Can Large Language Models Replace Human Subjects? A Large-Scale Replication of Scenario-Based Experiments in Psychology and Management

Abstract

Artificial Intelligence (AI) is increasingly being integrated into scientific research, particularly in the social sciences, where understanding human behavior is critical. Large Language Models (LLMs) have shown promise in replicating human-like responses in various psychological experiments. We conducted a large-scale study replicating 156 psychological experiments from top social science journals using three state-of-the-art LLMs (GPT-4, Claude 3.5 Sonnet, and DeepSeek v3). Our results reveal that while LLMs demonstrate high replication rates for main effects (73-81%) and moderate to strong success with interaction effects (46-63%), They consistently produce larger effect sizes than human studies, with Fisher Z values approximately 2-3 times higher than human studies. Notably, LLMs show significantly lower replication rates for studies involving socially sensitive topics such as race, gender and ethics. When original studies reported null findings, LLMs produced significant results at remarkably high rates (68-83%) - while this could reflect cleaner data with less noise, as evidenced by narrower confidence intervals, it also suggests potential risks of effect size overestimation. Our results demonstrate both the promise and challenges of LLMs in psychological research, offering efficient tools for pilot testing and rapid hypothesis validation while enriching rather than replacing traditional human subject studies, yet requiring more nuanced interpretation and human validation for complex social phenomena and culturally sensitive research questions.