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

Introduction

Scientific advancement over time has been characterised by the use of increasing amounts of data for solving issues of increasing complexity. Moreover, scientists have been using growingly powerful instruments that supplement the human brain and senses, like the microscope, medical imaging devices or particle accelerators. Writing, printing and now the computer also allow scientists to store, analyse and communicate scientific information. Artificial intelligence (AI), whose latest avatar is generative AI (GenAI), is another step in the historical trend of science instrumentation, which allows the processing of enormous quantities of complex data in new ways.

Scientific research has been at the forefront of AI adoption and the arrival of GenAI has further accelerated its use, making it more accessible to all scientists. Recent surveys suggest that more than half of scientists now use GenAI tools. GenAI can analyse and generate both structured and unstructured data, including text, tables, statistics, images, videos, diagrams, graphs, chemical and mathematical formulas, DNA sequences and other biological data. It can generate new data with specific properties by learning and re-combining patterns based on its training data; for example, responses to questions, synthetic datasets, predictions such as simulations or weather forecasts, and even simulated agents. GenAI helps researchers accelerate existing research tasks (such as writing text and statistical processing), improve the quality of others’ tasks (like editing and producing figures), and perform tasks that were previously out of reach (such as analysing extremely large datasets). In helping facilitate such tasks, GenAI is transforming scientific research.

This chapter will first present an overview of the various uses of AI in science, and highlight the extent to which those uses are relevant to education research. As education research draws on many disciplines (Vincent-Lancrin and Jacotin, 2023), knowing how GenAI is used in a variety of disciplines will help governmental research funders envisage upcoming changes in education research. The chapter will then examine the possible effects of AI on the creativity of research and the reliability of science, both highly debated issues in the scientific community, and which have direct connections with education. The conclusion will explore some of the possible consequences of the use of GenAI for education research.

The use of GenAI in scientific research

GenAI is used in research for four distinct but related purposes: manipulating language (including scientific language); managing knowledge; generating knowledge; and managing the entire chain of operations in a research project. Accordingly, GenAI models as used in research can be divided into four categories: 1) general purpose models, notably large language models (LLMs) – like GPT, Gemini and others – used by many researchers for generating text, images or computer code; 2) specialised models dedicated to managing various language-related tasks such as literature reviews, refereeing, and generating hypotheses or suggestions for experiments (“ideation”); 3) specialised models used to tackle highly complex scientific problems that involve vast quantities of data or complicated mechanisms (for example, the 3D shape of proteins); 4) research assistants and “robot labs”, which autonomously manage entire sequences of operations in a research project, from initial analysis of data to experimentation. We will examine these four categories of models in turn, after presenting the evidence concerning the diffusion of GenAI among researchers. A clear trend of increasing cognitive power and agency of GenAI over time will then appear.

To what extent do researchers use GenAI?

The use of GenAI in research is advancing rapidly, though statistics remain limited. Broader trends in AI adoption can help indicate future GenAI patterns.

AI adoption has been progressing fast. Duede et al. (2024) trace the share of AI-engaged publications (1985-2022), which rose from ~2% in 2015 to 8% in 2022 across all scientific fields (Figure 13.1). Evans et al. (2024), analysing 100 million papers (1980-2024), identify over 1 million AI-assisted papers (1.57% overall), showing pervasive adoption across biology, medicine, chemistry, physics, materials science and geology.

Total change: 1 293%

For GenAI specifically, Liang et al. (2024) studied nearly 1 million papers (2020-2024) and found steady growth in “LLM-modified” papers, with the sharpest rise after ChatGPT’s release. Uptake is strongest in Computer Science (17.5%) and weakest in Mathematics (6.3%) (Figure 13.2). The analysis does not include social science, which is one of the main contributors of education research, although natural sciences represent a significant minority of the education research production (Vincent-Lancrin and Jacotin, 2023).

A survey (conducted in March 2025, with 5 000 researchers) estimates that over half of researchers already use AI for manuscript preparation and error detection (Naddaf, 2025). About one-third use or plan to use GenAI for data collection/processing, while its use for complex tasks (journal choice, citations) remains less common. More than half of respondents believe that AI outperforms humans in tasks like literature review, summarising, plagiarism checks and citation management, anticipating mainstream adoption within two years (Figure 13.3). Early-career researchers show higher enthusiasm than senior colleagues, although many remain cautious about AI’s role in higher-level tasks. Despite the differences across fields, one can assume that the uptake for these mainly language-related tasks is similar for education researchers.

Language-related tasks

A first category of uses relates to language: translating, editing, writing and summarising papers. These tasks often involve general-purpose LLMs (ChatGPT, Claude, Gemini, etc.), which are the most accessible, but not exclusively, since some science-specialised tools also have such capabilities. AI can help adapt papers to meet journal submission guidelines, draft abstracts, write peer reviews, and assist in drafting grant proposals (Heidt, 2025). Although researchers were already using some AI writing assistants, the release of LLMs brought a substantial change in the extent of such use (Lenharo, 2024).

The AI model generates text in response to a query (a “prompt”) given by the researcher. Here is an example of a prompt: “I’m writing a paper on [topic] for a leading [discipline] academic journal. What I tried to say in the following section is [specific point]. Please rephrase it for clarity, coherence, and conciseness, ensuring each paragraph flows into the next. Remove jargon. Use a professional tone” (Gruda, 2024).

Machine-assisted editing is especially useful for non-native English speakers, due to its potential to improve flow, grammar and tone. In a poll by the European Research Council (ERC), 75% of more than 1 000 ERC grant recipients felt that generative AI would reduce language barriers in research by 2030 (Prillaman, 2024).

A Nature poll (Kwon, 2025) surveyed more than 5 000 researchers worldwide (with China underrepresented) in March 2025. More than 90% of respondents said they believe it is acceptable to use generative AI to edit or translate one’s research paper. When it comes to generating text with AI – for instance, writing all or part of a paper – a majority (65%) think it is ethically acceptable, but about one-third are against it. The most popular use was editing a research paper, but only around 28% said they had done this. That number dropped to about 8% for writing a first draft, making summaries of other articles for use in one’s own paper, translating a paper, and supporting peer review. While 42% of PhD students report using AI for editing purposes, the percentage drops to 22% for senior researchers.

Work by Kobak et al. (2025) finds that one in seven biomedical abstracts published in 2024 (among 1.5 million papers indexed in PubMed) was written with AI assistance. They detect such abstracts by identifying “excess words,” i.e. words whose frequency has surged since the rise of LLMs but that have no functional role (e.g. “delve,” “unparalleled”; in total, there are 454 excess words).

Support from AI in writing can boost researchers’ productivity for certain non-core tasks, like polishing style or handling administrative duties, freeing up time for more conceptual work (Gruda, 2024).

Specialised models (e.g. the Black Spatula Project and YesNoError) are used to spot errors in research papers, including factual mistakes, calculation errors, methodological flaws and referencing issues.

The systems first extract information, including tables and images, from the papers. They then craft a prompt, which tells a ‘reasoning’ model — a specialised type of LLM — what it is looking at and what kinds of errors to hunt for. The model might analyse a paper multiple times, either scanning for different types of errors each time or cross-checking results. However, the rate of false positives — instances in which the AI claims an error where there is none — is a major hurdle (10% on average according to some tests; for example, the model may state that a figure referred to in the text does not appear in the paper when it actually does) (Gibney, 2025).