AI & Accessibility: 5 Practical Ways to Use LLMs to Support Inclusive Learning — from learningguild.com by Dr. Athena Joyce Stanley (B.A., M.A.E., Ph.D.)
The following strategies focus on how instructional designers can design for accessibility by embedding or enabling AI-supported tools within learning experiences. These approaches move beyond access to content and begin to support access to understanding, expression, and participation.
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Personalized Clarification Through AI Copilots
Learners often need additional explanation, but may hesitate to ask questions in live sessions or require more individualized support.
Instructional designers can integrate AI copilots, LLM-powered conversational interfaces, into learning environments to provide on-demand clarification. These tools can act as digital coaches, helping learners understand concepts through simplified explanations and relevant examples.
This approach supports learners who benefit from additional processing time and personalized guidance.
Sample Prompt (for IDs Configuring Copilot Behavior)
You are a workplace learning coach.
Explain the following concept in a clear, simple way:
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- Use plain language
- Provide one practical, job-relevant example
- Avoid jargon unless necessary (and define it if used)
Then ask one follow-up question to check for understanding.
Teaching AI Literacy with Susan Ray — from intentionalteaching.buzzsprout.com by Derek Bruff and Susan Ray
Per Derek Bruff:
In my conversation with Susan, we talk about that syllabus activity, as well as the AI transparency journals she asks her students to keep during her courses. We also talk about how her personal background prepared her to navigate the challenges and opportunities that AI has posed to her teaching, how she approaches assessing student learning in this age of AI—especially in her online asynchronous courses—and much more.
Building the AI-Native University: Student Success and Lifelong Learning — from coursera.org
In this episode, you’ll discover:
- Why building an AI-native campus requires a data-first foundation
- How to build an “achievement architecture” for lifelong success
- How faculty-built AI tutors cut one program’s course attrition from 40% to 10%
- What it means to be a lifelong learning companion
We’re Raising the First AI Generation. They’re Pushing Back. (Rebecca Winthrop, Brookings) — from humanistxyz.substack.com by Allison Dulin Salisbury
“Young people harbor real anger about AI because they’re already experiencing its consequences in their schools, relationships, job prospects, and feeds.”
As Rebecca told me:
“Kids harbor real anger. They’re pissed about climate impacts. They’re upset about job prospects. They’re outraged about AI being used to plagiarize writing and produce counterfeit artwork. Their relationships are being affected, and they’re seeing deepfakes in their feeds.”
Our conversation explores what follows from taking those concerns seriously. We discuss cognitive stunting, the case for small, purpose-built AI models over frontier models in education, why students should spend far more time in explorer mode, and how schools can cultivate agency, curiosity, and independent thinking in an age of AI.
The interview ends with one of Rebecca’s most practical recommendations: every school should have a student AI council, not as a symbolic gesture, but with real influence over the tools their schools adopt, the ways they’re used, and the data students are asked to hand over.
Every generation of technology seems to relearn the same lesson: build with people, not for them. AI should be no exception.
