Personalised Learning: Can AI Deliver on the Promise?

The idea of personalised learning has captivated education for decades. As early as the 1960s, experts envisioned computers tailoring lessons to meet the specific needs of individual students. This vision—dynamic, adaptive, and responsive—has been revisited time and again, with each wave of technological progress reviving hopes that it might finally become a reality.

Now, generative AI has taken centre stage in this narrative. AI systems are being lauded for their ability to analyse performance data, predict learning gaps, and provide tailored recommendations at a scale never before possible. The potential seems enormous, but the promise remains fraught with challenges. Personalisation, as envisioned by many AI proponents, often reduces education to patterns and probabilities, overlooking the complexity of human learning and the richness of individual experience.

At its core, AI-driven personalisation relies on categorisation. These systems detect trends in data, grouping students into profiles based on measurable indicators like test scores, engagement levels, or response times. While this approach can offer useful insights, it is inherently reductive. A student flagged as “struggling” after underperforming on an assessment might be routed into remedial content, but without any understanding of why they struggled. Were they unwell that day? Experiencing external stress? Simply disengaged from a poorly designed task?

The danger of this reductionism lies not only in misinterpretation but in its lasting effects. When students are labelled—whether as high achievers or underperformers—those labels often shape their opportunities and, eventually, their outcomes. AI systems risk creating rigid pathways based on incomplete data, reinforcing the very inequities they are meant to resolve. Personalisation, instead of opening doors, can become a mechanism for limiting potential.

More troubling is the way these systems prioritise efficiency over nuance. AI doesn’t seek to understand why a pattern exists; it merely acts on the pattern itself. For students, this means that their learning journey is defined by what is easy to measure, rather than by the deeper, harder-to-quantify aspects of education. A lesson tailored to a student’s recent test performance might address immediate gaps, but it won’t necessarily engage their curiosity or encourage critical thinking.

This focus on measurable outcomes risks making education transactional. Algorithms recommend tasks, adjust content difficulty, and flag disengagement, but they can’t address the underlying reasons for a student’s struggles or triumphs. The relational aspects of teaching—the moments when a teacher intuits what a student needs based on subtle cues—are lost in this system. And as AI takes on a greater role in decision-making, the richness of these human interactions risks being eroded.

The limitations of AI-driven personalisation also extend to the classroom as a whole. By reducing students to data points, these systems can inadvertently undermine the broader, collective experience of learning. Education is not just an individual pursuit; it is a shared journey where peers inspire, challenge, and support one another. When personalisation is driven by algorithms, there is a risk of isolating students into narrowly defined tracks, fragmenting the communal fabric of education.

For all its technical sophistication, AI remains incapable of understanding the full context of human learning. It can categorise, recommend, and predict, but it cannot interpret the complexities of a student’s life or the nuances of a classroom dynamic. This is not a technological failing; it is a fundamental limitation of systems designed to process patterns rather than people.

The persistent allure of personalised learning stems from a deeply human aspiration: to create an education system that meets each student where they are, nurturing their growth in a way that feels uniquely tailored. But achieving this requires more than algorithms and datasets. It demands an approach that prioritises relationships, curiosity, and the unpredictable nature of human development.

Richard Foster-Fletcher ?? (He/Him) is the Executive Chair at MKAI.org | LinkedIn Top Voice | Professional Speaker, Advisor on; Artificial Intelligence + GenAI + Ethics + Sustainability.

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