India’s students don’t need to study more, they need to study smarter

This article is authored by Akul, founder, CognIQ and columnist on education, AI and learning science.

The problem with exam preparation is not always a lack of effort. Too often, students are working within a system that ignores how memory, attention and mental fatigue actually work.

Indian student (StockPic)

Every year, millions of Indian students prepare for examinations that can shape the course of their lives. From JEE and NEET to UPSC, CUET and other competitive tests, the routine is familiar: long study hours, strict timetables, repeated revision, mock tests and, often, very little sleep.

Yet many students encounter a frustrating experience on exam day. They recognise a question. They remember studying the topic. They may even remember the page on which they read it.

But the answer does not come.

The usual explanation is simple: I should have studied harder.

That explanation is also frequently wrong.

For many students, the real problem is not effort but the way effort is organised. India has built an extraordinarily competitive examination culture, but we spend remarkably little time teaching students how learning itself works.

A timetable can tell a student what to study at 6 p.m. on Tuesday. It cannot tell them whether what they studied last Thursday is still retrievable from memory. It does not know whether they are mentally exhausted, whether a difficult chapter needs another revision, or whether repeatedly rereading the same notes is creating familiarity rather than genuine recall.

Modern learning science has been pointing to these limitations for decades.

Cognitive load theory, associated with educational psychologist John Sweller, begins with a simple idea: working memory is limited. When students try to process too much complex information at once—particularly when tired, distracted or overwhelmed—the effectiveness of learning can decline sharply.

This has an obvious implication for the Indian student who equates the number of hours spent at a desk with the amount learned.

Eight hours of studying are not necessarily eight hours of learning.

A student can sit with a textbook for an entire evening, underline paragraphs, watch lectures and rewrite notes, and still retain surprisingly little. The visible activity of studying and the invisible process of learning are not the same thing.

Memory research points to another problem.

More than a century after Hermann Ebbinghaus first studied forgetting, the central lesson remains relevant: newly learned information becomes harder to retrieve when it is not revisited. The precise rate of forgetting varies, but the principle is clear. Memory weakens over time unless learning is reinforced.

And yet most study schedules are designed around coverage, not retention.

Finish thermodynamics on Monday. Complete organic chemistry by Thursday. Revise modern history next week.

The timetable moves forward whether the brain has consolidated the material or not.

That creates a cycle familiar to students across the country. A syllabus may be “completed” once, sometimes twice, while large portions remain only weakly retrievable. Revision feels productive because the material looks familiar, but recognition is not the same as being able to produce an answer under pressure.

Then, as the examination approaches, anxiety rises and study hours increase. Sleep decreases. Fatigue accumulates. The student responds to declining performance by adding more work—the very strategy that may further reduce the quality of learning.

What makes this particularly unfortunate is that several better approaches are already well established.

One is spaced practise: revisiting material across multiple sessions rather than concentrating all revision into one block. Another is retrieval practise: forcing the brain to recall an answer before looking at the notes, instead of repeatedly rereading them.

There is also interleaving, in which students mix different kinds of problems or topics during practise rather than completing dozens of nearly identical questions in sequence. This can feel harder, but that difficulty can be useful because the learner must repeatedly identify which method or concept applies.

Together, these approaches suggest a very different philosophy of studying.

The goal should not be to ask, “How many hours did I study today?”

It should be to ask, “What can I retrieve today that I could not retrieve yesterday?”

That distinction matters because competitive examinations do not reward the amount of material a student has seen. They reward what the student can recall, understand and apply at a particular moment under time pressure.

There is also a larger issue that deserves attention: cognitive readiness.

Students are not machines with identical processing capacity throughout the day. Mental energy fluctuates. Sleep, stress, previous workload and emotional state all influence the ability to concentrate.

Yet conventional study plans usually treat every hour as interchangeable.

A difficult new concept assigned to a depleted student at 11 p.m. is treated exactly like the same concept studied by a rested student at 8 a.m. From the timetable’s perspective, both count as one hour. From the brain’s perspective, they may be very different learning experiences.

This is where technology—and particularly Artificial Intelligence (AI)—could become genuinely useful in education.

Much of the current conversation around AI in classrooms focuses on generating answers, summarising textbooks or automating homework. But one of its more interesting applications may be much quieter: helping students organise learning around their own patterns of retention.

Imagine a study system that does not simply display today’s chapter list but continuously asks: Which concepts is this student forgetting? Which subjects need retrieval practise today? How much difficult work has already been attempted? Which tasks should be moved because the student’s performance is deteriorating?

Such a system would turn the traditional timetable on its head.

Today, the student is expected to adapt to the plan.

A genuinely intelligent learning system would adapt the plan to the student.

This does not mean algorithms should replace teachers, discipline or hard work. Nor can technology eliminate the enormous social and psychological pressures surrounding high-stakes examinations in India.

But it can challenge a particularly damaging assumption: that whenever students struggle, the solution is simply to increase the number of hours they study.

For decades, Indian students have been told to work harder. Many already do.

Some wake before sunrise. Some attend school and coaching classes before beginning another round of self-study at night. Some sacrifice exercise, recreation and sleep because they believe every hour away from a textbook places them behind a competitor.

We should be careful before telling such students that their main problem is insufficient effort.

The more useful question is whether that effort is being converted into durable learning.

India has spent decades optimising the competition around examinations. The next step should be to optimise the process of preparation itself.

Students do not merely need better timetables.

They need systems built around memory, attention, retrieval and recovery.

Because in education, more effort is not always the answer.

Sometimes the answer is making every hour of effort count.

This article is authored by Akul, founder, CognIQ and columnist on education, AI and learning science.