AI-Driven Contribution Evaluation and Conflict Resolution: A Framework & Design for Group Workload Investigation

TL;DR

This paper presents a framework for AI-assisted conflict resolution in team contribution assessments, integrating heterogeneous data sources and LLM analysis to improve fairness and transparency in workload evaluation.

Contribution

It introduces a novel AI-enhanced tool framework that organizes diverse team artefacts and uses LLMs for dispute investigation and conflict detection.

Findings

Framework effectively surfaces conflict markers using inequality measures.

LLM-based analysis provides interpretable and transparent dispute judgments.

Design aligns with current policies and addresses practical challenges.

Abstract

The equitable assessment of individual contribution in teams remains a persistent challenge, where conflict and disparity in workload can result in unfair performance evaluation, often requiring manual intervention - a costly and challenging process. We survey existing tool features and identify a gap in conflict resolution methods and AI integration. To address this, we propose a framework and implementation design for a novel AI-enhanced tool that assists in dispute investigation. The framework organises heterogeneous artefacts - submissions (code, text, media), communications (chat, email), coordination records (meeting logs, tasks), peer assessments, and contextual information - into three dimensions with nine benchmarks: Contribution, Interaction, and Role. Objective measures are normalised, aggregated per dimension, and paired with inequality measures (Gini index) to surface conflict markers. A Large Language Model (LLM) architecture performs validated and contextual analysis over these measures to generate interpretable and transparent advisory judgments. We argue for feasibility under current statutory and institutional policy, and outline practical analytics (sentimental, task fidelity, word/line count, etc.), bias safeguards, limitations, and practical challenges.