Teaching staff face growing demands to design learning that meets the diverse needs of learners and students, whilst managing limited time and resources. Generative AI can ease some of that workload, but its greater value lies in helping staff explore how inclusion and flexibility can be built into learning design. Through small, reflective experiments, staff can develop confidence, capability and insight into what truly makes learning inclusive.
By experimenting with AI tools, staff can test how materials might be adapted, review accessibility features and reflect on how content supports different learner needs. This approach builds AI literacy while deepening understanding of inclusive practice.
Building on Jisc’s staff guidance for further education, which outlines how to use AI safely and confidently, this blog explores how small-scale experimentation can help staff apply inclusive design principles in practice and strengthen inclusion, accessibility and flexibility in learning design.
Using Universal Design for Learning as a design lens
One practical way to approach this reflective use of AI is through the Universal Design for Learning (UDL) framework, developed by CAST, which encourages planning for learner/student diversity from the outset. Its three principles — engagement, representation, and action and expression — promote flexibility and choice in how learners and students access and demonstrate knowledge.
Although UDL predates generative AI, AI tools now make it easier for staff to explore those principles in practice. Staff can experiment with prompts that adjust complexity, tone or format to see how content can be made more accessible or engaging. Each experiment deepens understanding of variety and builds capability in using AI critically and creatively.
Example reflective exercise
Try prompting a gen AI tool like ChatGPT or Gemini to present information in multiple inclusive formats, then reflect on how each version supports different learner needs and how AI might help you explore inclusion in learning design. For example:
Step 1: Prompt: “Generate four versions of the following explanation: ‘The water cycle describes how water moves between the air, land, and oceans.’ Create a simple, plain-text summary, then rewrite it for UK education Levels 2, 4, and 6 using suitable vocabulary and short definitions of key terms. Finally, provide a screen-reader-friendly outline in text only, also including brief key-term definitions. Label each section clearly.”
Step 2: Compare the differences — what makes each more or less inclusive?
The examples below illustrate how small-scale experimentation with AI can develop confidence in applying inclusive design principles.
Developing capability through experimentation
Here are three more examples to get you thinking about how this approach could be used in other scenarios.
Exploring inclusive design in applied learning
Scenario
A course team is developing materials for a practical or applied learning module where students or learners have varied reading levels and language proficiency.
What staff can learn by experimenting with AI
By asking AI to rewrite materials at different reading levels or in multiple languages, staff can see how the tool handles complexity and tone.
Reviewing these outputs builds confidence in evaluating accuracy, clarity and cultural relevance. It also helps staff understand how differentiation might look across levels without needing to produce multiple drafts from scratch.
Exploring flexibility in blended delivery
Scenario
A teaching team is creating materials for a cohort that includes both on-campus and remote learners, balancing study with work or caring responsibilities.
What staff can learn by experimenting with AI
By prompting AI to reformat one resource into different delivery modes — such as a short video script, a quiz or a written guide — staff can explore how information translates across formats.
Evaluating which versions maintain engagement and accessibility strengthens awareness of how design choices affect learner experience in flexible and blended settings.
Exploring accessibility in research and independent learning
Context
A researcher or academic author wants to make their publications more accessible to a wider audience, such as students, practitioners, or the public.
What staff can learn by experimenting with AI
Academic and teaching staff can experiment with AI tools to simplify complex research texts, generate plain-English summaries, or suggest inclusive formats for diverse audiences.
Comparing outputs encourages critical evaluation of how AI interprets disciplinary language and supports accessibility in academic communication.
Summary
AI offers staff a practical way to explore how variety and inclusion can be built into learning design. Through experimentation, staff can test how content might be adapted, represented or shared to meet different learner needs and contexts. These small, reflective uses of AI build understanding of inclusive design principles. Used thoughtfully, AI becomes an assistant in the creative process — helping staff design materials and experiences that are more adaptable, accessible and inclusive for all learners and students.
As staff gain confidence through experimentation, institutions develop collective capability to design learning that is inclusive by default and ready for responsible AI adoption.
Find out more by visiting our Artificial Intelligence page to explore publications and resources, learn more about our communities and sign up for our AI Literacy training.
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Get in touch with the team directly at AI@jisc.ac.uk