How AI is changing education

Education is a key driver of human capital. Countries that develop the skills and talents of their populations enjoy lasting advantages in productivity, entrepreneurship and scientific discovery. For this reason, education policy has always been closely linked to long-term national competitiveness.

Today, however, the global education landscape faces a significant challenge. Artificial intelligence is beginning to transform how knowledge is produced, accessed and applied, raising fundamental questions about how education systems can prepare students for an AI-driven economy.

AI threatens to undermine the development of core cognitive skills. Over the past two decades, the rise of the internet – particularly social media on video- and image-based platforms – has already raised concerns about declining attention spans and weakening reading and analytical abilities among younger generations. AI tools that instantly generate text, solve problems or complete assignments risk intensifying this trend by supplanting the intellectual effort required to develop reading, writing, numeracy and critical thinking skills.

The same technology also holds the potential to significantly improve educational outcomes if used intelligently. AI could personalize learning, adapt exercises to individual abilities and provide continuous feedback to students and teachers. The central issue, therefore, lies in determining how AI should be integrated into education systems. Used poorly, it could accelerate the erosion of foundational skills; used well, it could substantially enhance human capital development.

Given the novelty of the technology, policymakers should remain open to the possibility that different age groups and educational contexts may require very different approaches, including, in some cases, limiting or even avoiding digital tools altogether.

AI’s transformative potential in education

Modern education systems face a persistent structural problem: Standardized teaching must serve a highly heterogeneous student population. Classrooms typically operate with a single curriculum, a common pace of instruction and one teacher responsible for many students. But students differ widely in their abilities, motivation, interests and prior knowledge. They also differ in how quickly they absorb new concepts, the amount of repetition they require and the forms of explanation that help them understand difficult material. Some progress quickly and become bored when instruction moves too slowly, while others struggle to keep up.

Debates about education policy often overlook this tension. Student diversity is frequently praised for building certain social skills but it also creates a fundamental challenge. Students with widely differing abilities, interests and learning speeds must still be taught within the same instructional framework. The ability to address the specific needs of individual students is very limited in the traditional classroom setting.

Artificial intelligence could help address this structural problem by enabling genuinely individualized learning. AI-supported educational tools can tailor exercises and explanations to each student’s unique needs by adjusting the level of difficulty, the pace of learning and presentation style. Students who grasp concepts quickly can move ahead to more advanced material; those who need extra help can receive additional practice or alternative explanations until they fully understand the basics. In principle, this allows students within the same classroom to follow different learning paths while still working within a shared curriculum.

If implemented well, such tools could make the long-standing aspiration of inclusive education more achievable in practice. Instead of forcing all students to learn at the same pace, AI systems could provide individualized tasks, feedback and reading materials tailored to each student’s abilities and interests. Teachers would remain central to the educational process, but their role could shift away from routine instruction and toward guidance and mentorship.

Rather than primarily delivering lectures or administering standardized exercises, teachers could focus more on monitoring progress, supporting struggling students and encouraging intellectual curiosity. AI systems may be effective at delivering standardized explanations and practice tasks, but they cannot replace the human elements of education – motivating students, recognizing individual talents and helping young people develop intellectual discipline and curiosity. Used well, AI could complement rather than substitute the role of educators.

AI may also improve the efficiency of education systems by reducing administrative and repetitive tasks. Automated grading, assistance with lesson preparation and data-driven feedback systems could substantially reduce the time teachers spend on bureaucratic work. This would free up resources for the core purpose of education: meaningful interaction between teachers and students.

Realizing these benefits, however, depends less on the technology itself than its integration into education systems. AI creates new opportunities for individualized learning, but it does not eliminate uncertainty about how education should be organized.

Countries that allow greater institutional experimentation may be better positioned to discover effective models of AI-assisted education.

 

Institutional uncertainty and the need for competition in education

While AI may expand the possibilities for individualized learning, the optimal way to integrate these technologies into education systems remains highly uncertain. AI tools are evolving rapidly, and their long-term effects on learning outcomes are still poorly understood. Different subjects, age groups and educational environments may require very different approaches. In some contexts, AI-assisted learning may enhance understanding and accelerate progress. In others, excessive reliance on automated tools could undermine the development of core cognitive skills.

Despite this uncertainty, education systems often respond to technological change with centralized solutions. Governments and education bureaucracies may try to standardize the use of AI tools across schools through national platforms, uniform digital curricula or detailed regulatory frameworks. Such approaches promise efficiency and administrative control, but they also risk locking entire education systems into a single model before its effectiveness is properly understood. If the adopted model proves misguided, the resulting mistakes may affect an entire generation of students.

In situations where knowledge is limited and technology is changing rapidly, institutional diversity and competition offer an important advantage. Economist Friedrich Hayek famously argued that competition serves as a discovery process. When various institutions experiment with different methods, information gradually emerges about what works and what does not.

Applied to education, this means that schools, universities and education systems should be allowed to explore different ways of integrating AI into teaching and learning. Some institutions may rely heavily on AI-supported tutoring systems, while others may adopt more cautious or limited forms of integration. Over time, successful models can be identified and adapted by others.

This diversity of approaches may prove particularly important because one temptation in AI-enabled education will be to use technology to continuously “facilitate” and simplify learning. Yet education inevitably requires effort. Reading complex texts, constructing structured arguments, solving difficult problems and practicing core skills all demand sustained concentration and repetition.

If AI tools are used primarily to remove these challenges, they risk undermining the very cognitive abilities education is meant to develop. Determining where AI assistance is beneficial and where intellectual effort must remain central will therefore require experimentation and careful observation.

Countries that allow greater institutional experimentation may be better positioned to discover effective models of AI-assisted education. In contrast, systems that rely on centralized planning and uniform implementation risk committing large-scale errors.