Enhancing Accessibility in Special Education Through AI

Outline

Introduction & Context Setting (5 minutes)
• Content: Brief overview of the intersection of AI and accessibility.
• Engagement: Interactive poll: "What's your biggest challenge in providing accessible learning experiences?" Results displayed and referenced throughout.

Live Demonstration: AI Tools in Action (10 minutes)
• Content: Presenters demonstrate 3-4 AI tools addressing different disabilities:
o Text-to-speech and speech-to-text for students with dyslexia/writing challenges
o AI-powered alternative text generation for blind/low vision students
o AI tutoring systems with built-in scaffolding for students with intellectual disabilities
o Real-time captioning and translation tools for deaf/hard of hearing and ELL tudents with disabilities
o Address common questions about data privacy, student agency, and avoiding over-reliance on AI
• Engagement: Participants observe live demonstrations using student work samples (de-identified), noting observations on a guided template
• Process: Show authentic use cases tied to actual IEP goals; model how to introduce tools to students.

AI Accessibility Scavenger Hunt (15 minutes)
• Content: Participants explore a curated collection of 10-12 AI tools organized by disability category (learning disabilities, sensory disabilities, physical disabilities, communication disorders)
• Engagement: In pairs, participants use devices to test at least 5 tools from the provided list with QR codes/links. They complete a digital scavenger hunt form rating each tool on: ease of use, accessibility features, privacy considerations, and potential IEP goal alignment
• Process: Structured exploration with guided questions; presenters circulate to answer questions and provide coaching.

Debrief & Critical Analysis (10 minutes)
• Content: Whole-group discussion of discoveries, surprises, and concerns from the scavenger hunt. Quick share-out of top discoveries; presenters highlight 2-3 tools with the strongest accessibility features.
• Engagement: Presenters facilitate discussions, highlighting themes such as ethics, data privacy, student agency, and the importance of avoiding over-reliance on AI. Brief presentation of the ethical framework for AI decision-making in special education. Use a digital word cloud to collect the tool names participants are most excited about
• Process: Brief mini-lecture. Use audience response to create relevance; share 2-3 participant quotes that represent common challenges.

Action Plan Development (10 minutes)
• Content: Individual reflection and planning time using a structured template
• Engagement: Participants create personal action plans, including:
o One specific student population they serve (e.g., "5th graders with dyslexia")
o Two AI tools they commit to piloting within 30 days
o One systemic barrier they'll address (training, funding, policy, etc.)
o One concrete next step with a deadline
• Process: Quiet work time with optional background music; presenters available for one-on-one consultation

Share Out & Resources (5 minutes)
• Content: Volunteer participants share one commitment from their action plan; presenters provide QR code to a comprehensive resource document including all tools, research articles, implementation guides, and follow-up support
• Engagement: Optional: participants exchange contact info with someone working with similar student populations for accountability partnering. A shareable Google Doc will be provided for sharing resources.
• Process: Rapid-fire sharing; final inspirational message about AI as a tool for equity

Outcomes

After this session, participants will be able to:
1. Evaluate at least five AI tools for their potential to support accessibility for students with diverse disabilities
2. Analyze existing instructional materials using AI-powered accessibility checkers to identify and address barriers to learning
3. Create a personalized 30-day action plan for piloting AI accessibility tools with specific student populations in their context
4. Apply ethical frameworks for AI use that protect student privacy while leveraging technology to support IEP compliance and student independence

Supporting research

1. Almarzouq, N. S., Almedlij, M. A., & Alshahrani, H. (2025). Special education teachers' perspectives on the use of artificial intelligence applications in teaching students with disabilities. Education Process: International Journal, 17(1).
2. CAST. (2018). Universal Design for Learning Guidelines version 2.2. http://udlguidelines.cast.org
3. CAST (2024). Universal Design for Learning Guidelines version 3.0. http://udlguidelines.cast.org
4. Drigas, A., & Ioannidou, R. E. (2013). Special education and ICTs. International Journal of Emerging Technologies in Learning, 8(2), 41-47.
5. Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
6. Hopcan, S., Polat, E., Ozturk, M. E., & Ozturk, L. (2023). Artificial intelligence in special education: A systematic review. Interactive Learning Environments, 31(10), 7335-7353. https://doi.org/10.1080/10494820.2022.2067186
7. U.S. Department of Education, Office of Educational Technology. (2023). Artificial Intelligence and the Future of Teaching and Learning: Insights and Recommendations. https://www.ed.gov/sites/ed/files/documents/ai-report/ai-report.pdf
8. Wahyuni, S., Pantiwati, Y., Sunaryo, H., In'am, A., & Bastian, A. (2025). Strategizing Universal Design for Learning (UDL) implementation: Enhancing inclusive education for students with disabilities in higher education. Al-Ishlah: Jurnal Pendidikan, 17(1). https://doi.org/10.35445/alishlah.v17i1.6630
9. Zhang, L., Carter, R. A., Liu, Y., & Peng, P. (2024). Let's CHAT about artificial intelligence for students with disabilities: A systematic literature review and meta-analysis. Review of Educational Research. https://doi.org/10.3102/00346543241293424