Drawing from a collaborative event involving students, educators, and policymakers, Stanford’s Accelerator for Learning has released a white paper advocating for AI systems designed around the needs of individuals with learning differences.
The report titled “AI + Learning Differences: Designing a Future with No Boundaries” stems from a two-day Working Symposium and Hackathon hosted by the Accelerator. This event gathered over 100 participants, including students with learning differences, to engage in dialogue and design innovative tools focusing on AI and learning differences.
The symposium emphasized the disability rights principle, “nothing about us without us,” highlighting the need to include people with lived experience in technology design. Participants discussed how AI could better support learners by identifying where additional support is needed, enhancing assistive technologies, improving Individualized Education Plans (IEPs), and addressing social and emotional well-being.
Released on July 21, the paper presents 12 recommendations for developers, educators, researchers, and policymakers to ensure equitable AI systems for all abilities. Elizabeth Kozleski stated: “Empathy and access must not be afterthoughts.” She highlighted that designing AI with disabilities in mind improves experiences for all learners. Chris Lemons added: “When we design for the full range of human experience, we build better systems for everyone.”
The white paper includes a Hackathon Toolkit to aid schools and organizations in creating inclusive AI tools. Seventh-grade student Mae T., who served as a hackathon judge, reflected on shifting perceptions of disability: “Teaching solutions should be based on fixing our ideas about learning differences … because everyone is different.”
This initiative was supported by the Alana Foundation alongside CAST, Stanford Institute for Human-Centered Artificial Intelligence (HAI), and Children’s Health Council (CHC).
