Large language models in psychological science
- Submission status
- Open
- Submission deadline
This Collection supports and amplifies research related to SDG 3- Good health and wellbeing.
Large language models (LLMs) — notably OpenAI’s GPT-4, Google’s Gemini and Meta’s LLaMa — have created unique opportunities for analysing and generating research data efficiently on a massive scale.
Given the central role played by language data in all areas of psychological science, new AI technologies have the potential to radically transform the field and unlock unprecedented opportunities.
Large language models (LLMs) — notably OpenAI’s GPT-4, Google’s Gemini and Meta’s LLaMa — have created unique opportunities for analysing and generating research data efficiently on a massive scale. Given the central role played by language data in all areas of psychological science, new AI technologies have the potential to radically transform the field and unlock unprecedented opportunities. This collection specifically aims to explore the value and impact of LLMs in psychological measurement, experimentation, and practice. Research relevant to all branches of psychology, including cognitive, behavioural, social and cultural, educational and in psychotherapy, is welcomed.
Original research and Reviews are invited on a range of themes, including but not limited to:
- Effectiveness of LLMs to mimic human-like linguistic outputs, cognition and behaviour;
- Current limitations of applying LLMs in psychological research;
- Strategies to evaluate and improve LLM performance and accuracy;
- Potential painpoints (e.g., issues relating to reproducibility, bias, technical challenges and ethical concerns);
- Other perspectives on LLMs (e.g., policy-related, open science)
- Implications and potential of AI and LLM usage on allied areas of research (e.g., computational linguists, mental health, education, and psychotherapy).
Theoretical research that does not have an explicit connection to the application of LLMs in psychological science will be considered out of scope.
