Developing Principles to Guide Artificial Intelligence (AI) Use in Education Research
As AI becomes more widely used in education, researchers are not only studying its impact in schools but also using AI for research activities like coding data, generating test items, conducting analysis, and drafting reports. Yet while there is substantial guidance on AI in classrooms, far less supports its responsible use in education research itself.
To address this gap, AIR’s AI Education Research Principles Design Lab convened researchers, industry leaders, and funders to develop shared, adaptable principles that apply across research types and stages. The aim is to complement existing research standards by promoting transparency, trust, and ethical, high‑quality use of AI—maximizing benefits while minimizing risks.
Draft for Public Comment: Principles for the Use of Artificial Intelligence in Education Research
AIR conducted a public comment process to gather input from the education research community. The comment period closed on May 8, 2026. All feedback is under review, and revisions will address the most common and relevant themes.
The increasing prevalence of artificial intelligence (AI) in schools and other educational settings has been accompanied by an expanding body of research that is intended to help policymakers, educators, family members, and students make informed decisions about how to use AI appropriately.
Education researchers are conducting a wide range of studies on AI use in education settings. They are also increasingly using AI as a tool to improve the quality and efficiency of their work. Researchers are using AI for a variety of tasks including transcript coding, essay scoring, test item generation, and report writing, and they continue to explore new uses.
However, although extensive guidance has been produced to inform the use of AI in educational settings, this guidance does not, for the most part, address responsible use of AI in education research.
Having a common set of principles for ethical, high-quality use of AI in education research is essential to establish transparency and trust and to help ensure that research leads to benefits and to minimize the risk of harms. Education researchers use standards and best practices to guide their research approaches.
Such standards, which include the Institute for Education Sciences’ Standards for Excellence in Education Research (SEER), as well as the Open Science Framework, emphasize the importance of research teams making their methods, findings, and data widely available. A similar, proactive approach is needed around AI in education research, not only to harness the opportunities this technology affords but also to mitigate the specific risks it can pose.
The AI Education Research Principles Design Lab
AIR has launched the AI Education Research Principles Design Lab to identify the key principles that will help education researchers navigate risks, benefits, and opportunities for use of AI. The initial, core group participating in the Design Lab involves an array of organizations, representing perspectives from education research, industry, and funders.
The Design Lab envisions AI education research principles that:
- Apply to all types of education research, whether basic or applied, early stage or expansion, qualitative or quantitative;
- Guide all aspects of the education research process, as well as related processes, such as enabling review of proposed research when making funding decisions or informing requirements of what to address for reporting and publication; and
- Are dynamic and will require revisiting and revising as technology evolves.
Early Design Lab activities have included discussions about central themes that the principles should address and how to make the principles general enough to apply to education research writ large, balanced with the need for sufficient specificity to make them actionable.
