Education and AI: 79% of Students Use AI, 47% Supervised

A gap exists between students' adoption of AI and the guidance provided. Experiments conducted with several European countries highlight common priorities, while early signals of usage by teachers coexist with areas of uncertainty that call for ongoing evaluations.

Evaluate Before Expanding: Evidence and Uncertainties Highlighted

Measuring the effects of AI on knowledge, independent thinking, and retention is presented as dependent on sustained evaluation. The tools cited to shed light on these effects include surveys, program monitoring, and consultations, aimed at documenting the experiences and obstacles faced by participants. Initial feedback mentions adoption by teachers and reported time savings as encouraging early signals, but these do not constitute proof of impact on learning. Observed government actions serve to prioritize the research to be conducted, while reminding that these are early experiences rather than a directly transposable model. The choices made today could influence how we learn, work, and participate in society, and a commitment to robust evidence is proposed as a condition for a deployment that expands educational opportunities.

ThinkYoung Reveals a Gap Between AI Usage and Guidance

A gap exists between the usage of AI and the guidance of practices. 79% of surveyed students say they use AI for their homework or projects, while 47% report having received instructions on this matter. This guidance gap fuels a recommendation: that governments provide increased support to educators and set clearer expectations for students.

What Partnerships Show in EMEA Countries

Collaborations with governments and educational systems in EMEA, citing Estonia, Slovakia, and Greece among the examples, aim to explore how AI can support teachers. Feedback from these contexts highlights three general lessons: the need for strong public leadership to provide clear direction, investment in teachers' skills and confidence, and practical support aligned with local curricula and classroom needs. Implementation varies by country, and these principles are presented as a useful foundation for responsible and large-scale integration.

Action Priorities: Access, Co-Designed Literacy, and Evaluation

Three common priorities structure the work: equitable access to cutting-edge AI tools, co-designed AI literacy programs, and continuous measurement adapted to local languages, curricula, and institutions. The framework displays support for educators, the development of local capacities, and rigorous evaluation. Five themes organize the lessons that emerge: clarifying educational objectives, preparing educators, anchoring local partnerships, promoting critical AI literacy, and relying on credible evidence.

Teachers at the Center and OpenAI's Positioning

As technologies advance, the role of teachers is strengthened rather than diminished, hence the call to equip schools and decision-makers with AI skills and the confidence needed to make decisions about usage. OpenAI asserts its mission to make general artificial intelligence beneficial to the greatest number, placing the flourishing of learners at the heart of this ambition. The organization argues that every young person, regardless of their background, should be able to acquire the skills necessary in a world increasingly shaped by AI, and expresses a desire to contribute to evidence and learn from it, even when it contradicts its assumptions. It indicates that it brings together researchers through the Learning Lab to better understand when and how AI can support educational systems. This positioning is part of a broader framework of reconfiguring educational purposes in the face of AI, also noted in discussions associated with PISA and the OECD.