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Can AI language models replace human participants?

Danica Dillion1, Niket Tandon2, Yuling Gu2

  • 1University of North Carolina, Department of Psychology and Neuroscience, Chapel Hill, NC 27599-3270, USA.

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Summary
This summary is machine-generated.

Large language models (LLMs) show human-like judgment capabilities, potentially replacing human participants in psychological science research. This work reviews current AI research and proposes a model for AI as a participant.

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artificial intelligencejudgmentslanguage modelsmoralityparticipantsresearch methods

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Area of Science:

  • Psychological Science
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Emerging research indicates that advanced language models, such as Generative Pre-trained Transformers (GPT), exhibit human-like judgment across diverse domains.
  • The increasing sophistication of AI raises questions about its potential role within scientific research methodologies.

Approach:

  • This study reviews existing literature on the use of AI, specifically LLMs, as participants in psychological studies.
  • A theoretical model is proposed to conceptualize the integration of AI participants.
  • Potential limitations and ethical considerations of employing AI in research are discussed.

Key Points:

  • LLMs demonstrate capacity for human-like decision-making and judgment.
  • AI can potentially serve as a novel participant type in psychological research.
  • Methodological frameworks are needed to guide the use of AI participants.

Conclusions:

  • While promising, the use of AI as participants requires careful consideration of its current limitations.
  • Further research is necessary to validate AI performance and establish best practices for its integration into psychological science.
  • AI offers a potential supplement, not a complete replacement, for human participants in the foreseeable future.