Scientific Authorship in the Age of AI: Challenges for Editors and Institutions

This work is licensed under a Creative Commons Attribution 4.0 International License. The license permits unrestricted use, distribution, and reproduction in any medium, on the condition that users give exact credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if they made any changes. Scientific Authorship in the Age of AI: Challenges for Editors and Institutions Konstantinos T. Kotsis  Department of Primary Education, University of Ioannina, Greece Abstract The extensive utilization of generative artificial intelligence (AI) tools like ChatGPT in academic writing poses significant challenges to conventional paradigms of scientific authorship and editorial oversight. As a conceptual and theoretical contribution, this article critically analyzes the ramifications of AI integration on fundamental practices of scholarly communication, such as peer review, authorship attribution, plagiarism detection, and institutional accountability. Utilizing recent interdisciplinary literature, the paper contends that the growing integration of AI in the writing process challenges traditional notions of originality, intellectual contribution, and the ethical limits of authorship. It examines the epistemological conflicts that arise when non-sentient systems engage in knowledge production and evaluates the dangers associated with AI-generated hallucinations, citation fabrication, and unrecognized automation. The article emphasizes the deficiencies of current editorial policies and academic integrity frameworks in confronting these emerging challenges. It suggests a redefinition of human–AI collaboration grounded in the principles of transparency, accountability, and rigorous oversight. The paper advocates for collaborative efforts among editors, institutions, and educators to ensure that academic standards and practices develop in a manner that preserves the credibility, accountability, and inclusivity of scholarly publishing in the era of AI. Keywords: AI-assisted writing, authorship ethics, scientific publishing, peer review, academic integrity. JEL Classification codes: O33, I23, D83, K23. Suggested citation: Kotsis, K.T. (2025). Scientific Authorship in the Age of AI: Challenges for Editors and Institutions. European Journal of Management, Economics and Business, 2(6), 209-216. DOI: 10.59324/ejmeb.2025.2(6).15 Introduction The emergence of generative artificial intelligence (AI) technologies like ChatGPT has instigated a fundamental transformation in academic writing, prompting essential inquiries regarding authorship, originality, and scholarly accountability. As large language models are increasingly integrated into research and publication processes, the distinctions between human and machinegenerated contributions are becoming indistinct (Kotsis, 2024a). The formerly human-centric process of intellectual creation is now intertwined with algorithmic intervention, raising significant concerns among educators, editors, and policymakers about the legitimacy and attribution of AIassisted texts. EJMEB (ISSN 3041-2102) | VOLUME 2 | NUMBER 6 | 2025 210 The ability of AI to produce coherent and contextually relevant academic writing presents both advantages and risks to the academic publishing system. AI tools can improve productivity, linguistic precision, and accessibility for non-native speakers (Mhlanga, 2023). Conversely, they contest prevailing definitions of plagiarism, authorship, and academic integrity—particularly when human supervision is limited or absent. This has resulted in diverse reactions from academic journals, including complete prohibitions on AI-generated content and conditional acceptance requiring disclosure (Cotton et al., 2024; Stokel-Walker, 2023). The academic community is at a pivotal juncture. Some perceive AI as a neutral instrument, whereas others contend it should be regarded as a co-author, contributor, or even an agent in the production of knowledge (Floridi, 2023). These inquiries are not solely theoretical; they possess significant ramifications for editorial choices, institutional policies, and the future of scientific discourse. This paper seeks to examine the redefinition of scientific authorship in the era of artificial intelligence. It rigorously analyzes institutional and editorial obstacles, ethical quandaries, and nascent policy frameworks that seek to address this swiftly changing environment. This article is a conceptual and theoretical research contribution. It synthesizes recent interdisciplinary literature to develop a critical framework for understanding how generative artificial intelligence reshapes authorship, editorial ethics, and institutional accountability. By adopting the structure and rigor of a research article rather than an editorial or commentary, it seeks to establish a conceptual basis that can inform future empirical studies and policy initiatives in the evolving discourse on AI and academic integrity. The Disruption of Traditional Authorship The advent of generative AI has disrupted traditional notions of authorship by creating entities capable of generating coherent, grammatically accurate, and contextually appropriate scientific writing without human thought or responsibility. Conventional notions of authorship—rooted in intentionality, accountability, and intellectual contribution—are being challenged by AI systems that can emulate these traits without genuinely embodying them (Amirjalili et al., 2024; Formosa et al., 2025). This prompts essential inquiries: Should AI be recognized