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

IF 3.4 Q1 EDUCATION & EDUCATIONAL RESEARCH
Nia Nixon, Yiwen Lin, Lauren Snow
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引用次数: 0

Abstract

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, and 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.

促进科学、技术、工程和数学团队中的公平:利用生成式人工智能促进包容性和多样性。
合作是 STEM 的关键,多学科团队研究可以解决复杂的问题。然而,STEM 领域中的不平等现象阻碍了其潜力的充分发挥,原因是代表性不足的学生在学习过程中始终存在心理障碍。本文记录了 STEM 中的团队合作,并探讨了计算建模和生成式人工智能在促进 STEM 团队多样性和包容性方面的变革潜力。利用生成式人工智能,本文概述了促进多样性、公平性和包容性的两个主要领域。首先,通过包容性分析将协作评估正规化,可以捕捉到细粒度的学习者行为。其次,自适应、个性化的人工智能系统可以支持 STEM 团队的多样性和包容性。四项政策建议凸显了人工智能的能力:正规化协作技能评估、包容性分析、资助社会认知研究、人类-人工智能团队包容性培训。研究人员、教育工作者和政策制定者可以建立一个公平的 STEM 生态系统。本路线图推进了人工智能增强型协作,为未来的 STEM 提供了一个愿景,在这个愿景中,不同的声音将在协作性科学努力中得到积极鼓励和倾听。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Policy Insights from the Behavioral and Brain Sciences
Policy Insights from the Behavioral and Brain Sciences Social Sciences-Public Administration
CiteScore
5.30
自引率
0.00%
发文量
24
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