AI evidence extraction

Scholarly Integrity and Generative AI : Five Boundary Violations for IS Scholarship

Jonny Holmström, Robert M. Davison · Information Systems Journal · 2026

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

9/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

7.53

FWCI

Top 10%

Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.1111/isj.70044

Methodology & findings

Study design

Editorial analysis and synthesis of institutional observations; non-empirical normative reflection on GAI boundary violations in academic publishing based on editorial experience at Information Systems Journal and broader scholarly discourse..

Main result

The editorial identifies five boundary violations in generative AI use for IS scholarship: "Recently, the ISJ and most other academic journals of repute have seen a rapid increase in boundary violations, that is, violations against the core values of academia, triggered by GAI use. Editors routinely encounter submissions in which reference lists contain hallucinated sources and real articles cited as support for claims they do not in fact make." The authors find that "Limited, disclosed, and human-verified uses, for example, language polishing, translation support, exploratory searching, or coding assistance checked by the authors, may be compatible with rigorous scholarship. Problems arise when GAI obscures provenance, simulates contextual understanding, hollows out theory, or displaces accountable human judgment."

Reports effect sizes.

Research paradigm

Normative/prescriptive; editorial guidance based on scholarly values and institutional experience

Author conclusions

The authors conclude that "Limited, disclosed, and human-verified use may be compatible with rigorous IS scholarship. What is incompatible is the substitution of polished machine output for contextual knowledge, theory development, practitioner sensitivity, and accountable human judgment." They emphasize that "Thinking slowly about GAI, then, is not a call to resist technology in the abstract. It is a call to protect the conditions under which IS research remains trustworthy, explanatory, contextually grounded, and useful."

Risk of bias

This is editorial commentary, not empirical research. No sample selection, control group, or quantitative comparisons exist. Potential perspectives: the authors represent a specific journal (ISJ) and may not capture disciplinary variation in GAI policies across other academic journals.

Limitations

The authors explicitly state: "It needs to be observed that these boundary violation types are indicative of the kinds of situations we see, but neither is this a complete list nor do the descriptions provide precise measures of how to determine if a boundary has been violated
Whereas hallucinated references are easier to detect, a GAI-generated summary may be much more difficult to confirm
A simple review of text may not be sufficient to reveal with a high degree of certainty that a particular boundary violation has occurred."

Open questions raised

The editorial identifies that "a broader scholarly consensus as to what constitutes appropriate use remains elusive. Researchers remain divided on what counts as acceptable GAI use in writing, analysis, and review." The authors note the need for clearer boundaries and proportionate editorial responses to distinguish between weak scholarship, theoretical hollowing, concealed use, and fabricated material.
The authors identify that "a broader scholarly consensus as to what constitutes appropriate use remains elusive. Researchers remain divided on what counts as acceptable GAI use in writing, analysis, and review." They note that "a complete list" of boundary violations is not yet established and precise measures for detecting violations require further development through scholarly conversation.
The paper identifies that "a broader scholarly consensus as to what constitutes appropriate use remains elusive" regarding GAI in academic work. It notes that the central question has shifted to "under what conditions that acceleration remains legitimate and accountable."

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