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

This report examines generative AI (GenAI), a transformative technology that brought artificial intelligence into the public spotlight, including for students and education policymakers, following the launch of OpenAI’s ChatGPT in 2022. Unlike earlier educational AI systems, GenAI is available and used by students outside of educational institutions, with or without the blessing of teachers, school leaders and policymakers. This presents both significant opportunities and complex challenges for education. After clarifying what is meant by GenAI, this chapter gives an overview of the uptake of GenAI among OECD populations, including students and teachers. It then provides a summary of the knowledge and information in this OECD Digital Education Outlook 2026: research evidence on the effects of GenAI on student learning, examples of what educational GenAI could look like, and possible uses to improve workflows at the institution and system levels.

Generative AI in education

What is generative artificial intelligence?

GenAI is a subset of AI focused on producing new content such as text, pictures, videos, songs, mathematical equations, computer programmes, typically in response to a question or command (“prompt”). These outputs are generated based on large volumes of training data. To do this, GenAI relies on advanced machine-learning techniques, such as neural networks based on transformers (notably Generative Pre-trained Transformer (GPT)), embeddings, tokens, etc. Most people have experienced GenAI via chatbots based on large language models (LLMs) such as OpenAI’s ChatGPT, Google’s Gemini, Microsoft’s Co-pilot, Anthropic’s Claude, Mistral’s LeChat or Deepseek’s Deepseek.

In contrast, non-generative AI systems mainly produce predictions, classifications, recommendations, and ratings, for example for movies, books or other products and services. While they may use similar techniques as GenAI, their primary goal is to identify patterns and relationships in vast amounts of data, rather than create new content. Those AI systems are sometimes referred to as “rule-based”, “predictive”, or “good old-fashioned” AI. Despite often being less visible to end users, these systems are still powerful and have a variety of uses, including in education. They are embedded in assistive technologies, for example for students with special needs, used to adapt learning to personal needs within intelligent tutoring systems, to score assessments or to predict whether students are at risk of dropping out (OECD, 2021).

An important distinction should be made between AI tools that are general-purpose and those that are specialised (in our case, mainly educational): general-purpose systems are versatile and designed to serve many purposes, including educational ones, whereas specialised educational tools are designed for educational purposes only (see Table 1.1).

What is so special about general-purpose GenAI tools?

General-purpose GenAI tools often provide pertinent and contextualised answers to questions, with the ability to clarify and ask follow-up questions. These capabilities were not possible with earlier (non-generative AI) natural language processing. They are trained on massive data sets that exceed what humans could retrieve manually. Moreover, they are flexible and can be applied to many different subjects. Contrary to most educational AI, general-purpose GenAI tools usually offer free versions, enabling students and teachers to use them even if they are not provided by universities or schools, assuming they have an adequate device and connectivity. Even offline, small language models can run, albeit with lower performance (Isotani, 2026).

A series of well-known shortcomings are also specific to current GenAI systems and inherent to its technology. Because they are based on probabilistic models, they can “hallucinate”, that is, produce a plausible but wrong answer or fabricate details of an output (see OECD, 2024, chapter 2). They usually do not generate consistent results over time. For example, repeating the same task several times will yield (at least slightly) different answers or productions, which is sometimes a problem. This is due to regular system updates and to their probabilistic nature. As they are trained on available datasets, their answers and other productions tend to reproduce the views and perspectives represented in those datasets, which are overwhelmingly based on English-speaking (and Western) cultures. For example, unless prompted otherwise, they will typically use Western names or examples in their production. In addition, despite appearing intelligent, GenAI tools do not “understand” the input they process or the content they generate. As a result, their outputs typically require human supervision and scrutiny, often more than specialised, non-generative AI systems.

While beyond the scope of this report, GenAI also comes with a series of societal challenges. Many observers are concerned by its environmental footprint, though this is still difficult to measure and compare with other digital technology. The dissemination of AI-generated information and data may decrease the quality of future generated content (as they enter their training datasets) and amplify some current limitations of our knowledge. This will make critical thinking and the development of metacognitive and higher-order thinking skills even more important than before. The full impact of how GenAI might transform societies, labour markets and economies is still emerging.

What is the general uptake of GenAI?

