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

GenAI tools offer different opportunities to support teachers to prepare their lessons, to adapt and personalise the curriculum to their classes, to design assignments and exams, to help them give more elaborate feedback to their students or even to support them in marking the work of their students. They can use educational GenAI tools to support student practices, to foster collaborative learning in the classroom and integrate their use by students in the learning scenarios they design. GenAI tools can also support teachers to undertake administrative tasks, such as documenting their teaching, liaising with parents and students, etc. At all levels of education, teachers have already embraced GenAI tools for these purposes to some extent. Their use could be a means to save time that hopefully allows them to spend more time and effort on higher level aspects of their teaching, such as building personal relations with their students, making their assignments or feedback more personalised or elaborate. In some contexts, GenAI could be used to support tutors in real-time, enhancing the quality of feedback of less experienced tutors.

Students can also enhance their acquisition of knowledge and skills using GenAI as a tool. General-purpose GenAI tools, notably LLMs, provide access to mainstream knowledge in a pre-digested format. Even if the GenAI output can be “hallucinated” at times, and thus be inaccurate, students can ask follow-up questions and make personalised queries. Students already use general-purpose GenAI tools as a search engine replacement and to get some concepts explained to them. They can receive feedback on their ideas and successive drafts, brainstorm, get title ideas, get their texts copy-edited, receive stylistic suggestions, get typos in their computer programmes identified, get ideas to solve a maths problem, etc. If they have access to an educational GenAI designed to support their learning rather than merely perform tasks, they can be scaffolded in their learning, as would be the case with non-generative educational AI.

Finally, at the system or institutional level, GenAI can be used to develop standardised assessment items to evaluate students’ work, to expand automated assessments to oral tasks, to automatically tag open educational resources and make them more easily discoverable by teachers and students, or to provide administrative assistance to students and teachers.

These possible benefits come with risks though. The most important one has already been mentioned: if students (or teachers) get the entirety of their learning (teaching) tasks done by a general-purpose GenAI tool, they may be able to produce the required output but risk not learning anything in the process. Students (or teachers) need to even more actively engage in their learning when they use GenAI tools. For example, as writing is a thinking task leading writers to clarify their intentions and organise their thoughts, asking an LLM to write without personal intellectual engagement in the process may hamper students’ thinking (in addition to their writing skills). This is what was referred to as skill atrophy in the OECD-EI Guidelines: the lack of continuous use of certain skills may lead to their decline and to the (collective) inability to perform certain task without AI tools. In the case of student learning, a more immediate risk is that knowledge and skills are not acquired. The risk is amplified with general-purpose generative AI as educational AI and GenAI tools are intentionally designed to support specific aspects of learning (and can usually not be used to perform students’ assignments).

At what age is it appropriate to use GenAI tools? To do what? And what kind of AI, general-purpose or educational? These are difficult questions to answer, as it really depends on what GenAI tool is concerned and for what purpose. For example, many non-generative educational AI tools such as intelligent tutoring systems are used in elementary school for the learning of mathematics and domestic language; those systems, which have been proven to be effective for learning, have become “hybrid” and can now utilise educational GenAI interfaces to make the tools more conversational. To the extent that they are effective and safe, there is no reason to discourage their use in primary education. However, should elementary school pupils be encouraged to do their homework, if any, with a general-purpose GenAI? Probably not. Most developers forbid the use of those tools below 13 in principle, but new models are being released for younger children. Is it possible to use general-purpose GenAI tools pedagogically with younger children? Possibly yes. GenAI will be increasingly part of the fabric of the digital content to which younger children are exposed, and having a teacher work with children on the AI generation of images to nurture a class discussion can be a sound pedagogical activity for example. While there is still little evidence on the inherent age-appropriateness of general-purpose tools, for learning this may relate to users’ level of proficiency (and desired proficiency) in the tasks performed with GenAI tools as much as to their age. Some level of mastery of the tasks delegated to GenAI tools (foundational knowledge and skills) is necessary to exercise critical thinking and judgment on the quality of their outputs, a prerequisite to learn something from using it. This is true whatever the student’s age.

