In the last decade, eLearning has evolved from static, one-size-fits-all modules into increasingly adaptive, data-driven experiences. The concept of individualised learning pathways sits at the centre of this transformation.

The vision is compelling – great quality training, tailored to the specific needs of each individual learner. The promise of delivering the right learning, to the right person, at the right time, in the right format. However, while the idea sounds straightforward enough, execution remains complex.

We recently carried out a survey of L&D leaders, asking them what they looked for from their LMS, and no surprises, individualised learning pathways came out on top. However, the fact that it did so, suggests that perhaps successful orchestration of the ultimate goal has yet to be reached.

It has been suggested that individualised learning pathways point to the ‘Holy Grail’ of online learning. They meet learners where they are in terms of knowledge. They train them in what they don’t know rather than feeding them generic information they may already know, thus reducing cognitive overload and closing knowledge gaps more efficiently.

They result in higher engagement with online training. They are relevant to the individual’s training needs, job role or interest. Relevance drives motivation – when training content feels targeted and useful, learners are more likely to engage, interact, and complete courses.

On a more strategic level, in the technological age in which we are living, the working environment is changing rapidly. Individualised learning pathways allow for efficient reskilling and upskilling of individuals in specific roles, to align with evolving business needs.

So why does it still seem a challenge to execute individualised learning pathways at scale?

Firstly, creating individualised learning, or adaptive, pathways is exponentially more complex than linear course design. Designers must anticipate multiple learner journeys, decision points, and content variations. Additionally, personalisation requires a huge amount of training content. More pathways mean more training content needs to be created or sourced and, crucially, maintained and updated. Of course, AI-driven solutions are improving, but still require subject matter expert involvement, integration, investment, and technical understanding.

Also, data-driven personalisation is only as good as the data behind it. Incomplete, biased, or poorly interpreted data can lead to ineffective, or even counterproductive, learning recommendations. On top of this, collecting and using learner data raises both technical and ethical challenges which have to be considered and addressed to establish what and how much data can be collected and used.

There is also the question of what people actually mean when they talk about individualised learning pathways. What are the expectations?

There seems to be a growing recognition that technology alone is not the answer. For many, individualised learning involves human connection – on-the-job learning, mentoring, coaching, social interaction. The concept of individualised learning is evolving and requires a shift in mindset. Individualised learning pathways are not just about delivering content, they are about creating personalised learning experiences.

This may be one of the reasons why the conversation is shifting away from ‘pathways’ towards dynamic learning ‘ecosystems’. Learning and development is a holistic experience, where adaptive, personalised pathways play a role within a much wider, interconnected journey.

It seems that the concept of individualised learning pathways is still evolving thus the “Holy Grail” may remain just out of reach, and perhaps it should. In our attempts to touch it, we are exploring the concept of individualisation more fully and stretching our ambition. We are understanding what individualisation in training really means to people, and how and where it can truly add value.