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

GenAI tools can also support education systems and institutions in ways that do not immediately impact learning outcomes. Like other sectors, GenAI offers opportunities to streamline workflows and improve the operational efficiency of educational institutions and systems. This Outlook focuses on this aspect through three lenses. It shows how GenAI techniques can already support the improvement of “back office” processes in higher education institutions, such as degree recognition, educational programme design, and support for study advisers (Pardos and Borchers, 2026); the development of standardised assessment items (von Davier, 2026); and new opportunities and challenges for scientific research, which are also largely similar for educational research (Guellec and Vincent-Lancrin, 2026).

How can GenAI support more effective pathways within and across institutions?

One of the challenges for higher education institutions is articulating their programmes with those offered by other institutions, domestically and abroad. Recognising equivalences between them is key to ensuring that students can change study paths without losing time and to support student mobility across institutions, for example. Some systems address these issues by designing national degree structures or using common credit systems that ease pathways across institutions and subjects for students. Articulation agreements and exchange programmes between institutions play a similar role. In practice, however, transfer decisions and articulation agreements are still largely human made and time-consuming, requiring faculty and admission officers to review syllabi and programmes and make a judgment on the equivalence between institutions. Simplifying these processes can enhance the systems’ efficiency but also their completion and attainment rates, thanks to more flexible pathways for students. School systems can face similar issues when, typically in secondary education, they start offering more tracks and choices of courses to meet students’ interests.

Pardos and Borchers (2026) show that models underpinning GenAI can support these equivalency processes by making relationships between courses, within and across institutions, apparent. To this effect, the AI model processes the text description of the courses and/or the past enrolment patterns to provide administrators with better clarity on which courses within their own institutions are close or similar or well-articulated, and which external programmes (and courses) can be considered equivalent to theirs. Current research shows that GenAI tools can be very similar to human judgment at identifying equivalences (and can also uncover new equivalence possibilities), but adoption depends on trust and the way information is displayed to the final decision makers. These models could also provide advice to students about the next steps of their studies, for example by recommending institutions or programmes.

In the same spirit, work on how GenAI can support study advisers is emerging. In education systems and higher education institutions, study advisers typically provide advice on course selection and career pathways. Lekan and Pardos (2025) developed a GPT-driven model that asks first-year university students about their course preferences and career goals, and then gives recommendations with justifications for advisors to review before in-person meetings. Academic advisors rated the suggestions of the GenAI tool favourably, fully agreeing with the GenAI-generated major recommendation 33% of the time, saving them time while maintaining both their professional autonomy and student relationships.

Other functionalities using similar techniques can be mentioned, such as the automatic tagging of learning content according to changing taxonomies. Tagging learning resources such as open educational resources, or the learning content included in an adaptive learning system, is essential for their discoverability and ensuring that they match local curricula.

How can GenAI support the development of standardised assessment?

GenAI is increasingly used in the development and delivery of standardised assessments, including in high stakes contexts. Pardos and Borchers (2026) and von Davier (2026) highlight how LLMs can automate the creation of multiple-choice and short-answer assessment items, particularly when anchored in existing curricular material or when models are initially designed by experts, possibly assisted by GenAI tools. For example, Pardos et al. (2024) report that ChatGPT-generated algebra items demonstrated comparable psychometric properties as those created by humans. Notably, the LLM-generated items exhibited slightly stronger differentiation between high- and low-ability respondents, suggesting that GenAI can produce assessment content of similar or even superior quality under controlled conditions. This represents time and productivity gains for both national authorities, test developers and potentially instructors to design assessments for students. This can also address known limitations in traditional test banks, for example the persistent challenge of overexposure of items (which makes them less effective). The limitation is that until GenAI stops hallucinating, instructional staffwill still need to check every problem before it is seen by a student.

GenAI can also be used to innovate standardised assessments. For example, von Davier (2026) shows how the Duolingo English Test introduced two new types of writing and speaking assessments that would not have been possible without the use of GenAI. One is an interactive writing task, where a chatbot provides mid-task feedback to the test-takers as they are writing a short essay in English, suggesting additional directions and revisions. Another one is an automated assessment of their oral speaking during a dialogue in “natural language” with a GenAI-powered agent. In the case of high stakes assessments, generative models are just a layer of a more complex architecture with other AI tools and humans.

Finally, GenAI models can be used to evaluate and calibrate the quality of standardised items. Liu et al. (2025) demonstrate that multi-agent AI models, which bring together ensembles of LLMs acting as “synthetic” or “simulated” respondents, can produce response distributions with psychometric properties closely aligned to those of students. These augmentation strategies, such as adding LLM responses to even a small set of human respondent data, suggests that LLM-based calibration can complement limited student response data, reducing costs and accelerating item validation cycles in the development of standardised assessments.

How can GenAI strengthen educational research?

GenAI is having a significant impact on scientific research, with growing use by researchers to write academic papers and assist them at different stages of the research process. Guellec and Vincent-Lancrin (2026) highlight these trends, providing examples of how GenAI intervenes in the research process in the natural sciences, including the elaboration of literature reviews, analysis of large datasets to generate new research questions or hypotheses, and cooperation with humans to generate and perform research and experiments. While GenAI has already had spectacular achievements, such as the generation of the 3D structure of 200 million proteins, it usually still involves intense human supervision and presents some risks for the research enterprise, such as reduced collective originality and the increasing impossibility to keep up with the collective research output.

Improving the quality of learning and effectiveness of education systems requires investment in high-quality research and evidence to inform policy and practice. While information on how education researchers use GenAI is not available, they likely leverage GenAI tools for tasks like writing and editing research papers and performing literature reviews. Beyond these tasks, three areas stand out as particularly promising for GenAI to support educational research. First, while education is a data-rich environment with lots of administrative data collected for the smooth operation of education systems and institutions, those are often under-analysed because of legitimate privacy concerns. GenAI applications can now easily generate synthetic privacy-preserving datasets that reproduce the statistical characteristics of a dataset with very little risk of privacy breach (as the dataset is entirely created). Second, the use of simulated data to augment real ones could also be tested and applied, albeit with caution and when the context makes it appropriate, to supplement real data where survey response rates are too low. Lastly, the rise of multi-agent models based on GenAI (also called “agentic AI”) allows new possibilities for research that could be deployed in the case of education, where answering research questions often require interdisciplinary perspectives.