Another important risk is the weakening of the essential human relation in education. While GenAI (and AI in general) present many opportunities for the personalisation of education, if not properly used GenAI could also lead to more impersonal education, with an increasing number of tasks automated or delegated to AI chatbots with almost automatic human endorsement. Learning is a social activity where students acquire knowledge about a subject but also learn about social interactions at school and in society, skills that will be essential for their future working life and that need to be mediated by humans (teachers and other adults). Impersonal education can be desirable for learning in some cases though, for example when students are anxious to speak a foreign language and prefer to practice with a conversational agent. But an education in which both students and teachers outsource their tasks to GenAI tools may become the ruin of education, turning an enterprise of human development into a cycle with no human engagement.

The use of (general-purpose) GenAI may enhance performance in a study task without leading to knowledge acquisition. Learning requires the active and often social work of students’ mind. Much of the art of curriculum design and pedagogy is to create tasks that will elicit student reasoning and give them opportunities to advance it. None of this can happen if the student avoids cognitive effort or thinking. For example, translating a text into a foreign language with an LLM is unlikely to correlate with improved mastery of the foreign language. This has also been shown with the use of general- purpose GenAI as an adaptive learning system in mathematics or for improving one’s written text. What is in principle the product of learning (the proficient achievement of a task) may no longer reflect actual learning if GenAI is involved. This does not imply that one cannot learn by using general-purpose GenAI tools, or that educational GenAI tools have the same effect, but the use of GenAI tools should be guided by clear pedagogical objectives or educational purposes. Jurisdictions may consider informing and sensitising parents and caretakers to this risk of using general-purpose generative AI tools when supporting their children, as well as the students themselves.

Education institutions aim to develop skills that are socially valued for humans, whether these skills become GenAI capabilities or not. The OECD has developed “AI capability indicators” designed to assess and compare AI advancements against human abilities. They include creativity, critical thinking and metacognition, and show that AI capabilities are progressing in these valued human skills. Even if GenAI could outperform humans on these skills, it would not imply that humans should stop valuing and developing them. For example, GenAI can be used to support the creative process and the acquisition of creative skills. However, its use has also been shown to reduce collective originality, probably because GenAI provides its users with similar suggestions in response to similar requests. Nurturing individual creativity with no use of GenAI may thus remain socially beneficial.

Similarly, because GenAI can generate incorrect or not locally appropriate answers, it also helps identify different perspectives on a subject: teachers may use GenAI outputs to help develop their students’ critical thinking. However, students must also develop critical thinking with no GenAI and learn to think like scientists and navigate uncertainty and ambiguity, make and test assumptions, and learn to weigh the strength of evidence.

Requesting students to provide personal insights on a subject, to research and work on topics they are personally interested in or to try to understand how they and others feel (empathy) can help mitigate the risk of overreliance on GenAI tools as well as nurture personal expression, independent thinking, appreciation of learning and of the world, and make educational tasks less standardised and more engaging for students.

As experts using general-purpose GenAI tools know, GenAI outputs vary from excellent to irrelevant – and the reason they know is that they are expert. Students are not experts yet, and they should reach a certain level of expertise in what they are studying to know when GenAI outputs should be discarded, improved or used. Expertise is correlated with age, especially so when students are younger, but proficiency in what one is supposed to learn trumps age when it comes to the use of GenAI. In some instances, teachers may ask students to use GenAI as a scaffold to help them with aspects of the task they do not master yet and to focus their attention on the acquisition of skills or knowledge that they are closer to mastering. Foundational knowledge that must be mastered for a given field should be assessed with no GenAI support. For tasks peripheral to the key learning objectives, enhancing one’s performance with a general-purpose tool is fine (given that the main objective is not about learning the related knowledge and skills). Assessment will also have to pay more attention to learning processes rather than just student products.

