AI evidence extraction

Editorial: Generative AI, human authorship and the transformation of scholarly communication

Luis Hernan [UNIFESP] Contreras Pinochet, Kavita Miadaira Hamza, Yogesh Kumar Dwivedi · Revista de Gestão · 2026

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

9/10

Relevance

0/4

Quality (LMQS)

Evidence

Citations

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.1108/rege-04-2026-0058

Methodology & findings

Study design

Hermeneutic analysis and normative editorial commentary synthesizing recent literature on generative AI ethics, scholarly communication, and research integrity.

Main result

The paper establishes that "the central issue is not whether AI should be used in academic writing, but how, to what extent and under whose responsibility" and emphasizes that "ethical engagement with GenAI in academic writing should not be understood as a binary choice between acceptance and prohibition, but as a process of progressive ethical maturation." The authors identify a critical distinction: "AI-generated writing refers to situations in which AI systems autonomously produce substantial portions of academic texts without meaningful human intellectual control" versus AI-assisted writing where "human authors retain full responsibility for conceptual development, theoretical coherence, methodological rigor and final editorial decisions."

Research paradigm

Critical interpretivism with normative ethical analysis

Author conclusions

The authors conclude that "the ethical governance of AI-assisted writing is most robust when grounded in proportionality and responsibility rather than technological restriction" and that "meaningful human control throughout the research process" is essential. They argue that "editorial policies that articulate proportional expectations regarding AI use, including how and to what extent AI assistance should be disclosed, can preserve research integrity without impeding legitimate methodological innovation." The authors stress that "human authors must remain the primary agents of framing, interpretation, validation and judgment throughout the scholarly process" and that "what is required, therefore, is not merely procedural compliance but the preservation of cognitive responsibility."

Risk of bias

Selection bias in review of editorials and cited literature; Potential confirmation bias in selecting sources supporting specific ethical frameworks; Editorial perspective may not represent all stakeholder viewpoints (particularly those of authors using AI); Recency bias toward current GenAI concerns without historical perspective on previous technological transitions

Limitations

The paper identifies that "editorial policies that attempt to ban the use of GenAI in academic writing face three structural limitations
First, such bans are largely unenforceable given the current and foreseeable limitations of AI detection technologies." Additionally, the authors note that "the reliability of detection tools remains contested, and researchers have raised significant concerns about false positives and methodological opacity in AI detection mechanisms." The paper also acknowledges that "recent debates in publication ethics have highlighted" the challenge of distinguishing between acceptable and unacceptable AI use, and that "in the absence of clear editorial guidance, researchers often rely on personal judgment, disciplinary norms or perceived editorial tolerance, leading to uneven ethical reasoning."

Open questions raised

The authors identify the need for discipline-specific editorial guidance on GenAI use, standardized provenance records for AI-assisted passages, reviewer competencies in evaluating AI-mediated manuscripts, training for editorial teams to distinguish AI-generated versus AI-assisted writing, and integrated editorial governance models that balance formal compliance with ethical judgment rather than relying on detection technologies.
The authors identify the need for: (1) harmonized policies across journals and publishers to address fragmentation in AI guidance; (2) discipline-specific editorial guidance to clarify acceptable AI use by research stage; (3) development of new competencies for peer reviewers in evaluating AI-assisted manuscripts; (4) standardized disclosure frameworks that can be consistently applied across editorial workflows; (5) institutional guidelines and training for both editorial teams and peer reviewers; (6) research on the cognitive dynamics and psychological effects of AI-assisted writing practices on scholarly reasoning and intellectual diversity.
The paper identifies several gaps: (1) lack of harmonized policies across journals and publishers regarding GenAI use; (2) absence of discipline-specific guidance for AI use in academic writing; (3) limited understanding of cognitive dynamics shaping how authors interpret AI involvement (the 'AI ghostwriter effect'); (4) need for peer reviewer competencies in recognizing variable performance profiles of LLMs; (5) insufficient training for editorial staff on distinguishing AI-generated versus AI-assisted content; (6) gaps in understanding systemic risks of GenAI normalization on scholarly argumentation homogenization and intellectual diversity.

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