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Catalyzing Equity in STEM Teams: Harnessing Generative AI for Inclusion and Diversity

Authors
Nia Nixon,
Yiwen Lin & Lauren Snow
Date
Publisher
arXiv
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.
Why it matters

Collaboration is key to solving complex problems in STEM, yet inequality hinders the field's full potential due to persistent psychological barriers in underrepresented students' experience.

Key findings
  • In STEM collaborative teams, adaptive and personalized generative AI systems can support diversity and inclusion.
  • Inclusive analytics can capture detailed student behaviors to help formalize collaboration assessments.
  • Persistent psychological barriers experienced by underrepresented students hinder multidisciplinary teamwork and equity in STEM fields.

Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.

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