Most people experience GenAI through chatbots based on large language models (LLMs), such as OpenAI’s ChatGPT, Google’s Gemini, Microsoft Copilot, Anthropic’s Claude, Mistral’s LeChat, and Deepseek’s Deepseek-R1. As of April 2025, based on website traffic data, chatbots dominated public use of GenAI tools, accounting for 95% of monthly traffic to the top 60 GenAI platforms. ChatGPT alone represented about 78% of the monthly visits, down from 89% in April 2023 (Liu, Huang and Wang, 2025). Image-generating tools accounted for 2.4% of GenAI websites’ traffic, video and audio tools for 1.9%, and productivity and business tools for less than 0.5%. While these shares remain small, the use of these systems has grown significantly since 2023, in line with the overall growth of GenAI use. Competition is also mounting across platforms, with newcomers such as Deepseek and Perplexity gaining market share since 2023. Liu, Huang and Wang (2025) show that the use of GenAI tools has both expanded and intensified. For example, between 2024 and 2025, the number of unique users of ChatGPT grew by 42%, visits per user increased by 50% and the average session duration doubled from 7 to 15 minutes – resulting in the doubling of its traffic (113% growth).

Most of this growth has been driven by users in high-income countries. In 2025, they accounted for 60% of GenAI use (compared to 55% in 2024), against 39% for middle-income countries and less than 1% for low-income countries (see Liu and Wang (2024) for 2024 data). This reflects strong uptake in OECD members as well as accession and key partner countries such as Brazil, China and India. However, it also points toward a widening digital divide based on an adoption and use gap. Part of this gap might be due to measurement issues, as users in low-connectivity regions may not be able to access platforms via the Internet and use versions running offline on their device. Figure 1.1 presents the share of Internet users that used ChatGPT in 2025 and 2024 and thus provides an estimate of the uptake of GenAI tools across populations, acknowledging that averages mask higher usage among younger generations.

Is it common for students to use GenAI?

While there is currently no authoritative comparative data on the use of GenAI by students at different levels of education, several domestic and international surveys provide an initial picture of how widely students use these tools and for what educational purposes.

In Switzerland, a 2024 statistically representative survey of 8-18 year-old students points to a steep difference in use depending on age. Around 8% of primary students stated they used GenAI tools in class, 30% in lower secondary, about half in general upper secondary education, and 40% in vocational education. Use in the home followed a similar age pattern (roughly 9%, 33%, 54% and 40% respectively) (Oggenfuss and Wolter, 2024). Including uses less frequent than at least once a week, about 70% of Swiss general upper secondary students use GenAI, and other Swiss pupils use it with a similar age/school pattern as intensive users.

In Estonia, a national survey of about 16 000 students found that 74% of lower secondary students and 90% of upper secondary students reported using AI tools to support their studies in 2024, with ChatGPT by far the dominant tool (70% of students use it) (Granström and Oppi, 2025). Beyond national case studies, a cross-country European survey of more than 7 000 12-17 year-olds across seven countries (Germany, Greece, Portugal, Romania, Spain, Türkiye, and United Kingdom) saw high use of Generative AI by students. For example, 48% declared having used ChatGPT in 2024, with almost half of them instructed to do so by their teachers (Vodafone Foundation, 2025).

The use of GenAI for higher education students seems to align with the age pattern mentioned above, although statistically representative surveys providing information on this are not yet available. Still, a few studies have surveyed a large number of higher education students (and reweighed their answers to make them more representative). In France, a 2023 study of about 4 500 students reported that 55% of higher education students used GenAI tools (Compilatio, 2023). In 2025, the share had increased to 82% (Pascal et al., 2025). In Germany, a survey of over 23 000 higher education students found 94% used AI in 2025, including 65% daily or weekly (Hüsch, Horstmann and Breiter, 2025). A 2024 international survey of 3 000 higher education students in 16 countries also found that 86% used AI in their studies, including 54% daily or weekly (Rong and Chun, 2024).

Evidence suggests that student use of generative AI has moved rapidly from marginal to mainstream since 2022. This is illustrated by looking at the trends among US upper-secondary students – the United States being one of the few places where several surveys were conducted over time. Surveys conducted in 2023 already indicated widespread exposure to GenAI, with around 25-33% of secondary students reporting having used GenAI for schoolwork (Center for Digital Thriving, Common Sense Media and Hopelab, 2024). In 2024, comparable surveys suggest a marked acceleration, with close to 50% of middle and high school students reporting some use of AI tools, particularly for homework support, idea generation and explanations of difficult concepts (Impact Research, 2024). In 2025, about 68% of teenagers aged 15-17 reported using AI chatbots such as ChatGPT (Pew Research Center, 2025).

