ARTIFICIAL INTELLIGENCE (AI) IN SCHOLARLY COMMUNICATION: TRANSFORMING RESEARCH DISSEMINATION AND KNOWLEDGE SHARING

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dc.contributor.authorNZE, Elizabeth D.
dc.contributor.authorOTOLORIN, Victoria M.
dc.date.accessioned2026-07-29T06:07:22Z
dc.date.available2026-07-29T06:07:22Z
dc.date.issued2026
dc.descriptionJournal of Library and Information Management Forum
dc.description.abstractThis study investigated the role of Artificial Intelligence (AI) within the realm of scholarly communication, specifically in transforming the dissemination of research and knowledge sharing of knowledge. AI is the ability of a digital computer to perform a task that is commonly associated with human. The increasing reliance on AI has prompted academic institutions to re-evaluate approaches to academic works and scholarly communication. Literature reveals that AI has great possibilities for scholarly communications through ethical dissemination of research and knowledge sharing. New possibilities redefined includes formulation of research inquiries, timely completion of academic work, dissemination of scholarly works, as well as aiding the performance of oversight in peer review and research assessment practices. Even though Artificial Intelligence (AI) has great potential to improve the efficiency and quality of scholarly communications, its deployment must conform to principles of transparency, ethical responsibility, and individual privacy. By embracing these rules, the field of scholarly communications can harness the benefits of AI while maintaining trust, accountability, and high academic standards. The study concluded that while AI holds substantial promise for improving the efficiency and effectiveness of scholarly communications, its implementation must center on transparency, ethical responsibility, and ensuring data quality. It was suggested that academic institutions and research organisations should design and apply comprehensive ethical frameworks to guide the use of artificial intelligence in research writing, peer review, and publication processes. Such frameworks need to emphasise transparency, accountability, appropriate citation of AI-derived content, and the maintenance of academic integrity. These are achieved by verifying the human-in-the-loop, prevention of a diffusion of responsibility in the case of biases, clearly defining attribution boundaries, and guiding scholars through structured trust calibration steps.
dc.identifier.urihttps://kubanni.abu.edu.ng/123456789/13270
dc.language.isoen
dc.publisherLibrary and Information Management Forum Vol. 28. No. 1 2026
dc.titleARTIFICIAL INTELLIGENCE (AI) IN SCHOLARLY COMMUNICATION: TRANSFORMING RESEARCH DISSEMINATION AND KNOWLEDGE SHARING
dc.typeArticle
dspace.entity.type
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