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

The diffusion of GenAI in scientific research has been extremely rapid. GenAI has gained a core role in all text-related tasks like writing, editing and translating; it is making breakthroughs in analytical work with specialised foundation models for complex phenomena; it is progressing in its ideation capabilities, for suggesting hypotheses or experiment design; and it is becoming a credible, if fallible, research assistant. Its impact on the functioning, the productivity and the epistemological dimensions of research are not fully clear yet, not least because the technology is in rapid evolution. It is, however, already clear that scientific research will be transformed in the years to come, especially as new generations of researchers with increasing grasp of the technology will take over (see Table 13.2).

Policies for encouraging GenAI, notably in the domain of research, have been examined extensively already (OECD, 2023; OECD, 2023); they research funding, investment in computing, infrastructure and appropriate regulatory frameworks. One important aspect is training: research in most, if not all, domains does or would benefit from AI, and researchers with the required skills and competencies are in short supply. Most scientists able to master AI techniques are junior researchers, fresh from specialised curricula, while senior researchers struggle with new techniques. This also risks creating a rift between AI experts and domain experts, resulting, on the one hand, in publications intensive in AI but poor in domain relevance, and, on the other hand, in publications strong in domain relevance but under-using AI. Hence, there is a need to train more scientists in AI curricula and to encourage multi-disciplinary teams, but also to involve more senior researchers in AI-related life-long learning. This is a new responsibility for higher education.

This evolution will also gradually affect education research. One can assume that many of these trends are already or will soon be visible in education research. It is likely that education researchers already use GenAI tools for language- related tasks, such as writing and editing, as well as for programming and data analysis. This is the same for GenAI tools used or designed to manage scientific knowledge: they can also be used to search the education research literature, to produce literature reviews or even research reports. While most specialised scientific GenAI tools were developed for the natural sciences, similar ones will most likely become available for human and social sciences (beyond the “research models” of large LLMs). As AI teacher assistants are being developed for teachers and students, education researchers and their research assistants, if any, could also benefit from GenAI research assistants performing some of their routine tasks and combining some of the affordances mentioned above.

As in other scientific fields, the modelling of complex phenomena such as learning or student pathways (e.g. Pardos and Borchers, 2026) has already started, it is likely to continue to expand thanks to the new possibilities of AI to explore large and complex datasets. This may allow for new types of analysis. One can also imagine that GenAI tools will be able to extract hypotheses from the research literature, improve some of these hypotheses thanks to the ability to manage more data and possibly generate new ones.

The generation of synthetic privacy-preserving datasets that largely replicate original datasets is also a new possibility offered by GenAI. This would indeed allow to explore and analyse datasets that are not shared, linked (or created) for privacy reasons. Education research would then potentially have broader access to both administrative data and data collected by digital learning platforms. Synthetic datasets require careful evaluation to ensure they properly replicate the statistical properties of the original dataset and really preserve privacy. As part of education research relies on survey data, which are said to be increasingly difficult to collect, the possibility to augment those data with simulated data also opens new possibilities, even though it is still unclear how promising this avenue is. While the development and analysis of standardised tests now routinely includes the generation of simulated answers (that is, simulated students), whether this technique can be extended to other subjects is still debated. One promising avenue in scientific research that could also be used in education research are AI multi-agent models: they could help address questions that benefit from interdisciplinary research or that would involve the feedback from simulated social agents, as can be the case in policy research and development.

Last but not least, education research may benefit from the advances in other fields that are made possible by new scientific GenAI tools. One could indeed imagine that, with the right incentives new research could explore the chemistry of the brain while learning or that neuroscience and cognitive sciences will provide new insights on the biological development of children that could inform education research and policies. There is clear convergence between machine learning as a discipline and psychology, notably developmental psychology, with bidirectional knowledge flows, where psychology informs machine learning about training methods and architectures and machine learning offers psychology a testbed for its hypotheses (see e.g. Goddu, 2024).

While it is difficult to predict how GenAI will become integrated in the fabric of education research, not as a subject but as a tool, one clear lesson of its use in other fields so far is that successful GenAI-powered research requires the tight supervision of humans and usually corresponds to an augmentation rather than a replacement model.