Everyday Uses of Generative AI for Learning Designers

Designing digital learning experiences means switching between big-picture thinking and getting the details right, often on a tight schedule. Generative AI (GenAI) tools like ChatGPT or CoPilot can help you get unstuck, test out different approaches, or write activities more efficiently. Below are some practical ways AI can help you throughout the design process.

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Generative AI use cases

Below are some examples of how GenAI can complement your design process. Try them out, adapt them to your needs or even test out your own use cases.

Persona development

AI can help create learner personas grounded in institutional data and access needs, supporting inclusive and human-centred design. You can use these personas to guide tone, pacing or language, or even to play devil's advocate.

Tip: Combine AI with actual learner analytics or survey feedback for more accurate outputs and to avoid designing based on stereotypes or assumptions.

Curriculum mapping and gap analysis

AI can assist in mapping learning outcomes and identifying where content, skills, or types of interactions are underrepresented. It can also highlight misalignments between assessment, activities, and intended learning levels.

Tip: Validate AI-generated gaps against your institution’s curriculum frameworks or benchmarks to ensure rigour.

Diversifying learning resources

AI can be prompted to suggest scholarly and practitioner materials from diverse, global and underrepresented voices. It can broaden reading lists beyond dominant Western or mainstream perspectives.

Tip: Be explicit in prompts when seeking inclusive resources; ask for works by women, LGBTQ+ scholars, or voices from the Global South. Also, leverage web search or deep research functions (where available).

Ideation for constructive alignment

AI can propose aligned activities, assessments, and feedback methods based on learning outcomes and module level. This helps ensure that all elements of the design reinforce each other and stay pedagogically coherent.

Tip: Provide AI with the exact level descriptor (e.g. QAA Level 5) to encourage accurate activity and assessment suggestions.

Repurposing content for different modalities

AI can adapt existing text-based activities into video scripts, podcasts, or infographic descriptions. This saves time while enabling multimodal learning suited to varied learner preferences.

Tip: Always review AI outputs for accessibility and tone—ensure it suits the intended platform and student demographic.

Suggesting peer interaction strategies

AI helps design inclusive, asynchronous peer engagement activities such as structured debates, peer reviews or collaborative projects, encouraging social learning, deeper reflection and a greater sense of community.

Tip: Guide AI to include clear and specific instructions on how students are expected to engage. It may help to tell the AI what platform (e.g. Blackboard Ultra) you are using.

Designing quizzes and knowledge checks

AI generates formative assessments, including multiple choice, scenario-based, and drag-and-drop-style question banks. These can be mapped to learning objectives and integrated into platforms like Moodle or Canvas.

Tip: Ask AI to provide a rationale for each correct answer to make the quiz feedback more meaningful and educative.

Breaking down complex theories

AI can deconstruct academic theories into simpler terms, helping to bridge the gap for learners with limited subject background or language confidence.

Tip: Request explanations at different reading levels, e.g. by specifying the reading age, to suit your learner personas.

Aligning with external frameworks

AI can support mapping to professional or institutional frameworks, improving assurance and employability relevance. It could also be used to help align content with the United Nation's Sustainable Development Goals (SDGs).

Tip: Provide AI with the full framework text or URL—don’t assume it has current standards embedded accurately.

Chunking content for microlearning

AI helps restructure large content blocks into manageable, structured learning bites. This supports learner autonomy, just-in-time learning, and mobile-first design.

Tip: Ask AI to apply clear subheadings and include recap or reflection prompts within each chunk for greater impact.

Bias analysis and language review

AI can flag potential bias in case studies, scenarios, or assessment language (e.g. gendered or Western-centric framing). It supports inclusive design by identifying assumptions that might exclude or misrepresent learner groups.

Tip: Use AI alongside human review as bias detectors in AI models can miss subtleties or introduce new ones.

Generating reflection prompts

AI can tailor reflective journal or discussion prompts to align with learning objectives and support deeper learning. It enables personalisation by incorporating real-world contexts and emotional engagement.

Tip: Prompt AI to link reflections to professional practice or lived experience to enhance relevance for apprentices and postgrads.

Supporting accessibility checks

AI can identify opportunities to improve plain language, alt text, and readability for a broad student demographic. This helps ensure content is usable by students with diverse learning needs and preferences.

Tip: Use readability-level constraints in prompts and test outputs with screen readers where possible.

Five ways to get better results with GenAI

The quality of what the AI gives you often depends on what you put in. These strategies will help you prompt more effectively and get outputs that align with your needs — select each of the following five headers for more information.

Be specific about your context

AI performs best when it understands your (and the students') needs. Add details about the learner, delivery mode, or subject matter for more relevant and useful results.

Example prompt: “Generate three assessment ideas for a fully online, asynchronous, level 6 Introduction for Psychology module for an Organisational Behaviour course. Ensure they are aligned with the following learning objectives...”

Ask for multiple options

Get variety by asking the AI to give you several ideas at once. This helps you compare or blend approaches without being boxed into one direction.

Example prompt: “Suggest five different peer learning activities to promote collaboration in a Level 5 online marketing module.”

Describe your audience

Tell the AI who the activity or explanation is for, including their level, experience, and goals. This makes the outputs more accurate and inclusive.

Example prompt: “Explain the concept of supply chain ethics to first-year international business students with no prior industry experience.”

Give examples

You’ll get better output if you show the AI what you want. This could be a sample format, structure, or tone.

Example prompt: “Here’s an example of an activity for a similar module. Use a similar format and approach to create an activity for the following content: [paste content].”

Refine iteratively

Start with a broad prompt, then adjust based on what the AI gives you. Asking for edits or improvements can sharpen tone, accessibility, or alignment.

Example prompt: “Revise this scenario so it is 50% shorter, is written in the second person, for a reading age of 16 and avoids slang or figurative speech.”

Want to explore ethical and collaborative AI use in more depth? These articles offer frameworks and practical examples:

• Responsible learning design with AI

• Team up with AI: Human-in-the-loop learning design

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