The integration of generative artificial intelligence (GenAI) into academia presents profound ethical challenges to research integrity. Disciplinary norms likely shape engagement with this technology, yet a comparative analysis between medical and non-medical research cultures is lacking. This study examines how this critical divide influences the adoption of GenAI, the development of ethically sensitive behaviors, and perceptions of oversight in Chinese universities. A cross-sectional online su…
Read moreThe integration of generative artificial intelligence (GenAI) into academia presents profound ethical challenges to research integrity. Disciplinary norms likely shape engagement with this technology, yet a comparative analysis between medical and non-medical research cultures is lacking. This study examines how this critical divide influences the adoption of GenAI, the development of ethically sensitive behaviors, and perceptions of oversight in Chinese universities. A cross-sectional online survey was administered to 5,731 active researchers (including postdoctoral fellows and doctoral, graduate, and undergraduate students) from 48 universities, categorized as medical (n = 1,935) or non-medical researchers (n = 3,796). The instrument measured the frequency of GenAI adoption, integrity awareness, self-reported behaviors (including data fabrication/falsification), and ethical attitudes. Analyses used the chi-square test and independent-samples t-tests. Non-medical researchers reported higher GenAI adoption (57.0% vs. 45.6%, p