Personalisation is FE’s next evolution

For decades, in most classrooms the idea of an ‘average learner’ has shaped how teaching is planned and delivered. But in reality, no such learner exists. Every cohort of students contains individuals with different levels of prior knowledge, confidence, experience and support needs. Yet, as educators we have historically had limited tools to address this. Meaning we deliver the same content, in the same way and at the same pace to all learners.

This presents a challenge. Some learners risk being left behind because the pace is too fast, while others may become disengaged because they are not being sufficiently challenged. As educators, we have a responsibility to support both groups, designing educational experiences to deliver the best outcome for every learner.

The good news is that rapid advances in technology and AI mean our ability to address this challenge is becoming increasingly achievable, at scale. Personalised learning can create opportunities to provide learners with the right level of challenge and support at the right time.

Personalised learning in practice 

Until recently, delivering a high level of personalisation was difficult. Outside one-to-one tutoring, educators simply haven’t been able to scale learning experiences to continuously adapt for each individual learner. However, advancements in AI and data are creating opportunities to change that and allow us to think differently about how we deliver education.

At Kaplan, we’re using the digital footprint of our learners to improve the quality of our education across our courses in real time.

AI helps us to see patterns in how learners progress and engage and, crucially, to act on this in a timely way to give learners maximum opportunity to reach their full potential. The technology means we can do this at scale, making a hands-on approach to learning – which everyone deserves – accessible to all.

Leveraging AI technology and data insights means we can deliver learning experiences that maintain ‘desirable difficulty’ for individual learners – a well-established concept in educational science referring to the productive zone of challenge where real learning happens, far enough beyond what a learner can already do to stretch them, but not so far that they disengage.

And AI is embedded into our learner ecosystem through our TutorBot, a study partner that provides the support of a teaching assistant around the clock. Learners can use the TutorBot to test and develop their understanding, posing questions that encourage active thinking rather than simply supplying answers. Over time it builds a picture of the individual learner – their confidence, their persistence, the way they approach problems – and helps us to adjust the level of challenge accordingly.

None of this removes the need for great educators. Technology enables teachers and trainers to be even more effective by providing better insight into learner needs and allowing interventions to happen sooner and with greater precision.

This means that personalised learning has a real, meaningful impact on students. Our TutorBot data shows a clear behavioural split between learners who use it as a thinking partner and those who attempt to cognitively offload their learning. That distinction is detectable early in a programme, long before assessment scores reveal anything, which means we can deliver human interventions at the point it is most likely to help, rather than waiting until a learner shows signs of struggle in assessments.

Looking forwards 

As educators, we have always owed it to our learners to provide the best possible educational experience. The fundamental foundations of effective teaching and learning haven’t changed, but the tools available to support it have.

As a sector, we should be challenging ourselves, not because providers who don’t adapt are failing their learners, but because those who do will give them something genuinely better.