as an author? What defines a valid contribution? How can we guarantee transparency in human-machine collaborations? These debates are also intertwined with legal concerns regarding the human authorship requirement in copyright law (Barr, 2025; Gaffar & Albarashdi, 2025). Some scholars argue that the very notion of an ‘AI author’ is conceptually untenable, marking what has been described as the ‘death of the AI author’ (Craig & Kerr, 2025). Certain journals, including Nature and Science, have adopted stringent policies that forbid the inclusion of AI tools as co-authors and mandate explicit disclosure of their usage. These policies address apprehensions regarding AI's incapacity to take responsibility for scientific assertions, engage in peer review, or guarantee the verifiability of research (Stokel-Walker, 2023). Nevertheless, such prohibitions may undervalue the intricate and frequently interdependent role of AI assistance in scientific writing—particularly when generative tools are employed for organizing arguments, enhancing language, or proposing citations. The ambiguity of authorship boundaries is exacerbated by the collaborative nature of academic writing. When researchers utilize AI to collaboratively generate paragraphs, condense intricate literature, or compose entire sections, the issue of original thought attribution becomes ambiguous. Research shows this phenomenon clearly in what has been termed the ‘AI ghostwriter effect,’ where users disclaim ownership of AI text yet still present themselves as authors (Draxler et al., 2024). Scholars like Floridi (2023) contend that although AI lacks agency, it serves as a coconstitutor of the final text. This perspective indicates the necessity for a redefinition of authorship, EJMEB (ISSN 3041-2102) | VOLUME 2 | NUMBER 6 | 2025 211 differentiating between intellectual authorship, mechanical assistance, and editorial intervention (Vinall & Hellmich, 2024; Mazzi, 2024). Furthermore, conventional indicators of academic proficiency—such as writing clarity, argumentative coherence, and linguistic sophistication—are increasingly shaped by algorithmic literacy. This poses a risk of establishing an inequitable environment in which scholar’s dependent on AI may seem more proficient, whereas those who write independently may face more severe evaluations (Kotsis, 2024a). The cultural capital of academic writing is consequently reallocated in manners that warrant further contemplation and policy intervention. Editorial Ethics and the Role of Scientific Journals The academic publishing editorial ecosystem confronts unparalleled ethical dilemmas as AIgenerated content permeates scientific communication. Editors are now required to assess both the scientific validity of submissions and the authenticity and integrity of their textual composition. The crux of this dilemma is whether AI-generated or AI-assisted texts fulfill the conventional standards of originality, transparency, and accountability in scholarly communication (StokelWalker, 2023; Park et al., 2023). The evolving interplay between human and machine contributions complicates traditional peer review methodologies, requiring the establishment of novel editorial standards and ethical protections, particularly as legal analyses stress that the notion of authorship itself is under pressure from AI-generated contributions (Lee, 2024; Bozkurt, 2024). Numerous prestigious journals have issued position statements or guidelines concerning the permissible use of AI tools. Science explicitly forbids the utilization of AI-generated text without complete disclosure and does not recognize AI systems as authors under any circumstances, citing AI's incapacity to assume responsibility for the content (Stokel-Walker, 2023). Other publishers, such as Springer Nature and Elsevier, have implemented policies mandating authors to disclose AI assistance and specify the degree of its contribution (Cotton et al., 2024). Notwithstanding these endeavors, discrepancies persist among journals and disciplines, resulting in a disjointed framework of editorial practices. Ethical dilemmas emerge not only from the utilization of AI in manuscript creation but also from its prospective implementation in the peer review process. Certain journals utilize automated systems to evaluate submissions, detect plagiarism, or facilitate initial triage. Although these tools may enhance efficiency, they pose a risk of perpetuating algorithmic biases or neglecting the nuanced discernment that human evaluators contribute to scholarly assessment (van Dis et al., 2023). Furthermore, the ethical obligations of editors are intensified when managing submissions that incorporate AI in undisclosed or covert manners, as these instances may compromise the integrity of both the peer review process and the published record, highlighting the urgent need for community consensus on ethical publishing standards in the age of AI (Wilson, 2024). Considering these complexities, there is an immediate necessity for collaborative editorial frameworks that delineate explicit standards for the utilization of AI in writing and reviewing. These frameworks must transcend mere technical compliance to incorporate the overarching ethical principles of trust, responsibility, and epistemic transparency in scientific dialogue. Park et al. (2023) and Wise et al. (2024) assert that a strong editorial ethics framework is crucial for preserving the integrity of academic publishing in the age of AI. EJMEB (ISSN 3041-2102) | VOLUME 2 | NUMBER 6 | 2025 212 Institutional and Policy Responses to AI-Generated