AI in Education: Deeper Learning or Faster Outcomes?
Education has always been interested in efficiency. We want teachers to spend less time on administration, students to receive feedback more quickly and learning resources to become increasingly accessible. Artificial intelligence appears to offer all three, often within seconds. It's hardly surprising that schools, universities and education companies are exploring AI's seemingly endless list of educational possibilities.
Yet there is a fundamental difference between completing a task and understanding how to complete it. A student who generates an excellent essay with AI may produce better work without becoming a better writer. Another who receives immediate help with mathematics might solve more problems while developing less confidence in solving them independently.
This raises an uncomfortable question for education systems investing in AI: are we improving the learning process, or simply making its outputs faster and more impressive?
Faster Performance Doesn't Always Mean Better Learning
One of the clearest illustrations comes from a 2025 study published in the Proceedings of the National Academy of Sciences (PNAS). Researchers examined how nearly 1,000 secondary school mathematics students performed when given access to generative AI during practice activities.
The immediate results appeared promising. Students using a general-purpose AI assistant achieved significantly higher practice scores than those without access. However, when the technology was removed during a subsequent examination, those students performed approximately 17% worse than the control group. Students using a specially designed AI tutor, which provided hints rather than simply revealing answers, largely avoided this negative effect, although they did not achieve a significant improvement in the independent examination.
The distinction is important. Both systems helped students complete practice tasks, but the way they provided assistance influenced what students appeared to learn. When AI removed too much of the intellectual effort, stronger immediate performance did not translate into stronger independent understanding.
Well-Designed AI Can Improve Learning
It would be equally misleading to conclude that artificial intelligence necessarily weakens education. Other research suggests that AI can improve learning substantially when its design reflects established teaching principles.
A 2025 randomised controlled trial published in Scientific Reports compared an AI-powered tutor with active-learning classes in an undergraduate physics course. Students using the specially developed AI tutor learned significantly more in less time and reported higher engagement and motivation.
These findings contrast with the mathematics study, but they aren't necessarily contradictory. The physics tutor was designed around educational principles, while the mathematics experiment demonstrated the risks of providing students with relatively unrestricted access to answers. Different learners, subjects and instructional designs can produce different outcomes.
Broader research also offers grounds for cautious optimism. A 2025 systematic review and meta-analysis of 68 experimental and quasi-experimental studies found an overall positive effect of generative AI on learning outcomes. However, the researchers also identified considerable variation between studies, suggesting that educational context, implementation and research design strongly influence results.
This is an important distinction for schools. Asking whether AI improves learning may be too broad a question. We also need to ask which tools help, under what circumstances, for which students and towards which educational objectives.
The Hidden Risk of Outsourcing Thinking
Much of AI's appeal comes from its ability to reduce effort. It can summarise lengthy readings, structure arguments, explain difficult concepts and generate polished responses. In professional settings, saving time is often an obvious benefit. In education, however, the relationship between effort and value is more complicated.
Some intellectual effort is precisely what learning requires. Students develop understanding by retrieving knowledge, testing ideas, recognising mistakes and attempting unfamiliar problems. If AI consistently completes those processes on their behalf, the final product may look impressive while the underlying learning remains limited.
The OECD's Digital Education Outlook 2026 makes a similar distinction. Its review of emerging evidence suggests that general-purpose AI can improve task performance without necessarily producing lasting learning gains. By contrast, educational AI designed around clear teaching principles is more likely to support meaningful learning.
That doesn't mean every use of AI needs to be difficult or time-consuming. Technology can reduce unnecessary barriers, particularly for students who benefit from additional explanations, accessibility support or opportunities for independent practice. But education should distinguish between removing obstacles to learning and removing the thinking that constitutes learning itself.
What Should Education Actually Measure?
If we want to understand AI's educational value, measuring how quickly students complete assignments isn't enough. Neither is asking whether students enjoy using the technology or whether teachers can produce resources more efficiently.
These outcomes matter, but they answer different questions. Efficiency tells us whether technology saves time. Engagement tells us something about the learning experience. Neither automatically demonstrates that knowledge has been acquired, retained or transferred to unfamiliar situations.
More meaningful evaluation might examine whether students can explain their reasoning without AI, apply concepts to new problems or demonstrate understanding several weeks after an activity. Schools should also consider whether AI supports learners who previously struggled or simply gives confident students another advantage.
For policymakers and school leaders, this also means looking beyond adoption rates and productivity statistics. The number of teachers using AI or the hours saved preparing lessons may indicate successful implementation, but they do not, by themselves, establish educational improvement.
The Bigger Picture: Better Learning, Not Just Better Outputs
Artificial intelligence offers genuine opportunities to make education more accessible, responsive and efficient. The emerging evidence suggests that carefully designed AI tutoring can strengthen learning, while unrestricted assistance can sometimes undermine the independent understanding education is supposed to develop.
Neither outcome is inevitable. Much depends on instructional design, learner behaviour, assessment and the professional decisions surrounding technology use. AI is not inherently a shortcut to better education, nor is it automatically a threat to meaningful learning.
The future of AI in education shouldn't be judged by how quickly students can produce an answer. It should be judged by how much they understand when the technology is taken away.
If AI helps students complete their work more quickly and accurately, what evidence would convince us that it is genuinely improving their learning rather than simply improving their results? Let us know in the comments below.
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