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

The challenges and principles for governing GenAI are largely similar to those for non-generative AI tools. They follow the OECD AI Principles: transparency and explainability, robustness, security and safety, and accountability. Other OECD standards on privacy and data protection and digital security risk management also apply. The OECD AI Principles recommend that governments “shape an enabling policy environment for AI” and that they “review and adapt, as appropriate, their policy and regulatory frameworks and assessment mechanisms as they apply to AI systems to encourage innovation and competition for trustworthy AI”.

All guidelines from the previous OECD-EI Guidelines remain relevant for GenAI: ethics, safety, privacy and data protection, transparency, explainability and negotiation. The ways to enforce these guidelines differ across countries and jurisdictions, and can for example be embedded in public procurement practices, the publication of guidance or standard expectations.

As part of their digital education governance, most OECD countries have put in place a strong data protection and privacy regulation, which is enhanced for minors, with possible different age thresholds for different types of data. Countries in the European Union have adopted legislation about the marketability and use of AI, giving education developers some obligations before deploying educational GenAI tools. In the case of education, while education objectives and learning must prevail, regulatory frameworks have to factor in incentives for EdTech companies to develop affordable, effective and equal opportunity digital tools and resources for the education sector. International co-operation may help make the education market less fragmented and more harmonised.

This section casts light on some specific issues associated with GenAI such as safety or lack of comprehensiveness of technical algorithmic precision for different contexts (mainly known as “algorithmic bias”).

The first, basic level of transparency is for end users to know that they interact with a GenAI tool rather than with a human, unless it is self-evident. A second aspect lies in how those tools protect the privacy, data and intellectual property of their users. While GenAI tools do usually protect the privacy of the data provided by users in their queries when used for a fee, this is often not the case for their free versions which may typically be used by students, teachers, and education staff (unless GenAI tools are provided by their educational institution). GenAI providers should ensure transparent information on whether the user data and queries are collected or used to further train their models. They should also aim to be as transparent as possible about how the tools operate, the criteria they use to generate their content as well as the datasets on which they were trained.

Jurisdictions and educational institutions that provide GenAI tools to education stakeholders should also have the same level of transparency with end users and be transparent about their own uses. To the extent possible, if they use AI to make or inform decisions, they should explain how the tools are used, and also the methods and criteria used to inform their decision. Transparency may also require social dialogue and negotiation of those criteria with teachers and their representative organisations, students (and their representative organisations when they exist), parents and families, and all other stakeholders. Transparency alone does indeed not guarantee social acceptance of the social tools.

Explainability is difficult to fully achieve with GenAI, even more so than with non-generative AI. Most GenAI systems are proprietary and trained on datasets that are not disclosed, making it challenging to understand the factors that shape their outputs. Educational GenAI tools are built on these underlying models and therefore inherit this opacity. Nevertheless, explainability remains possible at certain levels: some educational GenAI tools are designed to provide information about why and how specific content was generated, including the features considered or the steps taken. Such approaches can help strengthen trust and support responsible use, even if they only offer partial explainability.

Governments share a series of common challenges to ensure the quality, safety and education quality of GenAI tools: safety, data protection, alignment with pedagogy, algorithmic bias, etc. They could work internationally, in collaboration with GenAI companies, to develop international safety expectations for GenAI tools used in education, as is for example the case in England, partially in EU countries through the AI act, or in other countries through their procurement processes. They may work with GenAI companies about their transparency in complying with those expectations.

Used for educational purposes by education institutions, GenAI tools should generate safe and age-appropriate content. This risk is usually much lower with non-generative AI. Beyond GenAI tools used in or for education, non-educational GenAI tools can pose safety risks for students and other stakeholders, for example through new forms of cyber-bullying using their generative affordances in offensive ways. Jurisdictions and education institutions should monitor those risks and adapt education systems’ and institutions’ regulations and codes of ethics accordingly.

GenAI tools also pose greater risks of algorithmic bias. For non-generative AI, the risks often materialise as AI tools not performing equally well for different subpopulations rather than levelling the playing field for all. GenAI tools amplify the risks of presentational bias: images, texts or other content that reproduce views considered as inaccurate or inappropriate (or locally considered as such). Governments and companies may work together to identify different layers of agreement on these so that educational GenAI is aligned with their curriculum and what they consider acceptable views within the education sphere. Higher education institutions may fine-tune GenAI systems so they align with the course content and knowledge base suggested by their academic staff.

Similarly, while all countries may not agree on what “good pedagogy” is, given the variety of contexts, they may still agree on some basics anchored in educational research, including learning science and all its other sub-disciplines, and develop and share expectations for GenAI tools. This may lead to a multi-layered set of expectations that different countries and jurisdictions could choose from.

Students and families as well as teachers need to receive clear guidance about how GenAI tools can and should be used in the learning process. Jurisdictions and educational institutions should consider having a public position about the appropriate and inappropriate uses of GenAI, following a dialogue with their students, teachers, staffand their representative organisations. Education technology is a socio-technical system, and a public position based on social dialogue will also facilitate further agreement on the responsible uses of GenAI by students as well as teachers. This echoes the OECD AI Principles recommendation to “work closely with stakeholders to promote the responsible use of AI”. These public positions should be updated regularly given the evolution of those technologies.

This includes providing clear and plain-language information and supports to parents and caregivers, recognising their crucial role in guiding children’s use of GenAI outside of school. Those supports may include glossaries, guidance documents, and practical examples to help families understand the possibilities and limitations of GenAI tools, the associated risks of overreliance, hallucinations or biased outputs, and the importance of balancing independent learning with (hopefully teacher-guided) GenAI-assisted tasks. Engaging parents in this dialogue can contribute to more coherent expectations across school and home environments, and support more equitable and responsible use of GenAI among students. Some countries such as India also provided parents with GenAI applications that support them in inventing new stories and reading to their children, which is typically associated with stronger learning outcomes.

Many countries provide such guidance, which should be regularly updated based on emerging evidence on the actual use of and research on GenAI in education.

The principle of having a human alternative or final resort is as important for GenAI as for non-generative AI. GenAI tools should play a limited and clearly defined role in decision-making, especially in situations where their use may affect individual’s rights (for example, assessment, certification, admissions, disciplinary matters, or comparable tasks). While GenAI tools could be the first source of information for some questions or problems, providing the convenience of being available at any time of the day while humans may not be, users should be able to ask humans to provide those answers when they feel GenAI’s are unsatisfactory. In the (rare) event that GenAI tools are used to make decisions, those affected by those decisions or recommendations have to be able to challenge them and get humans back in the loop, as noted by the OECD AI Principles.