Open Science at the generative AI turn: An exploratory analysis of challenges and opportunities
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Abstract
Technology influences Open Science (OS) practices, because conducting science in transparent, accessible, and participatory ways requires tools and platforms for collaboration and sharing results. Due to this relationship, the characteristics of the employed technologies directly impact OS objectives. Generative Artificial Intelligence (GenAI) is increasingly used by researchers for tasks such as text refining, code generation/editing, reviewing literature, and data curation/analysis. Nevertheless, concerns about openness, transparency, and bias suggest that GenAI may benefit from greater engagement with OS. GenAI promises substantial efficiency gains but is currently fraught with limitations that could negatively impact core OS values, such as fairness, transparency, and integrity, and may harm various social actors. In this paper, we explore the possible positive and negative impacts of GenAI on OS. We use the taxonomy within the UNESCO Recommendation on Open Science to systematically explore the intersection of GenAI and OS. We conclude that using GenAI could advance key OS objectives by broadening meaningful access to knowledge, enabling efficient use of infrastructure, improving engagement of societal actors, and enhancing dialogue among knowledge systems. However, due to GenAI’s limitations, it could also compromise the integrity, equity, reproducibility, and reliability of research. Hence, sufficient checks, validation, and critical assessments are essential when incorporating GenAI into research workflows.
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Self-citationHigh-Quality Metadata: A Collective Responsibility and Opportunity
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An Empirical Study of the Non-determinism of ChatGPT in Code Generation
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Self-citationResnik, D. B., & Hosseini, M. (2026) · Accountability in Research · doi:10.1080/08989621.2025.2481949 · Cited by 104 · see citing works
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AI disclosure, moral shame, and the punishment of honesty
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Jack, T., Cooper, A., & Flower, L. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5299982 · Cited by 1 · see citing works
El uso de la Inteligencia Artificial Generativa en la Comunicación Científica
Nassi-Calò, L. (2025) · Revista Latino-Americana de Enfermagem · doi:10.1590/1518-8345.0000.4559
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Security Aspects in an Intelligent Lighting System for Public Squares using Fog Computing
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What Does an Editor Do in 2025?
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- Authors
- Hosseini, M., Horbach, S. P. J. M., Holmes, K., & Ross-Hellauer, T.
- Affiliations
- Northwestern University (US)
- Radboud University Nijmegen (NL)
- Open and Reproducible Research Group, Know-Center GmbH and Institute for Interactive Systems and Data Science, Graz University of Technology, Graz, Austria
- Corresponding author
- Hosseini, Mohammad
- Venue
- Quantitative Science Studies
- Publisher
- MIT Press
- Published
- 2025
- Volume
- 6
- Pages
- 22-45
- ISSN
- 2641-3337 (ISSN-L)
- PMC
- PMC11928019
- OpenAlex
- W4404059533
- Cited by
- 40
- References
- 97
- Citations by year
- 2026: 21 · 2025: 18 · 2024: 1
- FWCI
- 14.93
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Quantitative Science Studies (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion, cc-by-sa)
- Radboud Repository (Radboud University) (submittedVersion, cc-by-sa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- SocArXiv (OSF Preprints) (submittedVersion, cc-by-nd)
- Funders
- National Center for Advancing Translational Sciences (UL1TR001422, UM1TR005121)
- Office of Data Science Strategy (OTA-21-009, OTA Number OT2DB000013-01)
- U.S. National Library of Medicine (U24LM013751)
- Horizon Europe Programme (101094817)
- Know-Center (COMET—Competence Centers for Excellent Technologies)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Academic Publishing and Open Access
- Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- open science, generative AI, research integrity, transparency, reproducibility, UNESCO Recommendation on Open Science, research bias, research equity, research workflows
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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