The Mystery of AI in Education: What Does Personalised Learning Really Mean?

There's a lot of talk these days about AI in education. From conferences to articles, educators are bombarded with messages about how artificial intelligence is going to revolutionise teaching, with terms like "personalised learning" thrown around as though we all have a shared understanding of what they mean. But here's the uncomfortable truth: for many educators, the practicalities of AI and personalised learning are still shrouded in mystery.

We keep hearing that AI will enable us to offer "personalised" learning experiences for students. But what does that actually mean in the classroom? Personalisation sounds wonderful—students having tailor-made experiences that meet their individual needs, adapting lessons to fit each learner’s strengths, weaknesses, and interests. Yet, when we dig beneath the buzzwords, there is a noticeable gap between the promise of AI-driven personalisation and the reality most teachers face. The concept is exciting, but the practice is far less clear.

Part of the issue is that personalised learning is presented as an all-encompassing solution, without a clear map of how to get there. Does it mean every student has their own lesson plan, generated by an algorithm? Does it mean using AI to analyse data and identify areas where a student is struggling? Does it mean freeing teachers from marking so they can spend more time understanding each learner's needs? The answer might be all of the above, but the implementation details are fuzzy, and the tools that claim to offer personalised learning often come with a steep learning curve of their own.

It doesn’t help that AI is frequently treated as a black box. Many educators are left wondering how these systems actually work—how do they decide which content a student should see next? How do they measure progress, and are those metrics aligned with what we, as teachers, believe is important for our students to learn? There is a disconnect between the tech industry, which often speaks in terms of algorithms and scalability, and educators, who are deeply concerned with student engagement, equity, and the less quantifiable aspects of learning.

For many teachers, the promise of personalised learning is clouded by these questions. We want to know: how will AI understand the complexities of a student’s learning journey? Personalisation in education is not simply about serving up the right content at the right time. It’s about understanding a child’s motivations, their fears, their curiosity, and their context—things that are difficult, if not impossible, to quantify. If AI is only able to personalise learning at a surface level, by adjusting the difficulty of a maths problem or recommending a different reading, then are we really talking about meaningful personalisation? Or are we just dressing up differentiation in new technological clothes?

There's also the very real challenge of equipping educators with the understanding and skills they need to make the most of these tools. Many teachers don't have the time or training to decipher how AI works, let alone incorporate it into their already packed timetables in a meaningful way. Without clear guidance and support, AI becomes another buzzword—a nice idea that fails to translate into actual practice, leaving teachers frustrated and students underserved.

If AI is to genuinely transform education, we need to demystify it. That starts with clear, accessible explanations of what these tools do, how they work, and how they can be used effectively. It means going beyond the marketing buzzwords and getting into the nitty-gritty details of what personalised learning looks like day-to-day. It means training educators not just to use these tools, but to question them—to understand their limitations, to see where the personalisation might be superficial, and to know when human intervention is essential.

The potential for AI in education is vast, but without clarity, we risk ending up with a system that is driven by technology without truly serving the needs of our students. Personalised learning isn’t about algorithms or dashboards; it’s about creating environments where every student feels seen and supported. If we are to achieve that, then we need more than promises—we need transparency, training, and a clear understanding of how these tools can be integrated thoughtfully and ethically into the learning process.