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AI in higher education: Beyond the hype, where it actually works
AI tutoring works best as a supplement to teachers, helping provide scalable educational support that many institutions struggle to offer on their own.
For years, conversations about artificial intelligence in education have swung between evangelism and alarmism. At one end, sweeping claims that algorithms would personalise learning and reinvent the university. At the other, warnings that machines would erode scholarship itself.
Most academics have heard both versions often enough to be skeptical.
And fairly so. Much of the early promise around AI in education was overstated. Universities were told technology would transform classrooms; in many places it mostly added another layer of tools to manage.
But beneath the hype cycle, something quieter is happening. In some corners of higher education, these tools are proving useful -- not as grand disruptors, but as practical solutions to stubborn problems. Student retention is one example.
Universities have long known many students show warning signs before dropping out. The problem has rarely been awareness. It has been acting in time. That is where predictive systems are beginning to show value, helping advisors identify patterns earlier and intervene before students disappear from the system.
That may not sound revolutionary. But in practice, it can be.
The same is true of tutoring. Some of the stronger evidence around AI in education has emerged here, particularly where adaptive systems supplement (not replace) instruction. The point is not that software can substitute for a professor. It cannot. But it can offer support at scale in ways many institutions struggle to provide.
Perhaps even more quietly, universities are finding value in the operational layer. Faculty overwhelmed by administrative load do not need futuristic assistants; they need help with repetitive work. Drafting feedback, managing scheduling, sorting routine queries -- these are mundane tasks, but reducing them matters.
This is where much of the practical promise lies. Not in replacing teaching, but in giving educators more room to teach.
Of course, none of this resolves the hardest question confronting campuses right now: academic integrity. Generative AI has unsettled assumptions about assessment faster than universities have adapted. That tension is real. Many institutions are still responding with policies built for an earlier era.
But some are moving in a more interesting direction. Rather than treating every assignment as a policing exercise, they are rethinking assessment itself -- more oral examinations, more applied problem-solving, more work done in public, collaborative or iterative settings.
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That may end up being one of AI’s more unexpected contributions: forcing universities to ask what, exactly, they are trying to measure.
The institutions getting this right tend to share a common instinct. They are not chasing every new feature. They are starting with specific problems -- student support, feedback bottlenecks, assessment design -- and asking whether a tool helps.
That is a very different posture from adopting technology for its own sake. It is also a more honest case for AI in higher education.
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Because the strongest argument for these systems was never that they would transform education wholesale. It is that, used carefully, they can improve parts of it. And this distinction matters.
Education is still shaped by human judgment, intellectual friction, mentorship, and the unpredictable moments that happen in a classroom when ideas click. No software replaces that. Nor should it.
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What technology may do, at its best, is create more capacity for those moments. For higher education, this is less dramatic than the hype promised. But it may be far more important.
(This article is written by Sumesh Nair, Co-founder, Board Infinity)
