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

The opportunities of GenAI for education and learning rely on access to an adequate and affordable digital infrastructure, including digital devices, high-quality connectivity services and infrastructures, educational GenAI tools and other digital resources as well as qualified teachers who know how to use them appropriately for teaching and student learning. Ensuring a level playing field ensures that talented students are not held back by lack of basic infrastructure.

High-quality connectivity is usually a necessary prerequisite to support the high throughput (download and upload speeds) and low latency (i.e. minimal delays in the round-trip time for a response) required for GenAI to deliver responses in real-time. However, ensuring that all in society have access to high-quality and affordable communication services everywhere is still aspirational in many cases. Eliminating broadband connectivity divides and ensuring high- quality digital infrastructure at affordable prices for everyone, in all locations, is a first step to providing equality of opportunities to students from all socio-economic backgrounds and geographical origins. The OECD Recommendation on Broadband Connectivity underlines the importance of eliminating divides in access to high-quality and affordable broadband services as well as in the digital skills needed to make effective use of applications that leverage them, such as GenAI tools.

The advice in this section adapts the previous OECD-EI Guidelines to the case of GenAI, and also includes suggested actions on teacher professional learning. Non-generative educational AI tools such as intelligent tutoring systems were largely not available for free to students and teachers, and therefore usually not accessible unless provided through their educational institution. This is not the case for general-purpose GenAI tools, which are available for free regardless of institutional provision, and whose most powerful versions are available for a fee to the students and teachers who can afford them.

As mentioned throughout this document, education policy makers and other stakeholders should consider two main use cases for GenAI tools: a supervised and controlled use in and for the classroom as part of a teacher-designed learning activity, and educational uses by students and teachers that are unplanned by education institutions and stakeholders.

In addition to access to adequate devices and connectivity services and infrastructures and GenAI resources, students and teachers need the relevant skills to use GenAI tools for learning. This can be facilitated by access to skilled teachers, administrators and peers that have a high level of proficiency to use such tools in their educational practices. This involves the provision of learning opportunities to all stakeholders but also the investment in support staffthat can provide coaching and mentoring about the pedagogical and administrative uses of GenAI tools. Those opportunities can take different forms, from teacher education to formal professional learning activities through to the building of teacher professional learning communities around the use of GenAI tools.

Closing broadband connectivity divides, as well as broader digital divides is a challenge that goes beyond education, but remains an important priority to ensure the adoption and effective use of GenAI in education. This is a pre-condition for any use of AI for educational purposes. The OECD Recommendation on Broadband Connectivity provides recommendations to help bridge these digital divides, promote widespread broadband deployment and foster competition and investment in broadband development. The quality and reliability of connectivity is a key element of access. In education, quality devices need to be adequate for learning applications and have sufficient performance, including long enough battery life, be long lasting and abide by sustainability standards. This does not imply that public authorities have to pay for devices and connectivity for all students. Leases, loans, zero-rating of specific sites and other solutions have been successfully experienced in the past years in different countries and jurisdictions. If publicly provided, devices should be affordable, measured by the total cost of ownership. Among many possible models, Ceibal in Uruguay has developed a long experience in the provision and support of educational devices for all their public-school students.

General-purpose LLMs and other tools are usually not educational in purpose. Jurisdictions and institutions may consider providing access to GenAI tools that are aligned with their curriculum, courses, pedagogical preferences and other educational objectives. This provision is also a way to provide tools that respect privacy and data protection. Typically, a mix of general-purpose and educational GenAI tools will be most helpful. Depending on the task, they may consider whether non-generative educational AI tools would not be more effective or appropriate. One challenge for education lies in the cost of licensing privacy-preserving GenAI tools for students and teachers, but many countries, jurisdictions or education institutions already do so.

Some countries, jurisdictions and higher education institutions have adopted different approaches. England has developed a content store providing EdTech developers resources to finetune their educational GenAI models so they align with the English curriculum and research evidence about teaching. France is developing a “sovereign AI” for teachers and students that aligns with its values and curriculum and respects privacy. Korea has introduced AI-powered digital teaching and learning materials that blend traditional resources with pedagogically validated AI digital tools. These materials are designed for targeted use by students and teachers across specific school levels and subject areas. As part of its AI Leap programme, Estonia is working with GenAI companies and licensed general-purpose GenAI tools for teachers and an educational version for their high school students. Some higher education institutions across the world have also either developed their own GenAI tools to support their students and teachers or provided access to commercial models while ensuring their privacy, intellectual property and data protection.

Given the current reality of persistent connectivity and digital divides, jurisdictions and educational institutions can also consider the “AI unplugged” model. This approach consists of using AI tools and resources that work with existing digital connectivity infrastructure (usually mobile phone or smartphones) and level of digital skills. Brazil has for example successfully used it in some areas of the country where devices and connectivity are scarcely available. In the case of GenAI, Small Language Models run on mobile phones offline and can provide students and teachers with a first access to AI language models, although with limited capabilities compared to the large ones. It would be welcome that GenAI companies develop small GenAI models for educational purposes, and that jurisdictions make education stakeholders aware of them where appropriate.

Teachers’ GenAI pedagogical literacy can come from a variety of professional learning opportunities, in pre-service teacher education as well as during in-service learning opportunities. As for any other professional knowledge, it comes from learning by doing and thus from the access and use of GenAI tools for some professional purposes as well as from different types of real-time human support, which may range from “digital” or “AI” champions and professional learning communities through to innovation projects focused on the use of GenAI for teaching and student learning. Teachers should acquire enough professional knowledge to make informed decisions as to when GenAI tools should and should not be used, and how they can best support student learning.

For teachers, GenAI literacy also encompasses the ability to use GenAI tools in their teaching to support their students’ learning. They should gain a basic understanding of GenAI as a technology, including its strengths and limitations, and learn how to effectively and ethically use it. The AI literacy framework published by the European Commission and the OECD could support this effort.

Jurisdictions are well placed to support teachers and administrators to keep abreast of new developments in the landscape of educational GenAI tools and inform and curate them so they can more easily select and appropriately use them. This curation should consider non-generative educational AI tools that may be more effective or appropriate than GenAI tools for similar use cases. Teachers also need support to navigate the ethical, legal and pedagogical issues that arise when students and families use generative AI beyond the classroom.

AI tools such as classroom analytics provide teachers with feedback about some aspects of their teaching, with no intrusion of a human observer, but also with significant risks of unethical use by their employers. For example, these tools can record how much time teachers have spoken during class, what mix of pedagogical activities or learning strategies they have used, etc. GenAI tools can help turn the analysis of these classroom observations into suggestions and recommendations, sometimes in real-time, but most often after class. This feedback would allow teachers to reflect on and improve their practice. While research is still underway, this GenAI feedback may be more impactful when it is discussed with a coach or mentor who has not observed the class (nor made the initial suggestions). Jurisdictions and developers are expected to ensure these tools do serve professional learning and not surveillance, for example by limiting access to the collected information to the interested teachers only.