The above-mentioned increase in GenAI engagement between 2024 and 2025 (Liu, Huang and Wang, 2025) is also likely driven by younger age groups. In 2024, compared to general Internet users, younger age groups and more educated people drove a substantial share of traffic to these tools, signalling early and concentrated use among teenagers and young adults (Liu and Wang, 2024). There is no reason to believe that their contribution to this share decreased. It is possible or even likely that the early experimenters of 2024 might have transitioned to routine users in 2025.

In short, students do use GenAI – a small extent in primary education, a moderate share in lower secondary education, but a majority seem to use it regularly in upper secondary and higher education. While student uptake of GenAI varies by country, the overall trends suggest student use is broadly growing across OECD countries.

What do students use GenAI for?

Many students are clearly turning to GenAI tools for academic purposes. However, their primary motivations often center on convenience and efficiency rather than deeper learning. When asked why they use GenAI, according to a number of studies, students typically responded they wanted “cognitive support”, such as information, explanations and summaries, or “production support”, such as idea generation, drafting, and, perhaps more problematically, solution generation.

In Estonia, for instance, grade 6-12 students most often reported using GenAI to achieve better scores, make educational tasks easier, and save time. These uses typically do not support student learning. Common uses include answering homework questions and generating ideas. Lower secondary students more often reported fact-checking, while upper secondary students tended to report summarising specific topics and creating visuals for presentations (Granström and Oppi, 2025). In most of these cases, the primary motivation was efficiency and convenience (rather than deeper learning).

Similarly, in the seven-country European survey mentioned earlier, the most common out-of-school, non-instructed learning uses are obtaining information (56%) and getting explanations of terms and concepts (45%). Nearly one-third (31%) report using AI to provide complete solutions to tasks, while fewer (20%) use it for self-regulatory functions such as structuring personalised learning plans or tracking progress (Figure 1.2). These patterns align with findings from in-depth qualitative interviews with Dutch pupils (Topali, Ortega-Arranz and Molenaar, 2026).

In higher education, students seem to mainly use GenAI tools to search for information, as well as for linguistic tasks such as editing, summarising, paraphrasing, and to a lesser extent drafting (Rong and Chun, 2024). Hüsch, Horstmann and Breiter (2025) provide the most detailed categories of use and show a largely similar picture, with students primarily using it for general search, idea generation and literature research on the “cognitive” side and for summarising and drafting on the “production” side (with about 22% of regular users) (see Figure 1.3). Interestingly, about 33% of students use GenAI as a “learning partner”.

Taken together, available evidence suggests that a growing number of students use GenAI for general searches, comprehension and drafting, including as a shortcut to complete tasks and homework. The uses do not seem to be very different in higher education and upper secondary education and tend to reflect the study expectations for students at these different levels.

How do teachers use GenAI?

The OECD Teaching and Learning International Survey (TALIS) 2024 provides representative, comparative information on how lower secondary education teachers use AI (OECD, 2025). On average, across OECD countries, 36% of lower secondary teachers’ report having used AI in their work in the 12 months prior to the 2024 survey, with very large cross-country variations. Around 75% of teachers in Singapore and the United Arab Emirates report using AI compared to fewer than 20% of teachers in France and Japan.

While the survey does not ask whether they used AI (all kinds) or GenAI, the tasks that teachers report suggest that most uses involve GenAI tools. Teachers primarily use AI for preparation and productivity tasks: on average, 68% report they use it to efficiently learn about and summarise topics they teach, and 64% use it to generate lesson plans. Among AI users, on average 25% report using it to review data on student participation or performance and 26% use it to assess or grade student work (Figure 1.4).

In addition, 40% of teachers “agree” or “strongly agree” that AI helps them support students individually, on average. Around 50% agree that AI assists in creating or improving lesson plans, though agreement ranges from as low as 18% in France to as high as 91% in Viet Nam. Seven in ten teachers, on average, believe AI could enable students to misrepresent others’ work as their own. Around four in ten teachers agree that AI may amplify biases, reinforce student misconceptions, or compromise data privacy and security.

