AI in Knowledge Management
A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "AI-Driven Innovations".
Deadline for manuscript submissions: 20 April 2027 | Viewed by 1536
Editor
Special Issue Information
Dear Colleagues,
This Special Issue will explore how artificial intelligence (AI) will impact knowledge management (KM). The rapid advancement of AI is transforming the landscape of KM, offering novel approaches to capturing, sharing, and leveraging organizational knowledge across various industries and disciplines. AI-powered technologies such as machine learning, natural language processing, and cognitive computing are enabling organizations to automate complex knowledge processes, derive actionable insights from vast datasets, and enhance real-time decision-making, while also improving human–AI interactions and collaboration.
However, the integration of KM within AI-powered enterprises introduces several challenges, such as concerns regarding trust, ethics, data privacy, and the evolving role of human expertise in AI-driven environments. The opaque, “black box” nature of AI raises critical questions about how knowledge is generated and transferred. Additionally, an over-reliance on AI-generated knowledge can lead to significant risks, including AI hallucinations, misinformation amplification, contextual misunderstandings, and biases. These challenges span across industries, underscoring the need for careful examination and research to mitigate potential risks. Furthermore, careful research is paramount for promoting human–AI coexistence in ways that benefit all stakeholders proportionately.
Addressing these challenges requires interdisciplinary research and purposeful design to ensure AI complements rather than compromises human knowledge systems. This minitrack will bring together scholars, practitioners, and technologists to explore the intersection of KM and AI-powered businesses. We welcome contributions that examine both the opportunities and complexities of AI-enabled knowledge management, including design science, theoretical explorations, empirical investigations, practical implementations, and case-based studies. Submissions focusing on frameworks, processes, systems, tools, and applications are especially encouraged, along with interdisciplinary perspectives that consider the ethical, managerial, and technical implications of AI-driven KM strategies.
Prof. Dr. Murray Jennex
Guest Editor
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computers is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- knowledge creation
- knowledge representation
- knowledge discovery
- artificial intelligence
- knowledge management
- knowledge retrieval
- knowledge search
- knowledge acquisition
- machine learning with knowledge
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