New to AI in research?
Start with Section 1's Living Guide to see how AI can be used through the behavioural research process. This provides an overview of different tools and examples of application in real-world research.
Concerned about responsible use?
See Section 2's overview of key challenges to using AI in behavioural research and BR-UK's Statement on responsible and effective AI use in Section 5.
Looking for learning resources?
Check out Section 3 for more in-depth materials and courses on using AI. Also check out BR-UK's webinars and advice sessions in Section 4.
How to explore the repository
Living Guide of AI in the Behavioural Research Process
Resources on how to use AI in literature review, hypothesis generation, data collection, analysis, and writing. Includes real-world examples and tool recommendations.
Section 2Ethics, Sustainability & Responsible AI Use
Disclosure and transparency, biases, privacy, sustainability, and the regulatory landscape. Includes funder and publisher guidance, and an overview of UK regulations.
Section 3General AI Learning Resources
A curated set of courses and YouTube channels — from Elements of AI and Harvard CS50 to researcher-specific tutorials and advanced deep-learning courses.
Section 4BR-UK AI Webinars and Advice Sessions
Three on-demand webinars on improving behavioural research with AI, applying analytic AI, and using AI responsibly. Plus recordings of three advice sessions with Janna Hastings, Susan Michie, Amy Rodger, Maggie Guanyu Yang and Robert West.
Section 5Using AI for Behavioural Research Effectively and Responsibly
A forthcoming living statement addressing ethical standards, practical benefits and limitations, and recommendations for everyone doing, using, funding, or commissioning behavioural research.
Section 6Disclaimer & Contact
The repository is a living effort — resources evolve quickly. Here's how to get in touch with the BR-UK team and suggest additions, corrections, or feedback.
The Repository in a Nutshell
Generative AI (AI) is increasingly applied in behavioural research, with claims it can improve efficiency and quality for tasks like literature review, research design, data analysis, communication, and intervention planning. However, current evidence offers a mixed outlook, and more thorough evaluation is essential — especially considering research integrity risks. AI models may be biased due to skewed training data, which can reinforce stereotypes and underrepresent global viewpoints. AI raises ethical questions related to plagiarism, transparency, and privacy. Rapid, uncritical adoption could also lead to wider problems, such as degrading researcher skills, lowering public trust, and increasing environmental impact.
This repository gives behavioural researchers an overview of the Generative AI landscape and points to key resources for further learning. It is not intended as a comprehensive guide.
Any suggestions?
At BR-UK, we're committed to creating a resource for and informed by the behavioural research community. AI is evolving rapidly, and great learning opportunities arise from researchers exploring how to use AI tools in their research. If you have ideas, examples, or tools you'd like us to add to this resource, please fill in our suggestion form. We welcome your contributions.