The potential of AI in learning design is huge. And its influence is only set to grow. But, in the face of AI overload, the practical applications that are already possible can sometimes be lost. This short article explores some ways that AI can already support learning design to save time, effort and improve results.
Concept design, storyboarding, and narrative
One of the foundational stages in learning design is conceptualisation – defining the framework, narrative, and structure of the learning experience. AI tools like ChatGPT, Claude, Grok and others can significantly streamline this process.

These tools can assist in generating detailed outlines, brainstorming narrative ideas, and even sharpening learning objectives. If you have an outcome you’d like to achieve – say, improved health and safety awareness or management skills, learning designers can leverage these objectives to receive suggestions, alternative approaches and examples of best practice. Helping you saving time and contributing to the creative process.
Storyboarding, an essential part of most learning design, benefits greatly from AI-powered tools. Near Life’s AI-powered diagram generator, for instance, enables designers to actually build an interactive graph structure that can be the foundation of a gamified learning experience. This not only helps with visualising learner journeys but also allows a practical means of testing experience via play-through – before detailed content creation – it can even be shared with stakeholders.
The integration of AI-generated images can further enhance storyboarding. Perhaps you may be looking at creating an interactive video experience but before putting the time into producing the videos, you want to test and share the concept. AI generated images can help this process and communicate your ideas. Some tools like Near-Life and Canva have their own AI image generation features. And there are a number of specialist tools available like DALL-E and Stability AI that can be used to create illustrations and images in different styles.
AI-powered content generation

Once the concept is defined and agreed, content generation is the next step. AI lowers the barrier for the creation of engaging media: enabling the production of videos, images, and even immersive 360-degree content. Video tools like Vyond and Synthesia allow you to create detailed videos, based on prompts, in minutes and tools like SkyboxAI are pushing the boundaries with 360-degree visuals: perfect for interactive VR experiences. This opens up new possibilities for immersive content, vastly cutting down the time it takes to produce experiential learning that places learners in realistic or simulated settings.
AI-generated videos can help supercharge anything from interactive video branching scenarios, through to explainer content, tutorials, or more complex gamified simulations. Video creation platforms powered by AI enable the customisation of characters, backgrounds, and voiceovers, further reducing production time.
Another layer of immersive content design that AI makes possible, is the creation of AI characters, in the form of chatbots. And progress is happening all the time. By simulating real-world interactions, these AI entities can act as virtual tutors, mentors, or role-playing partners in scenarios. Chatbots can not only answer questions and act as a guide but can be active participants in gamified experiences. They can add value to anything from corporate training through to healthcare simulations and onboarding.
Tools like Near-Life already make it possible to design and integrate your own AI characters – and ‘teach’ them specific knowledge as well as defining the role you’d like them to play.
The future potential of AI in learning design

And yet still, despite all that is possible in this brave new world, the future of AI in learning is still undiscovered territory. There is huge, emergent potential in AI’s ability to analyse data and refine experiences based on insights drawn from learner interactions.
By capturing data during learning activities – such as engagement rates, decision-making patterns, and assessment results – AI can identify trends and among learning groups and areas for improvement.
Imagine an AI system that evaluates how learners interact with a gamified module, identifying sections where engagement drops or concepts that learners struggle to grasp. This data could inform future iterations of the design, ensuring continuous improvement. AI could also suggest personalised pathways for learners, adapting content based on individual progress and preferences.
Additionally, AI could play a pivotal role in predictive analytics, helping designers anticipate the needs of future learning projects. For instance, by analysing organisational goals, workforce skill gaps, or academic curricula, AI can propose learning solutions that address emerging challenges.
Conclusion
AI is fundamentally changing how we think about and approach learning design: providing tools and capabilities that, used in the right way, can support nearly every stage of the process: from concept creation and storyboarding to content generation and data analysis.
However, despite all these incredible technological advancements it is important to remember that, ultimately, it is the designer’s vision that brings the process to life. AI can simplify tasks and support creativity. But the best work will need human insight that brings true quality, with designers acting like a conductor of an orchestra.
If you’d like to see how Near-Life can help – book a demo now.