Artificial intelligence (AI) has quickly become part of conversations about teaching and learning. Much of the discussion has focused on what AI can do for education—personalizing instruction, increasing accessibility, reducing teachers’ workload, or supporting student learning. As these opportunities continue to expand, schools are increasingly faced with another challenge that receives far less attention: How do we know whether an AI tool is trustworthy enough to use with students?
This brief examines how mobile devices can support the independence of students with intellectual disabilities as they navigate college campuses and community settings. Drawing on Dr. Hester’s discussion of her collaborative work with Lori Cooney and Dr. Don McMahon, it explains how Google My Maps can be customized according to individual students’ destinations, preferences, support needs, and person-centered plans. The brief also considers how mobile technology can be incorporated into teacher preparation, how educators can develop more balanced perceptions of mobile devices, and what future research is needed to strengthen technology-supported transition and inclusive postsecondary education practices.
I went into ISTELive and ASCD Annual 26 expecting AI to show up often. I did not expect it to show up everywhere I turned, in the session catalog and on the expo floor alike. As a Graduate Research Assistant supporting CIDDL, the Center on Inclusive Design and Innovation in Learning, I work on AI and assistive technology research daily. I am not AI averse. Even so, the sheer volume at this conference gave me pause. This post is not an argument for or against AI in schools. It is simply a look at the numbers, drawn from the conference’s own program search, with a few observations as an attendee.
This episode asks a related question: What does it mean to be AI literate while remaining an active human agent?
Artificial intelligence tools are entering classrooms faster than the research base evaluating them can keep up with. For educators, that gap creates a real dilemma: how do you make sound instructional decisions about AI when rigorous evidence remains so thin? This post summarizes a new report from Stanford’s AI Hub for Education, The Evidence Base on AI in K-12: A 2026 Review (Fesler et al., 2026), which offers one of the clearest pictures yet of what we currently know and, just as importantly, what we don’t.
What if the most powerful assistive technology tool for your classroom was not something you ordered from a catalog but something you built yourself, in an afternoon, without writing a single line of code?
This brief explores what digital accessibility means in practice, why it matters for students with disabilities, and what teacher preparation programs and school leaders can do to build educator capacity in this area. Drawing on Cynthia Curry’s decades of experience and her current leadership of NCADEMI, the brief highlights the most pressing challenges schools face, and the opportunity to embed. accessibility into existing instructional initiatives, and practical resources educators and preparation programs can use to get started.
Conversations about AI in education often fall into two camps: advocates who emphasize its potential and skeptics who warn about its risks. Yet both perspectives tend to ask the same central question: Should students use AI? While this question is important, it may not be the most meaningful one for understanding what is actually happening in classrooms. A more productive question might be: What happens to students’ thinking when they use AI, and under what circumstances does AI become more beneficial or risky? This shift moves the conversation away from simple approval or rejection of AI and toward a deeper examination of how learning itself is changing. In our recent paper by Seung and Dr. Basham (2026), Cognitive Offloading in the Age of Generative AI: What Does It Mean for Students With Learning Disabilities? (published in Learning Disability Quarterly), we explore this question through the lens of cognitive offloading, a concept that helps explain how students redistribute their mental effort when using AI.
Conversations about AI in education often fall into two camps: advocates who emphasize its potential and skeptics who warn about its risks. Yet both perspectives tend to ask the same central question: Should students use AI? While this question is important, it may not be the most meaningful one for understanding what is actually happening in classrooms. A more productive question might be: What happens to students’ thinking when they use AI, and under what circumstances does AI become more beneficial or risky? This shift moves the conversation away from simple approval or rejection of AI and toward a deeper examination of how learning itself is changing. In our recent paper by Seung and Dr. Basham (2026), Cognitive Offloading in the Age of Generative AI: What Does It Mean for Students With Learning Disabilities? (published in Learning Disability Quarterly), we explore this question through the lens of cognitive offloading, a concept that helps explain how students redistribute their mental effort when using AI.
The May 19 CIDDL Office Hour highlighted the CIDDL AI Studio, a free six-week collaborative experience designed to deepen AI literacy.