Medina, V. A., & Mohan, M. (2025).
Journal of Ethics in Entrepreneurship
and Technology, 1–10.
Purpose
The potential for large language models (LLMs) to improve behavioral science research has generated significant discussion. But, the specific role that LLMs should serve in behavioral research, especially in terms of simulating human participants, remains an open research question. The purpose of this work is to engage with this open question and address a critical gap in the literature stemming from the lack of a practical framework for realistically using artificial research participants.
Design/methodology/approach
Google Scholar was systematically searched for modern, peer-reviewed literature. Additional articles were found by both backward and forward citation searching the relevant articles. Exclusion criteria were articles that were not directly related to artificial intelligence (AI) and/or research participants, and articles written in a language other than English. This approach resulted in 26 citations that comprehensively capture current perspectives.
Findings
This study proposes two novel stances: that artificial research participants can complement human participants during data collection, and replace human participants during pilot testing. This framework engages with the open question of artificial research participants usage while addressing a framework gap in the literature.
Originality/value
This workadvances discourse LLMs potentially transforming behavioral science by establishing a framework differentiating the use of artificial research participants in data collection versus pilot testing. This study reinforces this framework with clear implementation guidelines that maximize the strengths of AI while respecting the human element and the methodological integrity of behavioral research.
Here are some thoughts:
This paper matters for practicing psychologists because it signals a meaningful and near-term shift in how behavioral research will be conducted (which directly affects the evidence base clinicians rely on). For those who conduct or supervise research, it offers the first concrete guidance on a question that has been debated without resolution: LLMs aren't ready to replace human participants in full data collection, but they may already be capable of improving pilot testing and serving as a useful check on the robustness of findings. Used carefully, transparently, and with an awareness of their limitations (particularly their tendency to flatten human variability on ambiguous topics like morality), artificial research participants represent a practical efficiency gain, especially for researchers working with limited participant pools or tight budgets. Staying informed about this framework now puts psychologists in a better position to critically evaluate the research they read, ask good questions about how studies were conducted, and make thoughtful decisions about whether and how to incorporate these tools into their own work.
