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AI evidence extraction

Use of Generative AI in Scholarly Research: Challenges and Opportunities

Marco Marabelli, Robert M. Davison, Giovanni Gatti, Ankita Srivastava, Monideepa Tarafdar · Journal of the Association for Information Systems · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

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Methodology & findings

Study design

Panel discussion report documenting a panel discussion held in October 2025 with publishers and editors in chief regarding generative AI use in academic research and publishing.

Sample

< 30, 2 groups

Primary method

No statistical methods were employed. This is a qualitative panel report, not a quantitative study.

Main result

The panel identified four key outcomes: "1) publishers and editors have some disagreements regarding GAI use; 2) besides disagreements, all recognize that GAI is here to stay, therefore outstanding issues (e.g., training for PhD students and junior faculty and consequences for unallowed uses of GAI) need to be addressed jointly; 3) GAI is in constant flux, and this is a challenge for publisher and journal policies; and 4) it is unclear the extent to which GAI will affect future scholarship outcomes, especially with in mind potential for scholarly deskilling."

Reports effect sizes.

Research paradigm

Qualitative/Interpretive

Author conclusions

The authors conclude that "publishers and editors have some disagreements regarding GAI use" but "all recognize that GAI is here to stay, therefore outstanding issues (e.g., training for PhD students and junior faculty and consequences for unallowed uses of GAI) need to be addressed jointly" while acknowledging that "GAI is in constant flux, and this is a challenge for publisher and journal policies."

Risk of bias

Selection bias: Panel participants were primarily representatives from major publishers and journals, potentially not representative of broader scholarly community views; Lack of systematization: No indication of systematic sampling or data collection protocols; Reporting bias: Panel outcomes reported without indication of methodological rigor or inter-rater agreement

Open questions raised

  • The panel identified the need to address training for PhD students and junior faculty, consequences for unallowed uses of GAI, development of publisher and journal policies to account for GAI's constant flux, and clarification of how GAI will affect future scholarship outcomes, particularly regarding potential scholarly deskilling.
  • The panel identified that outstanding issues need to be addressed jointly, including training for PhD students and junior faculty in GAI use, consequences for unallowed uses of GAI, and the need for publisher and journal policies to adapt to the constant flux of GAI technology. Additionally, the impact of GAI on future scholarship outcomes remains unclear.
  • The authors identify several gaps: (1) need for training protocols for PhD students and junior faculty on GAI use; (2) need to address consequences for unallowed uses of GAI; (3) need for publisher and journal policies to keep pace with rapidly changing GAI technology; (4) uncertainty regarding how GAI will affect future scholarship outcomes and potential scholarly deskilling.
Data: not_statedCode: not_statedExtracted from: pdfAgreement 75%

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