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

TL;DR

A framework and implementation design for a novel AI-enhanced tool that assists in dispute investigation and argues 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.

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.

Figures (8)

• Figure 1: Bar graph of feature adoption in contribution assessment tools.
• Figure 2: High-level architecture overview.
• Figure 3: Deriving abstract metrics via LLMs.
• Figure 4: Compiling metrics into base measures.
• Figure 5: Base measures with semantic and hypothetical embedding.