As for the teachers who have not used it, they report feeling overwhelmed by the growing expectation to integrate digital tools in education, which they see as a barrier to using AI in their teaching. This varies markedly across systems, from fewer than 20% in Brazil, Chile, Costa Rica, Italy, Morocco, Türkiye and the United Arab Emirates, to over 50% in Croatia, the Flemish Community of Belgium, Japan and Serbia. On average, three in four teachers report that they lack the knowledge or skills to teach using AI. About half of these teachers also believe that AI should not be used in teaching. In terms of school policy, one in ten teachers reported that their school prohibits the use of AI in teaching.

OECD average Education systems Lowest value Highest value

36 use of AI

52 of AI use

66 with AI use

What do we know about the use of GenAI tools by teachers in other levels of education? TALIS 2024 allows for comparisons of teachers’ use of AI at other levels in a limited number of participating countries. In these education systems, primary teachers are generally less likely to report using AI than their lower secondary counterparts, with particularly large gaps observed in Australia and the Flemish Community of Belgium. Teachers who use AI in primary education are often more likely to apply it to targeted pedagogical practices, notably to support students with special education needs and to adapt the difficulty of lesson materials to students’ learning needs, with especially large differences observed in France and the Netherlands. This may suggest that they use assistive AI technologies (for students with special needs) and adaptive learning systems, which may or may not involve GenAI (OECD, 2021).

Domestic surveys and country studies largely confirm the picture drawn by TALIS in terms of GenAI uses. For example, in Estonia, a survey of about 4 000 teachers found that 53% reported using AI tools in their work, with higher reported use among primary and lower-secondary school teachers (66%) than among upper secondary teachers (50%) (Granström and Oppi, 2025). Teachers who use generative AI mainly do so to increase efficiency in their work, such as preparing materials, supporting planning, or streamlining routine tasks, rather than for deeply transformative or individualised pedagogical practices. The strongest predictors of use are teachers’ self-reported readiness, including confidence, access to tools, and institutional support, and their belief that GenAI provides clear practical benefits for teaching. By contrast, non-use is largely explained by insufficient skills, lack of confidence, limited training opportunities, or uncertainty about pedagogical value. Age shows a small positive association with use, while years of teaching experience do not matter once readiness and perceived usefulness are taken into account, suggesting that GenAI adoption in Estonia is driven less by seniority than by capacity-building and perceived relevance to day-to-day teaching.

Studies in Australia (AHISA, 2023; Collie and Martin, 2025), Italy (INDIRE, 2025), Slovenia (Licardo et al., 2025) and the United States (Diliberti et al., 2024) provide variations on the same types of usages mainly for preparation work (generative worksheets, lesson plans, activities). They also provide differences in the use across subjects: for example, in the United States, English and “social studies” teachers were more likely to use these tools, possibly because they are more likely to either design or adapt their lessons compared to some other subjects (Diliberti et al., 2024).

The uptake of GenAI is likely much greater in higher education, given the widespread use of GenAI tools for research (Guellec and Vincent-Lancrin, 2026). In France, a survey of 30 000 higher education students, teachers and academic stafffound that 80% of higher education teachers had already used GenAI tools in 2025, mainly to help them draft and prepare their course (49%) or draft student evaluations (26%), and more rarely to support them to correct (13%) or mark (8%) student work (Pascal et al., 2025). An international study of 1 700 teachers in 52 higher education institutions reported that 68% used AI in general. Among those, 75% used GenAI to create teaching materials and 24% to generate feedback to student work (Rong and Chun, 2025). Interestingly, both surveys suggest that higher education teachers rarely use GenAI as part of their actual teaching or request their students to use GenAI. Unfortunately, data on the frequency of use remains limited.

In sum, teachers’ use of GenAI varies by country and the education level they teach. While the use of GenAI seems more prominent in lower secondary than in primary when looking at TALIS 2024 data, research from Estonia shows that primary teachers there use these tools more than secondary teachers. Teachers seem to use these tools to the same or to a lesser extent than their students, but not more. In Estonia again, while 50% of upper secondary teachers used GenAI tools in 2025, 90% of their students do – and in the United States, the only study surveying students and teachers at the same time found similar levels of regular use (with slightly higher levels of use overall for undergraduate students) (Impact Research, 2024). In higher education, the share of regular users seems similar among teachers and students. Whatever the education level, teachers report mainly using GenAI tools for the same reasons: assisting them with the generation of lesson plans, pedagogical activities and teaching/learning materials, and sometimes with the provision of feedback to students.