How can AI enable real learning?

In a randomised study of about 1,000 high school maths students in Türkiye, students using a standard ChatGPT scored 48% higher than classmates without it while they had it. When access was taken away, they scored 17% lower than students who never had it. A 2025 paper in Nature Reviews Psychology makes the underlying point: performance gains are not the same as learning.

Most AI tools were built for work, not to optimise learning. At work, the goal is to finish the task with the least effort. But in learning, that effort is the point: it is what builds the capability. The task gets finished, but the understanding never develops. Researchers call this “metacognitive laziness”: the learner stops planning, monitoring and self-evaluating, because the AI always has an answer.

Learning scientists Robert and Elizabeth Bjork call these “desirable difficulties”: things like recalling an answer from memory, spacing your practice out, or trying a problem before you see the solution. They make learning feel harder now, but they make it last. When AI removes that effort, it can quietly remove the learning the effort produced. A 2026 review of 67 studies names the same mechanism, epistemic friction: without it, “AI-generated fluency can bypass the reflective struggle central to deep learning.”

If the struggle is the learning, the first thing it depends on is the person doing it. Writing in The Atlantic, David Brooks argues that what will set people apart in an AI-saturated world is not how smart they are but their relationship to mental effort. His frame is need for cognition, the trait named and measured by John Cacioppo and colleagues, who reviewed more than 100 studies of it: at one pole, people who read dense books and play hard games for pleasure; at the other, the cognitive miser, who avoids effortful thought where possible. It correlates with intelligence, Brooks notes, but is not the same as it.

The evidence he marshals is largely about what happens when effort is offloaded. An MIT Media Lab team led by Nataliya Kosmyna measured brain connectivity dropping by up to 55% during ChatGPT use; Michael Gerlich (SBS Swiss Business School) found a significant negative correlation between frequent AI use and critical thinking; a Carnegie Mellon team led by Grace Liu found that after about ten minutes of AI-assisted problem solving, people who then lost the tool did worse than those who never had it. A study of endoscopists found precancerous-lesion detection fell from 28.4% to 22.4% once AI was withdrawn. From this he sketches three responses: the Productive Passenger, who lets AI think and stops noticing; the Reluctant Optimizer, who means to resist and gets pulled in; and the Mental Marathoner, who keeps the hard parts hard. His worry is a cognitive polarization between the two ends. His practical rule: ask AI for thinkers, not thinking, and treat it as a brilliant librarian, not an oracle.

In a Harvard physics study, a purpose-built AI tutor designed with proper scaffolding beat in-person active learning by 0.73 to 1.3 standard deviations, two to three times the usual bar for a substantial effect in education research. In the Türkiye study, the standard ChatGPT left students worse off once it was removed, while a guardrailed tutor version avoided that loss entirely. Same models, opposite outcomes, depending on how they were designed.

In a controlled study in a university creative-coding course, students received identical AI-generated feedback on their work. Those told it came from a human teaching assistant ran their code more, wrote more code, and spent more time on later work. They rated the feedback equally helpful either way. The effect on effort was large (d = 0.88 to 1.56). The content was the same.

Education does three things at once: it builds knowledge and skills (qualification), it helps you find your place among others (socialization), and it helps you become someone who thinks independently and takes responsibility (subjectification). AI tools mostly reach the first. They rarely, if ever, address the other two, and those are where a teacher does their deepest work.

An LLM answers your question. Faster work, less learning.

An AI tutor asks questions back, no matter what you actually need. Often frustration and drop-out.

An AI learning companion (Dr Philippa Hardman calls it a “study mate”) remembers where you got stuck and pushes you towards the thinking you avoid. Capability that lasts.

Holmes (UNESCO) asks it directly: if generative AI is this powerful, do we still need to learn? His answer is yes, and the list grows: on top of what we wish to learn, we now need to learn AI’s profound limitations, its impacts on human rights, social justice and the environment, and “perhaps most importantly, [to] learn how to think… critically.” The World Economic Forum (WEF) keeps the balance: rote memorisation may matter less, but “the process of mastering knowledge continues to develop broader capabilities” such as grit, curiosity, communication and critical thinking, and assessment must evolve to capture them.

The tool: the guardrailed tutor and the learning companion are instructional design built into software: scaffolding, answers withheld, help that fades as you grow.

The classroom: co-design tools with teachers rather than deploying them on teachers, and set tasks that make learners compare, justify and revise what the AI produces. In the 67-study review, that scaffolding is what separated gains from cognitive offloading.

The system: AI only helps where the conditions are ready. As the WEF puts it, “learning outcomes will not be determined by technology itself, but by the conditions in which it is deployed”, and isolated fixes across policy, pedagogy and technology are unlikely to be sufficient.