Towards Effective Multidisciplinary Health and HCI Teams based on AI Framework

TL;DR

This paper discusses developing AI-driven socio-technical systems to facilitate effective multidisciplinary health and HCI teams, emphasizing inclusion and diverse expertise for improved collaboration.

Contribution

It introduces a novel AI framework for assembling multidisciplinary teams in health and HCI, focusing on inclusion and expertise diversity.

Findings

Development of algorithms prioritizing inclusion and expertise diversity

Design of socio-technical systems to guide team assembly

Initial insights into interdisciplinary team effectiveness

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.