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

As science is getting ever more quantitative, programming and analysing data are the main tasks of many researchers, especially PhDs, across all disciplines (including, increasingly, the humanities). This is certainly the case in education research, where the share of quantitative research has increased over the past decades, even though it remains a minority of education research (Vincent-Lancrin and Jacotin, 2023). These tasks require specific skills in complex techniques and can consume a lot of time (e.g. for “debugging”, i.e. tracking mistakes in computer code), while exposing the researcher to significant risks of errors. Special tools, based notably on GenAI, have been developed to alleviate these burdens.

Code editors are tools that aim to make it easier for researchers to use coding to organise data, create analytical sequences, generate descriptive statistics or visualisation. Such tools are now widespread, having overtaken GitHub and Stack (a community site) for troubleshooting. These tools allow researchers to save a lot of time, generate higher quality outputs and allocate time to more substantive matters. Rather than spending hours waiting for answers from correspondents, users can simply highlight a section of code and ask a GenAI chatbot to fix it (Heidt, 2025).

There are also more sophisticated AI models that can do extensive analysis of large tables of numbers and generate output like predictions (imputing), error detection, etc., hence avoiding the need for the researcher to do the programming by themself. For instance, TabPFN is a “tabular machine learning” model that infers outcomes from tables of any sort of data. It can take a user’s dataset and immediately make inferences about new data points (McElfresh, 2025).

GenAI models have also a strong capability of processing “unstructured data”, like texts and images, that can thereby be quantified and subjected to powerful statistical treatment: this is clearly of specific interest for humanities and educational research.

Modelling complex phenomena

While LLMs process words, models based on the same techniques (notably so-called “transformer” architecture) can be trained with other types of data: chemical formulae, mathematical concepts, astronomical pictures, DNA, etc. These models can also mix different types of data as inputs (“multi-modal”), or they can take one type of data as input and generate a different type of data as output. This diversification in data types allows for the application of GenAI models to a broad variety of problems, in a broad variety of disciplines and contexts.

These models are commonly applied to so-called “closed-world problems”, where the fundamental laws are known, but drawing out predictions is computationally difficult, because the parameters and variables are too numerous, or the relations are complex and non-linear. Examples abound in biochemistry, material sciences or weather forecasting. This allows for the combination of fundamental, established knowledge with an algorithm’s superior capacity to find meaningful correlations in data. These models are statistical in nature and are thus trained on vast amounts of data, which restricts the cases where they may apply (not all domains offer sufficient amounts of data).

These models allow researchers to gain time and reduce the cost of research. “Our goal,” says a biologist, “is to create computational tools so that cell biology goes from being 90% experimental and 10% computational to the other way around.” This comment was made in regard to a project using AI to create a “virtual cell” (Callaway, 2025).

While education systems generate large amounts of data, privacy and ethical concerns has made their widespread use for education research complex. Still, some of these techniques could increasingly apply to the analysis of large data sets and are already in use to some extent. For example, Pardos and Borcher (2026) use these AI analysis and visualisation tools to show the similarity between higher education courses based on student enrolment history. Moreover, education research increasingly builds on neuroscience, cognitive science and one can imagine that learning science will benefit from advances in the study of the brain from a chemistry or biological perspective as well. For example, advanced AI techniques may help to better understand the clinical and socio-genetic dimensions of learning and education performance (Isungset et al., 2022; Morris et al., 2022).

Chemistry and biology are leading disciplines where GenAI has been applied, due notably to the availability of a lot of data and to the combinatorial nature of mechanisms at work. The task of most models is to relate some property (therapeutic or physical) to the composition of a compound. Hence, a model can either predict the properties of given compounds, or, alternatively, it can predict the composition of compounds that display given properties. Some models can also do retrosynthesis, i.e. predict the sequence or network of chemical reactions that allow for the production of a particular compound with given ingredients (reactants).

These properties and reactions obey known physical and biological laws, but the number of components, the non- linearity of many mechanisms involved and the sensitivity of aggregate properties to minor modifications make it difficult, or even impossible, to analytically solve most cases. In the case of biology, the compounds are extremely complex (proteins can be made of several thousand molecules). Most models mix data analysis with knowledge of the basic rules of the domain, so that the generated items comply with the known laws of the domain.

