Section 1: Living Guide of AI in the behavioural research process

A living guide to using AI across each stage of the behavioural research process, with examples.

AI can assist in multiple stages of the research process, from literature reviews to data analysis and report writing. Think of AI as another colleague, able to help and assist (with some limitations) rather than a complete replacement. AI has the ability to enhance the work we are doing, making daily tasks simpler and less onerous, but there are some things to consider before starting a project.

This section presents an up-to-date collection of generative and analytical AI tools potentially useful for different stages of behavioural research. Each tool entry includes essential information such as links, core functionalities, limitations, cost/free access options, special research features (e.g., the ability to gather references and evidence), published research that applied the tool, user perspectives, and the latest update dates.

General reading on how AI empowers research processes

If you do not have a specific research task or AI tool in mind, start by exploring the following articles to get a broader idea of the tool's researchers are using and the research tasks they're deploying AI for. In Nature News Articles, Heidt (2025) interviews researchers using tools to aid literature reviews, generate hypotheses, streamline analysis workflows, and polish writing. Meanwhile, Gibney (2025) compares the capabilities of the largest LLMs. Charness and colleagues (2025) examine how LLMs can support the design, implementation, and analysis of experiments in Nature Human Behaviour, while Khalifa and colleagues (2024) review the various ways AI is being used throughout the research process.

Heidt, A. (2025). AI for research: The ultimate guide to choosing the right tool. Nature, 640(8058), 555–557. https://doi.org/10.1038/d41586-025-01069-0

Charness, G., Jabarian, B., & List, J. A. (2025). The next generation of experimental research with LLMs. Nature Human Behaviour, 1–3. https://doi.org/10.1038/s41562-025-02137-1

Gibney, E. (2025). What are the best AI tools for research? Nature’s guide. Nature. https://doi.org/10.1038/d41586-025-00437-0

Khalifa, M., & Albadawy, M. (2024). Using artificial intelligence in academic writing and research: An essential productivity tool. Computer Methods and Programs in Biomedicine Update, 5, 100145. https://doi.org/10.1016/j.cmpbup.2024.100145

Thought pieces on the challenges of using AI in research

AI has the potential to improve research productivity, accessibility, and collaboration in behavioural science. We advocate for a human-in-the-loop approach with thorough testing and careful evaluation of AI methods. Researchers should explore AI's capabilities responsibly, aware of benefits and inherent challenges. It's crucial to balance AI's advantages with maintaining core skills, creativity, and methodological diversity essential for rigorous research. The following papers offer a critical perspective on AI's promises and challenges in research. Also, please review the Repositories section on Ethics, Sustainability, and Responsible Use.  

Messeri, L., & Crockett, M. J. (2024). Artificial intelligence and illusions of understanding in scientific research. Nature, 627(8002), 49–58. https://doi.org/10.1038/s41586-024-07146-0  

Abdurahman, S., Atari, M., Karimi-Malekabadi, F., Xue, M. J., Trager, J., Park, P. S., Golazizian, P., Omrani, A., & Dehghani, M. (2024). Perils and opportunities in using large language models in psychological research. PNAS Nexus, 3(7), pgae245. https://doi.org/10.1093/pnasnexus/pgae245   

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