tAlfa: Enhancing Team Effectiveness and Cohesion with AI-Generated Automated Feedback
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.
Study specs
Between-subjects study where team interactions were analyzed by an AI agent (tAIfa) to deliver feedback on strengths and areas for improvement.
- Institution
- University of Notre Dame
- Discipline
- Human-Computer Interaction
- Sample Size
- N=18
- Study Type
- Experimental Study
- Year
- 2025
- Human Data Platform
- Prolific
- Source
- View Source Google Scholar
Measured Outcomes
Team communication, contributions, and cohesion with and without tAIfa's feedback.
Peer Review & Critical Discussion
Potential Selection Bias in 2023 Cohort
The participant pool shows a concerning overrepresentation of users from high-income demographics. Looking at Table 3, we can see that 78% of respondents had annual incomes above $75k, which significantly limits the generalizability of these findings to broader populations.
Non-naive Participants Issue
I've noticed a methodological concern regarding participant naivety. Given that Prolific users often complete multiple studies, there's a real risk that participants had prior exposure to similar experimental paradigms, which could confound the results.
RLHF Applicability to This Study Design
The implications for RLHF training pipelines are understated. If we accept the authors' conclusions about preference stability, this has direct consequences for how we should structure reward model training. The temporal decay effect described in Section 4.2 is particularly relevant.
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