OECD Digital Education Outlook 2026: Exploring Effective Uses of Generative AI in Education

The emerging evidence on GenAI highlights its potential to improve the quality and effectiveness of education. It also demonstrates that it carries risks for student learning and for the professional development of teachers. GenAI appears more disruptive than non-generative AI because students have access to general-purpose GenAI platforms. They can and do use them at home to perform their educational assignments. Even if general-purpose GenAI tools are not used in education institutions, their availability outside of school would challenge current educational processes. As a result, education stakeholders should consider how education systems can leverage and/or adjust to GenAI tools.

Many countries have included digital skills, including GenAI literacy, as part of their curricular objectives. Students should acquire GenAI literacy over the course of formal education, mainly to prepare for the labour market and for societies where GenAI will likely continue to play an important role. In some domains, such as computer programming, students’ employability depends on their ability to use GenAI to code, even though understanding the core concepts and principles of programming remains a must. Digital content will also increasingly incorporate AI-generated content, hence the importance for all to have some understanding of how GenAI works.

The acquisition of knowledge, skills and attitudes in various domains remains crucial to young people’s education, including reasoning, critical thinking, creativity, empathy, curiosity and judgment. While these skills can be acquired with no technology, GenAI could be leveraged by teachers and students for this purpose. Current evidence shows that educational GenAI tools aligned with educational knowledge and science can lead to better learning, but also that teachers can use general-purpose GenAI effectively if they embed it in a clear pedagogical strategy. In that context, GenAI tools could be used at any stage of the educational process, provided the GenAI tools are well designed or used with sound pedagogical purpose.

GenAI tools can also be used or designed to support teachers and other educational staffsuch as teacher assistants or study and career advisers. While several studies show that they can improve their productivity, allowing them to spend less time on some tasks with the same quality of output, they face the same risks of cognitive offloading and lack of learning as students. Research and development on educational GenAI is exploring how GenAI tool can maintain the users’ autonomy, professional learning and sense of responsibility in the final output. This can take different avenues, from co-creation with end users following the “human-centred design” approach to ensuring that teachers or other educational staffcan adjust the tools to their local context and objectives.

The provision of formative feedback to students, a crucial but time-consuming task for teachers, is a good point in case. Current research shows that feedback generated by GenAI, while often not as reliable and consistent as feedback provided by non-generative AI, matches or surpasses human feedback, given its own strengths and limitations. Still, most studies show that students find human feedback more meaningful, trustworthy and motivating. In this context, using GenAI to assist and complement teacher feedback, while they assume full responsibility for this feedback may be the way forward.

Understanding of what makes educational GenAI tools effective, and how this effectiveness compares to non-generative AI tools, is just emerging. Similarly, knowledge about the effective integration of general-purpose GenAI systems in teacher-designed learning scenarios is nascent. Several country initiatives will provide new knowledge on possible approaches (Box 1.2), highlighting the importance of international co-operation and educational research in this area.

Current research on the use of GenAI in education is still limited and could be strengthen by research investment and international co-operation. For example, most current results are based on very short interventions rather than a continuous, repeated use of GenAI tools over longer periods of time.

When considering how to effectively use GenAI in education, here are some key take-aways to consider:

• Successfully performing an educational task with GenAI does not automatically lead to learning; • Acquiring and demonstrating foundational knowledge and skills in key subjects without the use of general-purpose GenAI remains key; • GenAI tools, whether educational or general-purpose, should be used within learning scenarios intentionally designed by teachers to achieve specific learning goals; • When using GenAI tools, teachers and other education staffmust continue to exercise their professional judgment and remain responsible for the quality of the output by evaluating, modifying or endorsing the AI-generated output; • GenAI developers should design education-specific GenAI tools based on educational research and pedagogical knowledge, and involve teachers as well as other stakeholders such as students or parents or teacher unions, as appropriate, in the design process; • International co-operation on scientific research to assess the impacts of well-defined pedagogical uses of GenAI tools on students’ and teachers’ learning and well-being will help leverage these tools in an effective way.

Beyond issuing strategies and guidelines (see Annex 1.A), countries have a variety of innovative projects to support the adoption and use of GenAI in school. While information about the actual use of GenAI in school is still limited, expert guesses note that the adoption of GenAI in the classroom mainly takes places through pilot projects. These pilots are used to explore different uses of GenAI in school and test the pedagogical value of GenAI tools, identify their possible shortcomings and build institutional capacity before scaling.

These pilot projects typically involve collaboration processes, sometimes within country, across countries, or with companies. Across Europe, for instance, many education authorities participate in regional, national or international AI-related projects. They may also involve public-private partnerships. Two types of projects can be highlighted: some focus on the development of educational GenAI tools for education; others focus on the use of these tools in school (usually after some development).

As an example of the actual introduction of educational tools in school, Korea has licensed and made available GenAI- powered tutors to teachers and schools as AI digital learning materials that work like regular intelligent tutoring systems, allowing students to have adaptive practice in several subjects, and teachers to receive feedback on students’ possible misconceptions. Such features include adaptive explanation, automated feedback and interactive dialogue aligned with the national curriculum.

With its AI Leap programme implemented in 2025-26, Estonia aims to explore the use of GenAI in upper secondary education, with a system-level approach combining infrastructure, curriculum development, teacher training and partnerships with technology providers. The programme has made available general-purpose GenAI tools to all teachers and will provide free access to high school students to LLM chatbots that are configured to be educational and follow education research principles (and also to stick to Estonian as the language of interaction).

In Greece, selected upper secondary schools pilot the use of ChatGPT Edu through the OpenAI for Greece partnership (launched in 2025). The project includes teacher training and monitoring of the pedagogical impacts of using GenAI.

Other countries focus on the introduction of teacher- and school-facing tools. Slovakia pilots AI assistants for lesson planning and assessment. Finland tests GenAI applications primarily for teacher support and feedback. Japan, Canada and Australia conduct subnational pilots focused on writing support, feedback generation and workload reduction. France develops a “sovereign AI” for education that will support teachers for lesson planning as well as a Chatbot that will provide generic answers on human resource management questions to its 1.3 million teachers, allowing humans to focus on individual cases.

In the United Kingdom (England), the Department for Education’s “content store” consolidates curriculum guidance, lesson plans, and anonymised pupil assessments to support the training of AI models, enabling the development of accurate, high-quality, and legally compliant educational GenAI tools tailored for English schools. Safety expectations for GenAI tools were also developed, providing developers with a clear set of expectations that can facilitate adoption by schools.

In the Netherlands, the National Lab on Artificial Intelligence (NOLAI) co-designs and develops educational GenAI tools (among other AI tools) for the education system through a partnership between government, academia, industry and schools (Molenaar and Sleegers, 2023; Topali, Ortega-Arranz and Molenaar, 2026).