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

Researchers propose an AI-enhanced framework for evaluating individual contributions and resolving disputes in team environments by analyzing submissions, communications, and coordination records. The system uses LLMs to generate transparent advisory judgments based on normalized metrics across Contribution, Interaction, and Role dimensions, addressing a persistent gap in fair workload assessment.

This research tackles a fundamental organizational challenge: determining who did what work in collaborative settings. The framework's innovation lies in its systematic integration of heterogeneous data sources—code repositories, chat logs, email, meeting records, and peer assessments—into a unified evaluation system. Rather than relying on subjective judgment or manual audits, the approach normalizes objective measures and applies inequality metrics like the Gini index to surface conflict markers, making disputes more transparent and quantifiable.

The broader context reflects growing organizational pain points. As remote and hybrid work proliferates, teams struggle with visibility into individual contributions, leading to unfair performance reviews and team friction. Existing tools lack sophisticated conflict resolution mechanisms, forcing expensive human intervention. This framework fills that gap by automating preliminary analysis and providing data-driven advisory judgments through LLM analysis.

For enterprise software vendors and HR tech companies, this represents a significant market opportunity. Organizations increasingly seek tools that reduce bias in performance evaluation while maintaining fairness and transparency. The research's emphasis on bias safeguards and institutional policy compliance suggests commercial viability within existing governance frameworks.

The practical implications extend beyond dispute resolution into performance management, team dynamics analysis, and workload optimization. However, the framework's effectiveness depends on data availability and quality across organizations with varying documentation practices. Future adoption hinges on demonstrating reliability in real-world scenarios and addressing concerns about algorithmic fairness in employment decisions.

• →AI-driven framework systematically evaluates individual contributions across Contribution, Interaction, and Role dimensions to identify workload disparities objectively.
• →LLM architecture provides interpretable advisory judgments while maintaining transparency, reducing reliance on costly manual dispute investigation.
• →Integration of heterogeneous data sources—code, communications, tasks, and peer assessments—creates comprehensive contribution profiles.
• →Inequality measures like Gini index surface conflict markers and quantify workload imbalance across teams.
• →Framework designed with bias safeguards and institutional policy compliance to ensure practical organizational adoption.