Generative AI and the transformation of scientific research.

This section provides an overview of the various uses of AI in scientific research, and highlights the extent to which those uses are relevant to education research. Specifically, it examines the use of generative AI in facilitating language-related tasks, programming and data, modelling complex phenomena, managing knowledge (in search engines, literature reviews, research reports and summaries) and hypothesis generation, as well as the use of AI research assistants and robots. The section examines the possible effects of AI on the creativity of research and the reliability of science – both highly debated issues in the scientific community – which have direct connections with education. It concludes by exploring some of the potential consequences of the use of generative AI for education research

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 section 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, knowing how GenAI is used in a variety of disciplines will help governmental research funders envisage upcoming changes in education research. The section 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.