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The rapid integration of generative artificial intelligence (AI) into academic and professional workflows is reshaping human participants research. While prior discourse has focused largely on AI-assisted writing and analysis, less attention has been given to its influence on data generation and integrity. This invited commentary examines emerging risks associated with generative AI use by researchers and participants, including data fabrication and AI-mediated participant responses. These developments challenge foundational assumptions regarding data authenticity, measurement validity, and the evidentiary basis of research. Implications for research ethics oversight, particularly for institutional review boards and research ethics committees, are also explored, including the need to consider transparency, data provenance, and methodological accountability in AI-enabled research environments. Practical strategies are proposed to mitigate risks. Ultimately, human participants research will require a shift toward systems that balance trust-based models with structured verification mechanisms, alongside the development of clear institutional and international guidelines for responsible AI integration.
Melissa Singer Pressman (Wed,) studied this question.