Is generative AI reshaping academic practices worldwide? A survey of adoption, benefits, and concerns

• Generative AI tools are highly adopted in academia, with significant differences across fields, genders, and countries. • PhD students and early-career academics are the highest adopters for research purposes. • AI applications are widely used for translation, proofreading, and literature review, but less for data analysis in research activities. • Content creation, learning support, and assignment design are key reasons for using AI in teaching, with different patterns based on academic positions and countries. • Inaccurate information, plagiarism, and reduced critical thinking skills are the top concerns of AI use in academia. Although generative AI is transforming academic research and education, little is known about the role, gender, international, and disciplinary variations in uptake and use. This 20-country survey of publishing academics shows the widespread awareness and adoption of generative AI tools in academia, but with substantial international and disciplinary differences, and some role and gender differences. In particular, females were 10 % less likely to use Gen AI frequently (daily or weekly) for research, which may exacerbate gender inequalities. Perhaps surprisingly, the highest adoption rates occurred in some non-Western nations, possibly because of a greater need for translation services. The highest awareness is in the social sciences, perhaps because of the greater need for text analysis. Across all groups, these tools were mainly used for academic writing rather than data analysis and support for critical thinking. Despite this, personalized instruction and problem-solving are among generative AI's most generally claimed benefits. However, participants in all groups were skeptical about the creativity, accuracy, and consistency of AI-generated content in academic contexts. The most significant concerns about using generative AI in academia were inaccuracy, plagiarism, discouraging critical thinking, a lack of transparency and explainability, intellectual property rights violations, and data privacy risks. For policymakers, the findings point to fields and countries that may need action to prevent falling behind, as well as the ongoing need to investigate and monitor the impacts of generative AI on research practices.