Texts As generative AI capabilities advance swiftly, universities, funding organizations, and academic institutions are contending with the necessity to formulate coherent institutional responses that uphold academic integrity while recognizing the legitimate applications of these technologies. The conflict exists between fostering innovation in research productivity and safeguarding fundamental academic principles, including authorship accountability, transparency, and critical thinking (Cotton et al., 2024; Mhlanga, 2023). Institutional responses have varied from informal guidance to formal policies; however, many are still in an early stage, struggling to adapt to the technological and ethical complexities involved. Universities have implemented various strategies regarding AI in academic writing. Some institutions have implemented honor code amendments or AI usage declarations in coursework and theses, while others have created faculty task forces to oversee developments and establish guidelines. These measures seek to delineate the parameters of permissible AI utilization, underscoring the necessity for human supervision and complete transparency in all academic submissions (Park et al., 2023). The execution of these policies is frequently inconsistent, indicating a wider ambiguity regarding the impact of AI on both practices and pedagogical beliefs concerning student authorship and researcher independence. Academic associations and research councils are also starting to contribute their perspectives. In response to the increasing utilization of ChatGPT in academic settings, certain professional organizations have released position statements asserting that AI should not be credited as an author and must be employed transparently (Stokel-Walker, 2023). Some have proposed the establishment of sector-wide standards to synchronize institutional practices with those of scientific publishers, thereby ensuring consistency throughout the research ecosystem, a point echoed in discussions of copyright and consensus in AI-era publishing ethics (Wilson, 2024). Notwithstanding these emerging frameworks, deficiencies persist. Numerous institutions lack explicit enforcement mechanisms or educational resources for staff and students, and few offer comprehensive training on the ethical ramifications of AI-assisted writing (Mhlanga, 2023). Furthermore, the ethical governance of AI in education should be proactive instead of reactive, foreseeing potential risks, enhancing ethical literacy, and encouraging discourse across disciplinary and institutional boundaries (Kotsis, 2025; Balalle & Pannilage, 2025). The institutional response to AI in academic writing must extend beyond mere technical regulation. It should foster a culture of accountability, transparency, and critical reflection—empowering both researchers and students to navigate the advantages and constraints of AI with ethical consciousness and academic integrity. Implications for Peer Review, Plagiarism, and Accountability The incorporation of generative AI into academic writing presents intricate ramifications for peer review, plagiarism detection, and the overarching structure of scholarly accountability. Peer review, as the foundation of academic quality assurance, relies on the assumption that submissions represent the original ideas, effort, and accountability of the identified authors (Formosa et al., 2025; Draxler et al., 2024). The generation or substantial alteration of extensive text by AI obscures authorship boundaries, complicating reviewers' capacity to evaluate the authenticity and originality of academic contributions (Park et al., 2023; van Dis et al., 2023). AI-assisted writing may exaggerate the perceived proficiency of manuscripts—particularly in linguistic fluency and logical coherence—thereby potentially leading reviewers to overrate the academic quality of a submission. This issue is especially prominent in fields where argumentation EJMEB (ISSN 3041-2102) | VOLUME 2 | NUMBER 6 | 2025 213 and rhetorical precision are key evaluative standards. Furthermore, contemporary plagiarism detection tools frequently do not recognize AI-generated material unless it replicates established sources verbatim, thereby revealing a weakness in current academic integrity frameworks (Mhlanga, 2023; Balalle & Pannilage, 2025). The accountability dilemma intensifies when addressing the epistemic risks associated with AI hallucinations, which involve the creation of erroneous or fabricated content, encompassing references, data, and assertions (Kotsis, 2024a). The uncritical incorporation of AI-generated content may disseminate misinformation in academic literature. However, due to AI's lack of agency, it cannot be held accountable for these errors, and responsibility ultimately resides with the human creators, a principle also reinforced in legal and philosophical debates on originality and copyright in AI outputs (Mazzi, 2024; Gaffar & Albarashdi, 2025). This has led to demands for a redefinition of authorship criteria to prioritize human oversight and verifiability as essential requirements for scholarly legitimacy (Kotsis, 2024b; Moffatt & Hall, 2024). In reaction to these challenges, certain journals are formulating AI-specific peer review protocols or educating reviewers to identify distinctive indicators of machine-generated text. Nonetheless, these initiatives are inconsistent, and the swift advancement of AI tools indicates that detection methods may be outdated. Ethical peer review in the age of AI necessitates not only technological enhancements but also a