Using Customised LLM-Based Chatbots in Psychological Research: A Practical Tutorial
Year: 2026
Type: preprint
Abstract: Large language model (LLM)-based chatbots offer new methodological opportunities for psychological research by enabling scalable, standardised, and interactive research procedures. However, their usefulness depends on the researchers’ ability to customise, evaluate, and deploy them in ways that align with the methodological requirements of a given study. The current tutorial provides a practical, step-by-step framework for developing customised LLM-based chatbots for psychological research. We distinguish between no-code or low-code development approaches, including provider-hosted chatbot builders and visual or low-code programming platforms, and code-based approaches using proprietary model application programming interfaces (APIs) or locally hosted open-weight models. We compare these approaches in terms of technical expertise, behavioural and workflow control, study integration, cost, data governance, maintenance, and reproducibility. We also outline key components of the process of customising an LLM-based chatbot and illustrate deployment workflows across multiple platforms, including local desktop interfaces, messaging applications and survey platforms. Finally, we discuss key considerations for research ethics, privacy, data governance, security, transparency, and reproducibility, including recommendations for documenting and sharing chatbot configurations and study procedures. Through this tutorial, we aim to support researchers in integrating customised LLM-based chatbots into psychological research and in expanding the methodological possibilities available to them.
Source: PsyArXiv (OSF Preprints)
Authors: Hu Meilan, Gabriel Rongyang Lau, Adalia Yin Hui Goh, Samuel Rhys Cox, Louis Tay, Andree Hartanto
Language: English
Subfield: Applied Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7214403706