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

In line with the OECD AI Principles, the OECD-EI Guidelines emphasised the importance of investing in AI research and development. To make AI useful to education, they also called for the involvement of end users at the very beginning of the development process. Teachers and students are typically involved at the piloting/testing stage of the development process, but rarely at the design stage, when the purpose and functionalities of the AI tools are still to be defined. This is the same for GenAI. End users bring practical knowledge and needs that are not necessarily visible to developers or academics, hence the value of true “co-creation”. This section expands the previous guidelines by also highlighting the responsibility of EdTech companies when developing tools and highlights current gaps in educational research about GenAI for which international co-operation would be beneficial.

General-purpose GenAI tools are primarily performance- rather than learning-enhancers. As performance in the education sector is mainly about student learning, GenAI developers and technology companies should develop GenAI tools meant to support it. Those tools can be designed to be used by students, by teachers, or other education stakeholders. Student-facing learning tools should start from and incorporate insights from educational research and pedagogical knowledge while those for teachers should allow them to adjust them based on their pedagogical choices, either before or during teaching. For example, teachers may be interested in tools allowing them to easily increase the level of hallucinations of a GenAI system used by their students to support their critical thinking. The educational orientation of GenAI should undergo impact (or other) evaluations demonstrating their educational value. International co-operation on educational efficacy studies of GenAI use would help advance this agenda. Governments can support companies by making some datasets based on their curriculum and education research available, as is the case in England (United Kingdom) with the development of a “content store” allowing companies to finetune their GenAI models.

Because of their generative nature, GenAI tools bring more safety risks when it comes to age-inappropriate content, content not aligned with the curriculum, with educational research or pedagogical knowledge, etc. As key actors in the advancement of trustworthy GenAI, developers, and EdTech companies are responsible for the quality, safety, and law-enforcement of their tools. While existing datasets reflect past and current societal perspectives, technology companies and governments could collaborate to remedy to the extent possible this technical limitation. They could curate AI training datasets that include a greater variety of perspectives and cultures that are appropriate in students’ and teachers’ local context. This was, for example, the ambition of Apertus, an open-source language model developed in Switzerland. Relevant communities should be involved as appropriate in how their languages and cultures are represented in these new datasets. As some forms of algorithmic bias are impossible to remove, a regular audit and benchmark of educational LLMs could help identify and mitigate them.

A few countries have launched new initiatives to get end users involved before the late stages of the AI tool development process. The objective is to ensure the utility of these tools for education and their subsequent adoption. Governments may encourage partnerships between companies, academia and practitioners. In the Netherlands, the government- funded National Laboratory on AI (NOLAI) brings together teachers and school representatives with academics, EdTech companies and policy makers to design AI tools in a co-creation process. The French ministry of education also made it a requirement of its multi-step public procurement process of AI tools that companies co-create their products with teachers and students through its AI Innovation partnership (P2IA).

On the research side, there is still a dearth of information about what students and teachers do with GenAI tools for educational purposes, what types of tools they use and with what level of satisfaction, how they feel when using them, as well as what jurisdictions and schools make available to them. Governments could co-operate to develop international metrics of availability and use of these tools. Research on the effectiveness of GenAI for learning and for productivity gain is emerging, but most available experimental research currently lacks statistical power and is focused on short interventions that are hardly generalisable to a repeated use of GenAI. Moreover, many research studies on GenAI tools do not provide enough specificity about the pedagogical purpose and context of their use, making it difficult to replicate or scale them when showing promising effects. While educational GenAI tools are being developed, robust research on their effectiveness is needed. All these efforts would contribute to the international knowledge base if providing enough information about context. Internationally coordinated educational research studies may also ease the adaption of the use of GenAI to educational contexts.

Emerging research on the use of (mainly general-purpose) GenAI tools points to the need to change learning and assessment tasks so they are adapted to the availability of these tools. Educational GenAI tools may mitigate those risks and enhance learning, but still need to be meaningfully integrated in teachers’ pedagogical practices. Teachers should be encouraged and given the possibility to initiate or participate in research projects about the educational use of GenAI tools. This does not imply that all research projects have to be practice-engaged though. International sharing of educational practices and evidence on how to adapt educational tasks and pedagogies so that their use enhances (or at least does not hamper) learning will help to more quickly understand and adjust to the digital transformation and seize the opportunities brought by GenAI in education.

While research access to educational administrative datasets is possible, it entails privacy risks. The creation of privacy- preserving synthetic datasets keeping the characteristics of the real datasets without retaining any personal information has now become possible, largely thanks to GenAI. This possibility should be more systematically exploited by educational jurisdictions to broaden datasets for education research. Initiatives from other sectors could be examined for inspiration. The creation of these privacy-preserving synthetic datasets should not be limited to administrative data but also encompass learning data that are typically collected by private companies, at least partly through public contracts.

GenAI is also transforming scientific research: specialised GenAI tools are increasingly available and used to support almost all the stages of research, from literature reviews to experimentation through hypothesis generation. Higher education institutions should prepare future researchers to use these specialised scientific GenAI tools. Governments are advised to also encourage the development of GenAI tools that support educational research specifically and explore new avenues they may open for education research.