There is a strong analogy between chemistry or biology on the one hand, and language on the other hand, as both are compositional: they are made of elementary components (words or molecules) that combine to produce emergent properties (meaning or physical characteristics). Hence, the techniques used to train LLMs have been directly transferred to these domains. Certain researchers even directly use LLMs to conduct chemical analysis, although the training base of LLMs in the domain is not as large as specialised models. They note: “Our results show that LLMs can accurately reason about chemical entities in both local and global terms, analysing single reactions but also whole synthetic routes, and that such capabilities can be exploited through search algorithms for solving chemical problems in more flexible terms.” (Bran, 2025).

Some models are made of several interconnected modules handling different types of data, and leverage the synergies between these types of data. Certain models relate natural language with chemical or biological data, which lets users query the model about the chemical composition that some particular property would possess, explained in natural language.

Box 13.1 provides examples of specialised scientific AI models.

ESM3 is a generative model of the three fundamental properties of proteins: sequence, structure and function, through language modelling. ESM3 can be prompted with instructions from each of these sorts of inputs. This allows prompts to be specified at multiple levels of abstraction, from atomic level structure to high-level keywords describing the function and fold topology (Hayes, 2025).

AlphaFold is a family of models produced by Google DeepMind, aimed at predicting the 3D shape of proteins, and for which its main authors were awarded the Nobel Prize in chemistry in 2024.

AlphaFold has had a broad and deep impact on structural biology (Saplakoglu, 2024). All researchers who use proteins now use AlphaFold2 or an alternative, so that they can reorient their time and efforts to other tasks. As “simple proteins” have been solved, researchers are turning to new matters: complex proteins (e.g. those whose shape is context-dependent or those that are composites) and RNA, giving rise to AlphaFold3 and other models. The impact of AlphaFold on downstream research (as proteins’ shape is used as an input to other research) has been enormous: reviving structure-based drugs discovery; accelerating the creation of hypotheses in biology; and creating new avenues to understand complex interactions occurring within cells.

AlphaFold can still present risks for researchers. Errors occur, mainly in complex cases, sometimes leading to incorrect assumptions if unchecked.

DeepMind’s AlphaProof and AlphaGeometry 2 (2024) solved four of six International Mathematical Olympiad problems, reaching silver-medallist level. These systems combine formal language training with neuro-symbolic reasoning, reducing “hallucinations” and enabling rigorous verification (Castelvecchi, 2024). Progress is impressive, though still short of tackling research-level mathematics.

Krenn et al. (2025) developed Urania, an AI algorithm for designing gravitational wave detectors. The system produced numerous innovative designs, some improving sensitivity more than tenfold; such designs reimagine known techniques, opening new possibilities for astrophysics.

LLMs can be used to simulate human participants in empirical studies, for example, producing synthetic interviews, interactions between actors or specific behaviour in particular situations. There is active research on simulating human behaviour, with models being especially trained on psychological material (e.g. Binz, 2025) and integrating knowledge from the cognitive sciences. AI models trained on human behavioural data can serve as test benches for simulating human decisions regarding various contexts including educational ones. They could play a similar role as organoïds (self-assembled constructs that mimic certain properties of in vivo organs) play in medical research. Such models accelerate studies and reduce their cost.

There are still limitations to the potential of this approach though, as the capacity of LLMs to simulate the diversity of human behaviours in complex situations is still limited. For instance, in a study about factory working conditions, a worker on the floor and a manager would likely have different responses about a variety of aspects related to the work and workplace. However, an LLM participant’s generated responses might combine these two perspectives into one answer, conflating attitudes in ways that are not reflective of reality (Kapania et al., 2025).

Should this use of AI become fruitful, it could have a strong impact on education research, notably for the generation of survey answers, which, according to survey implementers, have become increasingly difficult to collect. For example, in the production of standardised assessments, Liu (2025) shows that multi-agent AI models bringing together ensembles of LLMs that can serve as “synthetic respondents” producing response distributions with psychometric properties closely aligned to those of college students. Pardos and Borchers (2026) argue that LLM-based calibration can complement limited student response data, reducing costs and accelerating item validation cycles. While human responses remain essential (not least because they are used to generate simulated ones), AI-generated responses could augment them and, as is the case of answers to test items, expand their variance while remaining aligned with them. While it will take time to assess when simulated answers add value without distorting human responses, this is a line of AI impact that will be particularly useful for education research if it becomes successful.