By Dr. Athena Joyce Stanley (B.A., M.A.E., Ph.D.)
Learning materials don’t work if they aren’t designed for everyone; that’s why accessibility and inclusivity are the true cornerstones of instructional design. It is our ethical responsibility to ensure that eLearning experiences are designed to support a diverse range of learners with varying needs. At its core, this work is about making learning equitable. Learning experiences should be accessible to all individuals, regardless of ability or disability.
Assistive technologies such as screen readers, language translators, text-to-speech tools, speech-to-text transcription, video captioning and transcription tools, and customizable font and color settings have significantly expanded access to online learning environments. Many of these tools now incorporate AI, while others are evolving in that direction. However, these are not the only tools L&D professionals can leverage to make learning more inclusive.
Large language models (LLMs) offer new and promising capabilities for L&D professionals. AI can enhance the instructional design process by increasing efficiency in brainstorming, planning, content generation, and revision. Beyond supporting course development, LLMs can also be intentionally embedded into learning environments to reduce barriers and better support diverse learners in real time.
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.
Alt-Text Generation for Accessibility
Adding alt-text to images has long been a standard practice for ensuring that individuals who use screen readers can access course content. However, writing high-quality alt-text can be time-intensive.
Instructional designers can use LLMs to generate initial alt-text descriptions for images, significantly improving efficiency while maintaining accessibility standards. This approach allows designers to scale accessible design practices across large volumes of content. As with all AI-generated outputs, review and refinement are essential to ensure accuracy and appropriateness.
Sample Prompt (for IDs)
You are an accessibility specialist. Generate concise, descriptive alt-text for the following image.
Focus on:
- Key visual elements
- Any visible text in the image (quote exactly if readable)
- Context relevant to a learning environment
Keep the description under 150 words. Avoid assumptions or interpretation beyond what is visible.
Image: [upload your image]
Reflection or Brainstorming Chatbots
Learners often experience anxiety when beginning open-ended tasks or may struggle to generate initial ideas. Instructional designers can address this by embedding or linking to reflection-oriented chatbots within learning experiences.
These chatbots can be designed to guide learners through structured thinking processes by asking open-ended, scaffolded questions. Importantly, they should be intentionally constrained to avoid providing direct answers, instead supporting learners in developing their own ideas.
This approach helps reduce “blank page syndrome” while promoting active engagement and learner confidence.
Sample Prompt (for IDs Configuring Chatbot Behavior)
You are a learning coach supporting a learner in a professional development course.
Your role is to help the learner think through their ideas, not to provide answers.
When the learner shares a response:
- Ask 2–3 open-ended, guiding questions
- Encourage deeper thinking and clarification
- Do not generate solutions, examples, or final answers
- Maintain a supportive and non-judgmental tone
Begin by asking the learner what they are working on and what ideas they have so far.
Instant Reading-Level Adaptation
Content complexity can present a significant barrier for many learners, including English language learners (ELLs), neurodivergent learners, and individuals with varying literacy levels.
Instructional designers can embed or provide access to LLM-powered tools that allow learners to adapt content to their preferred reading level. This enables learners to simplify dense materials, clarify complex instructions, or generate plain-language summaries as needed.
Designing for this type of flexibility supports more inclusive engagement with course content.
Sample Prompt (for IDs)
You are an instructional designer focused on accessibility.
Rewrite the following text at a sixth-grade reading level while:
- Preserving key terminology
- Maintaining the original meaning
- Using shorter sentences and clear language
If helpful, break complex ideas into bullet points.
Text: [Insert your training content]
Sample Prompt (for Learners)
Help me understand this content more easily.
Rewrite the following text so that it is easier to read and understand:
- Use simple, clear language
- Keep important terms (and explain them if needed)
- Break ideas into short sentences or bullet points
If possible, include a brief summary at the end.
Text: [Paste the content here]
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:
- 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.
Concept: [Insert concept]
Sample Prompt (for Learners)
Can you help me understand this concept more clearly?
Please:
- Explain it in simple, easy-to-understand language
- Give me one example related to a real work situation
- Define any unfamiliar terms
Then ask me a quick question to check my understanding.
Concept: [Paste the concept here]
Generating Practice Examples
Learners benefit from exposure to multiple examples and opportunities to apply concepts in varied contexts.
Instructional designers can use LLMs to generate additional scenarios and practice examples that reinforce key concepts. These examples can be tailored to different levels of complexity, supporting differentiated instruction and self-paced learning.
By designing for flexible, on-demand practice, L&D professionals can better meet learners where they are.
Sample Prompt (for IDs)
You are an instructional designer creating practice activities for learners.
Generate 3 additional practice examples based on the following concept.
Requirements:
- Vary the difficulty (easy, moderate, slightly challenging)
- Keep instructions clear and concise
- Ensure each example is relevant to a real-world workplace context
After each example, include a brief explanation of what a strong response should include (do not provide a full answer).
Concept: [Insert concept]
Sample Prompt (for Learners)
Can you give me a few practice examples to help me better understand this concept?
Please:
- Provide 3 examples
- Start with an easy example, then make them a bit more challenging
- Keep each example short and clear
- Use real-world or workplace situations if possible
After each example, briefly explain what a good answer should include (but don’t give the full answer).
Concept: [Paste the concept here]
Responsible Use & Human Oversight
AI can support accessibility, but human oversight remains essential to ensure accuracy, fairness, and meaningful learning.
Designing for equity and accessibility requires empathy. Instructional designers should remain intentional in how AI is used, continuously considering the needs of their learners. This helps ensure that AI-enhanced learning experiences remain human-centered and inclusive.
Conclusion
AI has the potential to transform the way instructional designers approach their work. LLMs now offer new opportunities to enhance accessibility, not only by improving efficiency in design workflows, but by enabling more flexible, responsive, and inclusive learning experiences.
Join Us at the AI & Learning Design Online Conference
Don’t miss the Learning Guild’s AI & Learning Design online conference, August 26-27, 2026. We’ll explore how AI is changing workflows and demanding complex choices about what to automate and where human expertise is still needed. The six sessions move beyond exploring AI-driven tools into probing decisions about quality, trust, accessibility, and ensuring that learning experiences are engaging and meaningful.
Over two days, you’ll explore ways to use AI safely in regulated or secure contexts, streamline development, retain the human connection, and more. Each day, you can join our new ThinkSpaces to chat with speakers and other attendees at virtual roundtable discussions.
Registration is open—and it’s free for Professional members!
Image credit: Supatman
