As we move further and further into 2026, the learning design landscape is shifting faster than ever driven by generative AI, increasing expectations for personalised digital experiences, and an often-stated renewed commitment to accessibility, but many of these shifts only hold under ideal conditions unless they are designed for variability from the outset. Earning teams are orchestrating adaptive, human‑centred ecosystems that meet learners where they are which sharply contrasts the content that is still a digitised version of powerpoints and pdfs.
Now AI gets talked about a lot and will be talked about a lot for the foreseeable future (last week’s blog was on Practical Tips and Ethical Considerations for using AI) but it does mean that powerpoint and pdf filled digital learning landscapes will finally begin to decline as even the most time poor SMEs feed their files into AI to generate content. Learning designers too can use SME file to generate content faster than ever before. What is done this this content will become the differentiator in 2026, particularly in how it is reviewed, constrained, and judged before use. (This shift echoes wider predictions about how AI will reshape roles, workflows, and expectations in education — outlined in The Future of AI and Machine Learning in Education.)
AI is already becoming deeply integrated into authoring tools, enabling organisations to rapidly prototype, revise, and localise learning materials at scale. Instead of starting from a blank page, designers will use AI to produce draft scripts, scenario outlines, visual ideas, and even multimedia assets that can be refined through human expertise. In previous blogs, I’ve touched on how this shift doesn’t replace the designer, or the SME; it elevates them to a curator and critical thinker. With the tools we have right now, we should be exploring (and sharing with SMEs) how AI‑generated drafts can speed up early-storyboarding and how iterative prompting can help refine tone, complexity, and structure. Taking the pressure off as we are all asked to do more with less. The key is to treat AI as a collaborative partner: generate, review, improve, and personalise, recognising that it can assist with generation but not judgement.
Alongside rapid content creation comes what may be the defining trend of 2026: true personalisation at scale. This year’s tools are making that not just possible but practical. Most are in their early stages, and I don’t know of any widespread integrations into LMSs just yet, but it would be foolish to assume it isn’t coming and quickly. Streaming, social media, and shopping platforms are already highly personalised, and learners will increasingly expect similar in their digital learning experiences: AI-driven pathways recommending content based on learner data, previous performance, or even real‑time interactions that adapt to their current goals and context, though these systems shape what becomes visible to learners and constrain the pathways they are able to take.
Implementing this tomorrow involves designing modular content today and building branching options. Start by creating small adaptive elements within existing courses and expand as your ecosystem matures. The algorithms in these tools might create personalised pathways where the learner’s choices, interests, or confidence levels influence the sequence but learning designers and SMEs still need to ensure the journey is meaningful and leads to successful outcomes for learners. (And to check whether these adaptive pathways actually support learning rather than just algorithmic flow, the frameworks in Learning Analytics for Designers: Making Data Meaningful offer practical ways to analyse patterns and refine these personalised journeys.)
A natural off shoot of personalised learning journeys and another notable trend for 2026 is likely going to be the rise of multimodal learning experiences. With AI now capable of producing audio, video, interactive elements, and detailed imagery in minutes, designers can craft richer environments without relying on multiple specialist teams. Whether it’s generating instant voiceovers or creating draft visual concepts (something I’ve definitely experimented with) the barrier to creating varied content, a goal for a long time now, is significantly lower. The challenge for designers today is not technical capability but intentionality: choosing the right format for the right learning moment. But personalisation likely also means that learners will be able to choose the mode for themselves and AI certainly makes that easy so the intentionality must also be written into the AIs guidelines for both the journey learners take and the modality it is able to generate. (Choosing the right format at the right moment is also grounded in well‑established multimedia design principles. The breakdown in Top Multimedia Principles for eLearning Design can help keep multimodality purposeful rather than chaotic.)
The focus on personalised learning journeys and multimodality pushes learning design more seriously into accessibility in 2026. In personalised journeys, accessible-by-default will be an expectation rather than an aspiration. Which means learning design also needs to be accessible-by-default in 2026. Not just compliance with WCAG standards but, inclusive pedagogical design; content that supports different cognitive styles, sensory needs, and learning preferences. Many authoring tools already include built‑in checkers or AI‑powered prompts to help designers create accessible text alternatives, structure content correctly, and ensure that multimedia elements support diverse learners, and their number will continue to increase.
In an earlier blog, I explored accessibility-first rewrites and structural improvements, and those approaches remain the best starting point: begin every project with accessibility requirements in mind rather than retrofitting at the end. But this 2026 trend isn’t accessibility and inclusivity as compliance, a resource intensive add-on, this is accessibility and inclusivity as a fundamental part of the design, not an add-on but a condition that determines whether the learning experience holds under variation, and pushing for inclusivity in the tools we use so the learning journeys we create are fit all learners. (If you want a refresher on accessibility‑first design choices that pair naturally with these trends, the examples in Ensuring Digital Accessibility in Learning Design provide a solid foundation for building access‑by‑default approaches.) Of course, this means our institutions can then profit from all learners and I’m not above highlighting that over and over again if it moves the needle even a little bit.
Finally, organisations are increasingly embracing continuous improvement cycles powered by data. Real-time analytics, sentiment insights, and AI‑powered pattern detection enable learning designers to iterate more effectively than ever. You don’t need advanced platforms to begin this: start by reviewing learner behaviours, noting where they hesitate, skip, or rewatch. Combine this with AI‑assisted insights to identify opportunities for small, high‑impact improvements, while recognising that behavioural data supports only limited forms of inference about learning.
Truthfully, 2026, as with 2025, will continue to be all about AI but, as it matures and our use of it becomes more nuanced, it will revitalise ideas that we know work but have previously been too resource intensive to be embraced fully. The most successful learning designers, will be those who combine innovation with thoughtful design, using AI to enhance creativity, personalisation, and accessibility without losing the human understanding at the heart of effective learning. The tools are already here; the future is in how we use them.
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