ARTIFICIAL INTELLIGENCE (AI) IN SCHOLARLY COMMUNICATION: TRANSFORMING RESEARCH DISSEMINATION AND KNOWLEDGE SHARING
ARTIFICIAL INTELLIGENCE (AI) IN SCHOLARLY COMMUNICATION: TRANSFORMING RESEARCH DISSEMINATION AND KNOWLEDGE SHARING
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Date
2026
Authors
NZE, Elizabeth D.
OTOLORIN, Victoria M.
Journal Title
Journal ISSN
Volume Title
Publisher
Library and Information Management Forum Vol. 28. No. 1 2026
Abstract
This 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.
Description
Journal of Library and Information Management Forum