The Future of Research in an Artificial Intelligence-Driven World
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Current and future developments in artificial intelligence (AI) systems have the capacity to revolutionize the research process for better or worse. On the one hand, AI systems can serve as collaborators as they help streamline and conduct our research. On the other hand, such systems can also become our adversaries when they impoverish our ability to learn as theorists, or when they lead us astray through inaccurate, biased, or fake information. No matter which angle is considered, and whether we like it or not, AI systems are here to stay. In this curated discussion, we raise questions about human centrality and agency in the research process, and about the multiple philosophical and practical challenges we are facing now and ones we will face in the future.
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The use of AI complicates traditional concepts of authorship and scholarship, raising questions about what constitutes independent academic work.
“Moreover, the use of AI complicates traditional concepts of authorship and scholarship, raising questions about what constitutes independent academic work (Kulkarni et al., 2024; Luo, 2024).”
From Participatory Co-Design and Evaluation of a Novel Approach to Generative AI-Integrated Coursework Assessment in Higher Education (2025) · cites this paper as Kulkarni et al., 2024
AI use may complicate traditional definitions of scholarship and independent academic work.
“This echoes broader challenges in the literature, where AI use may complicate traditional definitions of scholarship and independent academic work (Kulkarni et al., 2024; Luo, 2024; Yusuf et al., 2024).”
From Participatory Co-Design and Evaluation of a Novel Approach to Generative AI-Integrated Coursework Assessment in Higher Education (2025) · cites this paper as Kulkarni et al., 2024
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Cited by
Sorted by citations
Martin, A. F., Tubaltseva, S., Harrison, A., & Rubin, G. J. (2025) · Behavioral Sciences · doi:10.3390/bs15060808 · Cited by 16 · see citing works
“This echoes broader challenges in the literature, where AI use may complicate traditional definitions of scholarship and independent academic work (Kulkarni et al., 2024; Luo, 2024; Yusuf et al., 2024).”
Developing the HAKI model as a conceptual framework for AI-assisted academic research
Shahini, A. (2026) · Discover Education · doi:10.1007/s44217-026-02041-4
“These views are consistent with recent research suggesting that widespread use of large language models may contribute to increasing uniformity in academic writing by favouring statistically common linguistic patterns and conventional forms of expression [49].”
- Authors
- Kulkarni, M., Mantere, S., Vaara, E., van den Broek, E., Pachidi, S., Glaser, V. L., Gehman, J., Petriglieri, G., Lindebaum, D., Cameron, L. D., Rahman, H. A., Islam, G., & Greenwood, M.
- Affiliations
- Indian Institute of Management Bangalore (IN)
- McGill University (CA)
- University of Oxford (GB)
- Stockholm School of Economics, Stockholm, Sweden
- University of Cambridge (GB)
- University of Alberta (CA)
- George Washington University (US)
- INSEAD (FR)
- Grenoble Ecole de Management (FR)
- University of Pennsylvania (US)
- Northwestern University (US)
- Monash University (AU)
- Corresponding author
- Kulkarni, Mukta
- ORCID
- Venue
- Journal of Management Inquiry
- Publisher
- SAGE Publications
- Published
- 2024-02-22
- Volume
- 33
- Issue
- 3
- Pages
- 207-229
- ISSN
- 1056-4926 (ISSN-L)
- 1552-6542
- OpenAlex
- W4392109933
- Cited by
- 80
- References
- 17
- Citations by year
- 2026: 37 · 2025: 29 · 2024: 13 · 2023: 1
- FWCI
- 11.70
- Type
- journal article
- Language
- en
- Open access
- Locations
- Journal of Management Inquiry (publishedVersion)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Apollo (cc-by)
- Funders
- University of Alberta
- Times Higher Education
- Social Sciences and Humanities Research Council of Canada
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Keywords
- artificial intelligence in research, algorithmic bias, fake information, AI misinformation, future of research, research philosophy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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