Most AI training isn't working. Here's what the research tells us and what we're going to explore on May 28.
There is no shortage of AI courses right now. Platforms, providers, and internal L&D teams are all racing to upskill their workforces in time to matter. And yet, completion rates are low, transfer to real work is minimal, and nobody is quite sure whether any of it is sticking.
This is not a content problem. It is a learning design problem.
The dominant approach to AI upskilling today is essentially the same as it was for digital transformation ten years ago: identify a topic, find a course, assign it to everyone, measure completions. The subject matter has changed. The approach has not.
What learning science has known for decades – and what a growing body of research on AI in education is now confirming – is that this approach was never particularly effective for durable skill development. It is even less effective when the skill in question is something as contextual and fast-moving as working with AI.
Generic AI courses teach people about AI. They do not teach people how to use AI in their specific role, with their specific tools, on the tasks they actually do every day. That distinction sounds simple. Its implications for learning design are profound.
Skill transfer - the ability to apply learning in a new context – is one of the most studied topics in educational psychology. The consistent finding across decades of research is that transfer is dramatically higher when learning is situated: when it happens close to the context in which the skill will be used, when it is built around real tasks rather than abstract concepts, and when learners receive feedback that is specific to their performance rather than generic.
Role-specific, task-based learning is not a new idea. What is new is the ability to deliver it at scale – to personalise a learning journey not just at the content level, but at the level of role, task, pace, and prior knowledge, for every individual in an organisation simultaneously. This is what agentic AI makes possible. And this is what most current AI upskilling programmes are not yet doing.
The organisations that will build genuine AI capability over the next two to three years are not necessarily the ones spending the most on training. They are the ones designing learning that is closest to work – that meets people where they are, builds on what they already know, and develops the specific skills their roles actually require.
For HR and L&D professionals, this is both a challenge and an opportunity. It requires letting go of the course-completion model as the primary measure of success, and building towards a model where learning is embedded in the flow of work, personalised at the individual level, and continuously adapted as both AI capabilities and organisational needs evolve.
On Thursday, May 28 at 10:00 AM CEST, LearnChamp is hosting a free live webinar – AI-Native Learning: What Science Says Actually Works – featuring Vinitra Swamy, co-founder and CEO of Scholé and one of the first PhD graduates from EPFL's Machine Learning for Education Lab.
Vinitra has published over 30 peer-reviewed papers on personalised learning, knowledge tracing, and large language models in education. Scholé, the platform she built directly from that research, was recognised by Forbes as the number one way to master AI agents in 2026, and is already trusted by learners across more than 100 organisations in over 20 countries.
In this session, we will cover:
The cognitive and pedagogical principles behind effective AI upskilling
Why task-based, role-specific learning dramatically outperforms generic course formats
How agentic AI can personalise learning at the individual level — by role, task, and pace
What AI-native L&D looks like in practice, and what it means for organisations designing learning programmes today
The session includes a live Q&A with Vinitra.
This webinar is designed for L&D leaders, HR professionals, and organisational learning strategists who are responsible for AI upskilling in their organisations and who want to move beyond generic training towards approaches that actually build capability.
Whether you are in the early stages of building an AI learning strategy or reassessing an existing programme, this session will give you the research foundation and practical frameworks to make better decisions.
Registration is free.
Can't make it live? Register anyway and we'll send you the recording.