Assessing student knowledge with or without GenAI can be done in a controlled environment, either by having supervised paper and pencil exams or by using digital tools that block or document student use of GenAI tools. It is more difficult for tasks that are not meant to be supervised. Beyond the design of assignments that cannot easily be performed by GenAI, several models are possible, such as oral examination, the request of short videos rather than, say, short essays, a focus on conceptual understanding rather than practical performances. Alternatively, non-supervised assignments can also be used to develop students’ skills in creating or engaging with GenAI tools.

It is a new responsibility for teachers to guide students in using GenAI from time to time as part of their studies. They should design some learning activities that involve the use of GenAI, whether general-purpose or educational. The younger and less proficient students are, the more appropriate educational GenAI tools are. When using general-purpose GenAI tools, teachers should design tasks that require cognitive effort and work despite their use. Teachers should also support students on how to use these tools: they may request intermediate outputs, prompts, or have access to learning analytics documenting the student use of GenAI.

A productive collaboration with GenAI tools can take different forms. It could be about receiving feedback on one’s work, but also about evaluating GenAI tools’ outputs in a mindful and critical way. While education systems value students’ ability to answer questions and provide solutions, they may now emphasise more students’ ability to ask good questions and elicit good answers, by refining their questioning, identifying incorrect or misleading information, knowledge gaps or missing perspectives in answers, so as to get balanced and satisfactory answers from GenAI tools (and also humans). They may also put more emphasis on the metacognitive skills they are developing.

While the appropriate use of GenAI tools may support the learning of academic knowledge and skills in any subject, students also need to develop a certain level of literacy in GenAI itself as part of their digital literacy. The EC-OECD AI literacy framework describes AI literacy as four competences: engaging, creating, managing and shaping AI. This literacy includes engagement with a variety of GenAI tools, including for non-academic purposes, but also encompasses understanding the basics of how GenAI works.

Preparing students to use GenAI does of course not imply that they should use GenAI all the time. As for any other tool for learning, teachers should provide students with a variety of learning activities, experiences and scenarios, including some with no use of GenAI tools (or technology), and ensure diversity and balance in learning activities. This is essential for student wellbeing and engagement.

Like their students, teachers should exercise their evaluative judgment and critical thinking on what GenAI tools produce and ensure the produced contents are appropriate for their students in their given context. These time-saving tools are useful to provide teaching ideas, to contextualise courses or personalise them to specific classes, but they may also include inaccurate knowledge, misunderstandings of the specific learning contexts, etc. For example, while teachers should mobilise their knowledge of how students have and have not been progressing in the classroom to decide which topics need revisiting as a priority and which do not, they could use a GenAI tool to generate warm-up activities bringing these topics together. Ideally, if available, teachers should use educational GenAI tools that are aligned with their course contents and curriculum rather than general-purpose GenAI tools.

Providing feedback and assessing the quality of student work is time-consuming. However, assessment is not an administrative task but an essential part of teaching and learning. While technological support can help, especially when teaching many students, teachers’ ability to provide personal feedback and motivate their students is bound to their personal knowledge of students’ work, their strengths and weaknesses, and a good understanding of their classes.

GenAI tools open new perspectives for student work assessment. For example, they ease the assessment of assignments and exams with rubrics that include multiple explicit assessment dimensions and proficiency descriptions. They also allow for the generation of more detailed feedback, which is sometimes found more useful than human feedback (at least when the recipient does not know the source). While teachers can take advantage of those new possibilities, and occasionally automate assessment tasks or subtasks, they should continue to regularly and primarily assess their student work themselves and always check and take responsibility for any mark that could have a stake for students’ future. The design of GenAI assessment tools could for example ensure that feedback or marks are actively validated and thus facilitate teacher agency. GenAI can also be used by students to receive immediate feedback on their work when instructors are unavailable, but this cannot replace the professional human feedback of teachers. The social dimension of feedback, that is, the knowledge that another person is engaging with the student work, may change the way students react, feel, and behave when they produce it. The authenticity of feedback is not only important from the student perspective, it is also a professional and moral imperative for teachers.