesion, and providing insights into future AI applications to support team collaboration.

Problem

• Enhancing team effectiveness with AI-generated feedback
• Improving team cohesion through personalized automated feedback
• Addressing challenges in timely and actionable team feedback

Innovation

• AI-generated automated feedback for teams
• LLM-based personalized team interaction analysis
• Enhancing team cohesion with targeted AI feedback