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Generative Artificial Intelligence (GenAI) in the research process – A survey of researchers’ practices and perceptions

Author

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  • Andersen, Jens Peter
  • Degn, Lise
  • Fishberg, Rachel
  • Graversen, Ebbe K.
  • Horbach, Serge P.J.M.
  • Schmidt, Evanthia Kalpazidou
  • Schneider, Jesper W.
  • Sørensen, Mads P.

Abstract

This study explores the use of generative AI (GenAI) and research integrity assessments of use cases by researchers, including PhD students, at Danish universities. Conducted through a survey sent to all Danish researchers from January to February 2024, the study received 2534 responses and evaluated 32 GenAI use cases across five research phases: idea generation, research design, data collection, data analysis, and writing/reporting. Respondents reported on their own and colleagues' GenAI usage. They also assessed whether the practices in the use cases were considered good research practice. Through an explorative factor analysis, we identified three clusters of perception: "GenAI as a work horse", "GenAI as a language assistant only", and "GenAI as a research accelerator". The findings further show varied opinions on GenAI's research integrity implications. Language editing and data analysis were generally viewed positively, whereas experiment design and peer review tasks faced more criticism. Controversial areas included image creation/modification and synthetic data, with comments highlighting the need for critical and reflexive use of GenAI. Usage differed by main research area, with technical and quantitative sciences reporting slightly higher usage and more positive assessments. Junior researchers used GenAI more than senior colleagues, while no significant gender differences were observed. The study underscores the need for adaptable, discipline-specific guidelines for GenAI use in research, developed collaboratively with experts to align with diverse research practices and minimize ethical and practical misalignment.

Suggested Citation

  • Andersen, Jens Peter & Degn, Lise & Fishberg, Rachel & Graversen, Ebbe K. & Horbach, Serge P.J.M. & Schmidt, Evanthia Kalpazidou & Schneider, Jesper W. & Sørensen, Mads P., 2025. "Generative Artificial Intelligence (GenAI) in the research process – A survey of researchers’ practices and perceptions," Technology in Society, Elsevier, vol. 81(C).
    • Handle: RePEc:eee:teinso:v:81:y:2025:i:c:s0160791x2500003x
    DOI: 10.1016/j.techsoc.2025.102813
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    2. Njuguna, Gerald Nderitu & Qingfei, Min, 2026. "How would prompt completion editing impact user experience scores in academic research with large language models?," Technology in Society, Elsevier, vol. 84(C).
    3. Liu, Yiming & Wang, Tianhong, 2025. "Treating differently or equally: A study exploring attitudes towards AI moral advisors," Technology in Society, Elsevier, vol. 82(C).
    4. Suárez, David & García-Mariñoso, Begoña, 2025. "On the verge of a digital divide in the use of generative AI?," Telecommunications Policy, Elsevier, vol. 49(7).
    5. Deng, Ruolan & Ahmed, Saifuddin, 2025. "Perceptions and paradigms: An analysis of AI framing in trending social media news," Technology in Society, Elsevier, vol. 81(C).
    6. He, Hao & Bai, Shizhen & Han, Chunjia & Yang, Mu & Fan, Weijia & Gupta, Brij B., 2025. "Beyond simple interaction: Uncovering the perception-interaction intrinsic mechanism of generative AI agents—A multi-modal big data analysis with PLS-SEM and fsQCA," Technology in Society, Elsevier, vol. 83(C).

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