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.

--
Citations
Research
Paper Only

Study specs

Between-subjects study where team interactions were analyzed by an AI agent (tAIfa) to deliver feedback on strengths and areas for improvement.

Sample Size
N=18
Study Type
Experimental Study
Year
2025
Human Data Platform
Prolific

Measured Outcomes

Team communication, contributions, and cohesion with and without tAIfa's feedback.

Peer Review & Critical Discussion

3 threads

Potential Selection Bias in 2023 Cohort

DSJDr. Sarah J.
Verified PhD Candidate
12 replies

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.

2 hours ago

Non-naive Participants Issue

MCM. Chen (OpenAI)
Data Scientist
8 replies

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.

5 hours ago

RLHF Applicability to This Study Design

PRWProf. R. Williams
Verified Researcher
15 replies

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.

1 day ago

Verify your expertise to join discussion

Create an account and verify your credentials to participate in peer discussions.