About the Journal

The Journal of Knowledge Learning and Science Technology (JKLST) is a reputable, peer-reviewed academic journal that operates on an Open Access model. The journal aims to provide a comprehensive and reliable source of information on groundbreaking discoveries and the latest advancements in various areas of the field. It welcomes original research papers, review papers, case reports, short communications, and other relevant contributions.

The review process of JKLST is rigorous and ensures the highest quality of published content. Manuscripts are reviewed by the journal's editorial board members or external experts who are knowledgeable in the respective fields. The review process involves obtaining approval from at least three independent reviewers, followed by final approval from the editor. This meticulous evaluation guarantees the validity, significance, and credibility of the published work.

The editorial team of JKLST is committed to managing the entire submission, review, revision, and publication process efficiently. They work closely with authors to ensure a smooth and timely publication experience. The journal's online platform provides unrestricted access to its content, allowing researchers and scholars from around the world to benefit from valuable insights and findings without any barriers or subscription requirements.

JKLST strives to maintain its reputation as a reliable and influential platform for disseminating knowledge and promoting scholarly discussions in the fields of knowledge acquisition, learning methodologies, and science and technology. By fostering collaboration and facilitating the exchange of ideas, the journal contributes to the advancement of the scientific community and the development of innovative solutions in these domains.

Current Issue

Vol. 5 No. 2 (2026): Human-Centered Generative AI for Learning, Work, and Knowledge Creation
					View Vol. 5 No. 2 (2026): Human-Centered Generative AI for Learning, Work, and Knowledge Creation

This issue examines how generative AI can augment learning, professional practice, and knowledge creation while preserving human agency, inclusion, and accountability. It welcomes empirical studies, design research, evaluations, and policy analyses across education, organizations, and public services.

Published: 25-06-2026

Articles

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