AI-Driven Contribution Evaluation and Conflict Resolution: A Framework & Design for Group Workload Investigation
This addresses workload disparity and unfair evaluations in group work, offering a tool for automated dispute investigation, though it appears incremental as it builds on existing methods with AI integration.
The paper tackles the challenge of equitably assessing individual contributions in team settings by proposing an AI-enhanced framework that organizes diverse artifacts into three dimensions and uses LLMs to generate interpretable advisory judgments for conflict resolution.
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.