reinvigorated focus on transparency, disclosure, and collective accountability among authors, reviewers, and editors. Redefining Human–AI Collaboration in Scientific Writing The growing prevalence of AI in scientific writing necessitates a fundamental reevaluation of the distinctions between human and machine contributions. Instead of perceiving AI merely as a menace to academic integrity or a passive instrument for routine tasks, certain scholars propose a more sophisticated perspective of AI as a collaborator in the scholarly process—capable of augmenting human creativity, assisting multilingual authors, and improving clarity in scientific communication (Floridi, 2023; Kotsis, 2024a). This reconceptualization necessitates new definitions of collaboration that transcend authorship credit and prioritize epistemic transparency and collective accountability. In the conventional collaboration model, all enumerated authors are presumed to have made intellectual contributions and to bear responsibility for the work's integrity. In contrast, AI systems are incapable of asserting intentions, defending arguments, or addressing critiques. As AI integrates into the writing process—offering sentence structure suggestions, translating concepts across languages, summarizing sources, and proposing titles—it occupies a role that is more dynamic than simple grammar correction yet less independent than human judgment (van Dis et al., 2023). This ambiguous domain complicates the dichotomy between author and instrument, raising broader questions of ‘whose words’ are ultimately represented in AI-assisted texts (Vinall & Hellmich, 2024). Certain researchers advocate for the introduction of novel categories to delineate AI's functions such as "computational assistant" or "textual contributor"—and recommend that these roles be explicitly recognized in disclosures or authorship statements (Park et al., 2023). Others highlight the developing paradigm of "distributed cognition," in which knowledge creation is progressively disseminated among humans and intelligent systems. Within these frameworks, collaboration involves not attributing human characteristics to AI, but rather distinctly defining the human oversight required to maintain intellectual accountability, a theme widely discussed in scholarship on co-creation, authorship, and ethics in generative AI (Bozkurt, 2024). EJMEB (ISSN 3041-2102) | VOLUME 2 | NUMBER 6 | 2025 214 Educational institutions and publishers can facilitate this reframing by establishing organized platforms for discourse on responsible AI utilization, formulating accessible disclosure standards, and promoting transparency rather than obfuscation. Instead of regulating the distinction between human and non-human contributions, academic culture should focus on managing and ethically incorporating both inputs (Kotsis, 2025). Conclusions and Future Directions The incorporation of artificial intelligence into academic writing has presented significant challenges that impact the essence of scientific authorship, editorial accountability, and institutional credibility. As AI tools advance in sophistication, the parameters of human creativity, intellectual responsibility, and machine support become increasingly intricate and contentious. This changing environment requires a fundamental redefinition of authorship in scientific knowledge within the digital era. Scientific journals must transcend policy statements and establish actionable frameworks that tackle transparency, disclosure, and the ethical boundaries of AI utilization. These frameworks must promote coherence among disciplines and publishing ecosystems, circumventing the existing disarray of inconsistent standards (Stokel-Walker, 2023; Cotton et al., 2024). Institutions must adopt a proactive approach by educating researchers and students on ethical AI integration while maintaining academic integrity without hindering innovation (Floridi, 2023; Mhlanga, 2023). The ramifications for peer review and accountability are extensive. The dangers of AI-generated content, unrecognized plagiarism, and the decline of authorial accountability necessitate a comprehensive approach that includes reviewers, editors, and authors. In this context, the academic community must resist the inclination to regard AI as either a menace to be prohibited or a remedy to be uncritically embraced. A framework for responsible human–AI collaboration is essential, founded on transparency, critical engagement, and epistemic humility (Park et al., 2023; van Dis et al., 2023). Future initiatives should prioritize international cooperation to develop common standards for AI in academic writing, interdisciplinary investigation into its cognitive and epistemological effects, and the joint creation of ethical frameworks that reconcile innovation with accountability. As artificial intelligence transforms the framework of scientific communication, it is incumbent upon the academic community to ensure that its values develop with clarity, intent, and integrity. Conflict of Interests No conflict of interest. References Amirjalili, F., Neysani, M., & Nikbakht, A. (2024). Exploring the boundaries of authorship: A comparative analysis of AI-generated text and human academic writing in English literature. In Frontiers in Education (Vol. 9, p. 1347421). Frontiers Media SA. https://doi.org/10.3389/feduc.2024.1347421 Balalle, H., & Pannilage, S. (2025). Reassessing academic integrity in the age of AI: A systematic literature review on AI and academic integrity. 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