AI-Powered Learning Design
How AI is transforming the velocity & effectiveness of learning experience design
One question I get asked a lot is: as someone who designs learning experiences, has AI impacted your design & development process in the last few months?
The answer is yes.
My hypothesis, supported by a tonne of experimentation in the last few months, is that by leveraging the power of AI to increase the velocity of our processes, we can focus on where we bring most value.
For me, this is three-fold:
Developing my domain-specific knowledge to be expert in my field.
Honing my analysis skills to develop a deep understanding of my learners’ needs & motivations.
Developing my learning science expertise to optimise my design choices for learner motivation and mastery.
In this post, I’ll share examples of how I and other educators use AI day to day and - I hope - provide some inspiration for you to make your learning design process more efficient and effective.
AI & Human Velocity
In controlled tests, ChatGPT has proven to be capable of increasing human productivity (i.e. the time it takes to successfully complete a task) by around 35%.
To put this into context:
ChatGPT is just one of hundreds of tools that can be used in the process of designing a learning experience.
The productivity gain of the steam engine in the 19th century was only 25%.
Research has shown that in the pre-AI world, the design and development of one hour of learning content:
Takes between 20 and 200 hours to produce.
Has an average cost of $10,000 per hour of learning produced.
TL;DR: designing and developing learning experiences is slow and expensive - there’s a lot of efficiency to be gained.
Using AI to Increase Learning Design Velocity
From conversations with learning designers, instructional designers and educators on the ground it’s clear that a lot of experimentation with AI tools is happening.
Below is a summary of a post-AI process informed both by my own experience and the experiences shared by colleagues from around the world.
1: Gathering Insights
The initial step in your design process likely involves gathering information, often in collaboration with Subject Matter Experts (SMEs) and learners.
Traditionally, we’d manually collect this data, which could take several weeks depending on the volume of responses. AI can significantly enhance the efficiency of this part of the process.
In-meeting AI-powered in-meeting transcription tools like Fathom.ai can automatically transcribe & create minutes from online meetings.
AI-powered transcription tools like Otter.ai can automatically transcribe notes from interviews or conversations with SMEs.
2: Analysing Insights
Traditionally, we’d manually analyse data, which could take several weeks depending on the volume of responses.
AI-powered survey analysis tools, like SurveyMonkey's AI analytics, enables users to automatically analyze data and identify learning needs. AI-powered data analysis tools provide visualizations and insights that make it easy to quickly understand the key findings and themes, saving time and reducing the risk of manual error.
I’ve also started to experiment with User Interview tools like Dovetail which use Natural Language Processing (NLP) techniques to capture and analyze key themes and sentiments, ensuring that no crucial information gets lost.
Top tip: UX skills & research tools have the potential to bring a a tonne of additional value to the learning design process - check them out!
3: Developing Domain Expertise
Whether you’re the SME or you’re working with a SME, the process of learning design is always enhanced by a solid understanding of the most up to research on the topic you’re designing for.
AI tools can help rapidly summarise complex, domain-specific content like long form books and academic articles into key take aways.
Tools like Scholarcy and SMMRY are popular with both learning designers and educators. They generate concise summaries of long texts, making them easier to digest and incorporate into your instructional design.
4: Rapid Prototyping
One of the most game-changing aspects of AI for people who design learning experiences is the opportunity that it offers us to rapidly create prototypes (perhaps even multiple versions of learning designs) to test with stakeholders and/or learners before we commit to development.
Uizard and Miro both use AI to enable rapid prototyping.
Presentation tools like Tome and Gamma enable you to rapidly create beautiful presentations to share your design ideas and narratives.
5: Content Development
Once you have your knowledge base, it's time to get designing & building. This is where AI writing tools like ChatGPT, Quillbot and Rytr come into play.
By inputting key findings and themes generated in steps 1 & step 2, you can generate a draft text that can be refined and approved by the SME. This not only accelerates content production but also ensures consistency in the information being communicated.
Of course, AI doesn't just excel at text—it can also help with image, video and audio content.
For images, tools like Canva’s new text-to-image product turn text into original images in seconds. Night Cafe is one of the most popular text-to-image tools among educators.
Google's AutoDraw uses AI to turn rough doodles into professional-looking drawings, which can help create more visual, accessible content for learners who struggle with text-based materials.
For video content, tools like Lumen5 and InVideo can automatically create videos from text content. For those who want to really dive into the deep end with AI, tools like Synthesia and Collosyan can generate “humanised” video from text.
When it comes to audio, AI voice synthesis tools like Google's Text-to-Speech and Amazon's Polly can convert text into lifelike speech, enabling you to create voiceovers without needing a human narrator.
AI music creation tools like OpenAI's MuseNet can even compose new music to go along with your content.
When you’re in post-production, tools like Krisp use AI to reduce background noise in audio recordings, improving the audio quality of your content. AI video editing tools like Frame.io can partially automate time-consuming editing tasks.
6: Accessibility & Inclusion
Ensuring inclusivity and accessibility of course content is crucial, and there are several AI tools that can make this both faster and more effective.
Textio uses AI to provide suggestions for improving the readability and inclusivity of your text. It can help identify biased language or phrases that could be exclusionary, which is massively important when striving for inclusive content.
Hemingway App uses AI to make your writing more accessible by identifying complex sentences and suggesting simpler alternatives. This can be particularly useful for ensuring that learning materials are easy to understand for all learners.
As I covered in a previous blog post, with the right prompts you can also use ChatGPT to review your curriculum content for equity and inclusivity.
That’s all folks! I’d love to hear what tools you’re using and how they’re impacting both your efficiency and effectiveness.
Happy designing,
Phil 👋
The tension between AI-augmented facilitation and authentic learning experience is one of the more productive debates in L&D right now. The risk of over-automation, where AI generates so much of the reflection and synthesis work that learners skip the productive struggle is real and worth foregrounding.
That said, there's an important distinction between the learning activity layer and the administrative layer around it. Documentation of session outcomes, capturing participant insights, and structuring debrief outputs are genuinely administrative - they don't constitute the learning experience, they serve it. Tools that automate the documentation layer (like meeting intelligence platforms such as CogniAIX in facilitation contexts) can free facilitators to invest more time in designing the actual learning experience rather than managing its records.
The design principle I'd suggest: AI on the periphery, human presence at the core. When AI handles the capture and structure, you have more cognitive space for the relational and adaptive work that learning actually requires.
It was a good read. Article was informative but I left like it was missing my favourite tool Imagine AI Image Generator.