Catalyzing Equity in STEM Teams: Harnessing Generative AI for Inclusion and Diversity
This work addresses psychological barriers for underrepresented students in STEM, offering a roadmap for researchers, educators, and policymakers, but it is incremental as it builds on existing AI applications without new empirical results.
The paper tackles the problem of inequality in STEM teams by exploring how generative AI can promote diversity and inclusion, proposing areas like collaboration assessment and adaptive AI systems to support underrepresented students.
Collaboration is key to STEM, where multidisciplinary team research can solve complex problems. However, inequality in STEM fields hinders their full potential, due to persistent psychological barriers in underrepresented students' experience. This paper documents teamwork in STEM and explores the transformative potential of computational modeling and generative AI in promoting STEM-team diversity and inclusion. Leveraging generative AI, this paper outlines two primary areas for advancing diversity, equity, and inclusion. First, formalizing collaboration assessment with inclusive analytics can capture fine-grained learner behavior. Second, adaptive, personalized AI systems can support diversity and inclusion in STEM teams. Four policy recommendations highlight AI's capacity: formalized collaborative skill assessment, inclusive analytics, funding for socio-cognitive research, human-AI teaming for inclusion training. Researchers, educators, policymakers can build an equitable STEM ecosystem. This roadmap advances AI-enhanced collaboration, offering a vision for the future of STEM where diverse voices are actively encouraged and heard within collaborative scientific endeavors.