Meg Knight

 

As universities look for practical ways to improve online learning, AI in education is shifting the conversation from simple personalisation to something more precise: learning experiences that respond to each student’s goals, context and needs. Precision learning offers a way to make teaching more relevant at scale, helping institutions strengthen engagement, support learner outcomes and preserve the human expertise that great education depends on. 

My sons regularly come home from school with the same complaint: “It’s boring. When am I ever going to use this?” 

Recently, one of them was learning about supply and demand in economics. The concept itself wasn’t generating much enthusiasm. 

But when we started talking about it in the context of his ambitions to start a business — what he might sell, how demand would affect his pricing, and what would happen if supply became constrained — the conversation completely changed. 

He was engaged. He was asking questions. Suddenly, he was full of ideas. 

The content hadn’t changed, the relevance had. 

It reminded me of something from my own time as a primary school teacher. 

I knew my students. I knew their interests, their hobbies, what excited them and often what would make a concept click. That knowledge allowed me to make learning more relevant to the individual child. 

Great teachers have always done this. The problem is that it has historically been very difficult to scale across whole universities. And this is especially true when students are fully online or studying from somewhere else in the world.  

AI potentially changes that equation. 

From personalised learning to precision learning with AI 

I recently came across the concept of precision learning, described in this EDUCAUSE article, and it captures something I think is increasingly possible. 

Rather than simply personalising content, what if we could use AI to first help us to understand a learner’s goals, prior knowledge, interests, progress and context? And what if we could then use those signals to make learning more relevant at the moment it matters most? 

Two students could be working towards exactly the same learning outcome but encounter different examples or explanations along the way. 

Supply and demand might be explored through entrepreneurship for my son. For someone else, it might be concert tickets, gaming, housing or something happening in their workplace. 

The learning outcome and academic rigour doesn’t change, but the context does.  

How AI can scale relevance in online higher education 

This matters because learners are investing significant time, money and effort in education and understandably expect value in return. 

The 2026 Lumina Foundation-Gallup State of Higher Education research found that 73% of US adults believe earning a degree is as important today as it was 20 years ago. Yet only 25% of adults without a degree believe quality, affordable post-secondary education is accessible to most people. 

Institutions are simultaneously being asked to improve learner outcomes while operating within significant resource constraints. 

So the question I find interesting is: 

Can we use AI to scale the kind of personalisation that great educators have always provided, without needing to scale human resources at the same rate? 

The opportunity goes well beyond generating content. AI can potentially help recognise patterns in learner behaviour, identify gaps in understanding and adapt examples, practice and support. A 2025 systematic review of 125 studies found AI-enabled personalised learning already being applied across teaching, learning and assessment. 

The real opportunity comes from combining AI with experienced educators, learning designers and student-support professionals. 

Humans provide pedagogical judgement, empathy, creativity and an understanding of when intervention really matters. AI can help us recognise and respond to individual needs at scale. And personalisation shouldn’t mean creating an educational bubble around every learner. Shared experiences, collaboration, intellectual challenges and exposure to different perspectives remain fundamental to learning. 

Instead, precision learning could help us become much more intentional about where personalisation adds value, what an individual learner needs next, and where human expertise can have the greatest impact. 

For me, that is one of the most compelling possibilities for AI in education. 

Not replacing what great educators do. Helping us do more of it, for more learners. 

Sources 

EDUCAUSE Review. (2025). From personalized to precision learning: Unlocking the next transformation in higher education. https://er.educause.edu/articles/2025/11/from-personalized-to-precision-learning-unlocking-the-next-transformation-in-higher-education 

Lumina Foundation & Gallup. (2026). State of higher education 2026. Gallup. https://www.gallup.com/analytics/644939/state-of-higher-education.aspx 

SpringerOpen. (2025). AI-enabled personalised learning: A systematic review of 125 studies. https://link.springer.com/article/10.1007/s44163-025-00598-x