While talking about the relaunch of our
Learning Design Foundation Certificate on social media, I came across a comment suggesting that people should not bother trying to become learning designers anymore. Given the advances in AI, they argued, it would be better to retrain as a plumber.
I think concerns like this are understandable. AI is changing how learning experiences are designed, developed, and delivered. But the conclusion that learning design is therefore becoming obsolete rests on a very narrow understanding of what learning designers actually do, and what we are capable of doing in an AI-supported future.
This is a transitional moment for our profession. Excitement, experimentation, anxiety, and genuine innovation are all happening at once. Tools are evolving more rapidly than institutional cultures, and expectations are shifting faster than job descriptions.
Many learning designers are already experimenting with AI in real projects, sometimes with impressive results, and sometimes with deeply flawed ones. Much of this experimentation sits somewhere between promise and absurdity.
AI can generate content quickly, remix ideas creatively, and accelerate production workflows. At the same time, it regularly produces outputs that look plausible but are fundamentally wrong.
Image generation offers a good illustration. Anyone who has tried to generate a clock face showing a specific time will have encountered the same issue. The numbers may look odd or incorrectly placed, and the hands almost always display the same aesthetically pleasing time (ten to two, or ten past ten), regardless of the prompt. This is likely because training data1 over-represents advertising imagery, where symmetry matters more than accuracy. In education and training, however, accuracy matters.
I have encountered similar issues recently when generating botanical imagery for a project. At first glance the results looked realistic, but closer inspection revealed inconsistencies that would not exist in nature. Even where accuracy was not necessary for the learning objective, those errors made the outputs unusable.
Assessment design reveals the same pattern. When testing AI-generated multiple-choice questions, I have seen poor practices being emulated, such as weak or silly distractors, incorrect options being identified as correct, and even question sets where none of the answers were valid. While AI can save time, it cannot replace expert quality assurance.
AI generates patterns, not understanding. It does not know what it does not know. It produces convincing outputs, not pedagogically sound ones.
For a qualified learning designer, these problems are visible. For someone under pressure, under-resourced, or lacking professional grounding, they may not be.
The greatest risk AI poses to learning design is not the replacement of people. It is the amplification of poor practice.
As with previous waves of educational technology, AI risks accelerating the production of superficially polished but pedagogically weak learning. With AI layered on top of rapid authoring tools, it is now possible to generate large volumes of content, bespoke graphics, and coherent-looking courses with minimal effort.
Without professional scrutiny, educational judgement, and ethical consideration, this becomes a false economy: an ocean of AI-generated learning that looks convincing but delivers little value (our very own flavour of AI slop).
This is not a failure of individual designers. It is a systems issue, driven by time pressure, cost-cutting, and persistent misunderstandings about the purpose and potential of learning design.
The complex professional landscape of instructional design, learning design, or learning experience design has a long and distinguished history. Our vocation existed long before the internet, long before digital platforms, and long before learning technologies became an industry in their own right. At its heart, it has always been about people, not tools.
Instructional design has always been about creating the conditions in which learning can occur. Supporting understanding, capability, participation, and growth. Helping individuals and organisations achieve meaningful goals through intentional learning.
The digital age, and more recently the rise of AI, can make it easy to forget this. Tools come and go. Terminology shifts. Job titles proliferate. But the underlying purpose of the profession remains remarkably consistent.
Far from making learning design obsolete, AI may force a return to these foundations. It sharpens the distinction between content production and professional judgement, between automation and responsibility, between what can be generated and what must be designed.
For those entering the field now, this is not a diminishing profession. It is one in the midst of redefinition. A field with deep roots, but also with the opportunity to evolve in thoughtful, creative, and ethically-grounded ways.
AI is already changing learning design. It will continue to reshape workflows, tools, and expectations. But replacing human-led learning design would mean replacing judgement, responsibility, and ethical decision-making.
That is something AI cannot do.
What this moment does require is a clearer understanding of what learning design actually involves, beyond tools and outputs. It requires grounding in learning theory, design techniques, critical judgement, and an appreciation of learning as a human, contextual, and often messy endeavour.
This is precisely why foundations matter. Not as a nostalgic return to the past, but as a way of preparing for a future in which learning designers are expected to work alongside increasingly powerful technologies without surrendering professional responsibility.
The
Learning Design Foundation Certificate (LDFC) was created with this in mind: to support people entering or developing in the field to build a robust and holistic understanding of learning design as a professional practice, rather than simply a set of tools or techniques.
The updated programme includes AI tips across every topic area and dedicated AI topics. But it also looks beyond the technologies of this moment in time to a postdigital future, in which learning designers act as advocates for learners and interpreters of the human, physical, and social worlds that technologies alone cannot understand.
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In Part 2, I will look ahead to what this transformation means for the future of the profession, the skills learning designers will need to develop over the next decade, and how organisations and the sector itself may need to change if learning design is to realise its potential in an AI-supported world.
