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

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arxiv 2504.14222 v1 pith:FS4LDRVH submitted 2025-04-19 cs.HC

classification cs.HC
keywords feedbacktaifateamteamsautomatedcohesioncollaborationcommunication
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Call for Collaborative Intelligence: Why Human-Agent Systems Should Precede AI Autonomy

    cs.AI 2025-06 conditional novelty 4.0 of 10

    A position paper arguing that LLM-based human-agent systems, not fully autonomous agents, should be the immediate goal for AI development.

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