2025 Collaborative Network for Engineering & Computing Diversity (CoNECD)

Bridging Educational Equity Gaps: A Systematic Review of AI-Driven and New Technologies for Students Living with Disabilities in STEM Education

Presented at Track 3: Technical Session 1: Bridging Educational Equity Gaps: A Systematic Review of AI-Driven Tools for Students Living with Disabilities in Engineering and STEM Education

The underrepresentation of Students Living with Disabilities (SLWD) in engineering highlights a critical educational diversity gap, necessitating fundamental changes within engineering education to attract, support, and retain these students. Current research underscores the effectiveness of personalized learning strategies, which consistently lead to improved learning outcomes and increased student engagement for SLWD populations. However, the layered complexities of race, ethnicity, gender, socio-economic status, and cultural backgrounds, which intersect with disabilities, make it essential to adopt a comprehensive solution that not only addresses accessibility but also embraces the full spectrum of student diversity to truly close the equity gap. Artificial intelligence (AI) holds transformative potential in bridging these gaps by augmenting learning accessibility and providing personalized support. This study conducted a systematic literature review of thirteen articles to explore existing AI-driven and new technologies in STEM education for SLWD. The review identified several benefits of AI-driven and new technologies for SLWD, including enhanced engagement, accessibility, personalized learning, progress tracking, skill development, deeper understanding, and increased confidence. However, existing tools for SLWD also reveal significant challenges, including accessibility and technological limitations, customization constraints, practical and applicability barriers, and educational inefficacy. This review analyzed proposed solutions to these challenges in technological advancements, user-centric design, and methods for evaluation and validation. The insights from this review will inform a proposed participatory design study aimed to amplify the marginalized voices of SLWD by addressing their specific academic and intersectional needs. This approach will take a step towards an equitable learning environment, setting a new paradigm in personalized, diverse, and inclusive engineering education through AI technology.

Authors
  1. Kevin Zhongyang Shao University of Washington [biography]
  2. Eric Kyeong-Min Cho University of Washington [biography]
  3. Sophia Tang University of Washington [biography]
  4. Hanlin Ma University of Washington [biography]
Note

The full paper will be available to logged in and registered conference attendees once the conference starts on February 9, 2025, and to all visitors after the conference ends on February 11, 2025

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For those interested in:

  • Advocacy and Policy
  • Broadening Participation in Engineering and Engineering Technology