Towards Effective Multidisciplinary Health and HCI Teams based on AI Framework

2025-06-05

AI Summary

To address the challenges of interdisciplinary team formation and low collaborative efficacy in health-focused Human-Computer Interaction (HCI) research, this paper proposes an AI-driven, interpretable team composition method. Grounded in socio-technical systems theory, the approach innovatively integrates an inclusivity-first principle with quantitative modeling of members’ domain familiarity, establishing a multi-objective optimization framework that jointly incorporates inclusivity metrics and actionable, human-controllable recommendations. Compared to conventional heuristic team assembly, our system significantly improves diversity balance and cross-domain collaboration performance, enabling dynamic coordination among participants from education, academia, and healthcare backgrounds. The core contribution lies in unifying inclusivity modeling and familiarity quantification within an interpretable AI team recommendation paradigm—marking a paradigm shift from opaque “black-box matching” to human-centered, cognitively transparent, and intervention-aware team formation.

Technology Category

• Humans and AI: Teamwork, Team formation
• Multiagent Systems: Teamwork
• Application Domains: Humanities & Computational Social Science

Application Category

• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

As a Ph.D. student with a diverse background in both public and private sectors, I have encountered numerous challenges in cross-disciplinary and multi-stakeholder team projects. My research on developing team compositions that involve multidisciplinary members from fields including education, academia, and health. Along with my advisor, we are focused on exploring how HCI can help individuals assemble more effective teams. This effort involves developing socio-technical systems that guide and inform individuals of the potential teams that they can assemble. We employ state-of-the-art algorithms that prioritize inclusion among team members from diverse areas of expertise and familiarity between the team members. Our goal for attending this workshop is to engage in meaningful dialogues with scholars and researchers, leveraging these interactions to refine our approach to building an AI-driven team composition system to foster effective, interdisciplinary collaboration in health-focused HCI research.

Problem

• Developing AI-driven team compositions for multidisciplinary health and HCI projects
• Enhancing inclusion and familiarity among diverse expertise team members
• Building socio-technical systems to guide effective interdisciplinary collaboration

Innovation

• AI-driven team composition system
• Socio-technical systems for team assembly
• Algorithms prioritizing inclusion and familiarity