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Redefining Knowledge Management Systems: The Role of Generative AI in...
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Redefining Knowledge Management Systems: The Role of Generative AI in Innovation, Learning, and Knowledge Processes

A Special Issue of Systems (ISSN 2079-8954).

Deadline for manuscript submissions: 31 October 2026 | Viewed by 5206

Editors

Prof. Dr. Eric Tsui

E-Mail Website
Guest Editor
Educational Research Centre, The Hong Kong Polytechnic University, Hong Kong, China
Interests: knowledge technologies including search engines, portals, personal knowledge management, personal learning environments, and business process management
Special Issues, Collections and Topics in MDPI journals
Dr. Gang Liu

E-Mail Website
Guest Editor
Business School, Shenzhen Technology University, Shenzhen 518118, China
Interests: knowledge management; innovation; GenAI and KM; cross-cultural management
Dr. Muhammad Saleem Sumbal

E-Mail Website
Guest Editor
Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Interests: knowledge management; HRM; innovation; GenAI and KM

Special Issue Information

Dear Colleagues,

In the era of digital transformation, Knowledge Management Systems (KMSs) are undergoing a profound evolution driven by the rise in generative artificial intelligence (GenAI) and other advanced technologies. This Special Issue invites contributions that explore how GenAI, large language models (LLMs), and related innovations are reshaping core knowledge processes—creation, retrieval, sharing, and application—within organizations and learning environments.

We welcome interdisciplinary research, conceptual frameworks, empirical studies, and case analyses that examine the integration of GenAI into KMSs to enhance innovation, accelerate decision-making, and support personalized learning. Topics of interest include AI-enabled knowledge discovery, conversational interfaces for knowledge access, GenAI-driven content generation, expanded Knowledge Repositories that incorporate internal and external knowledge, and the ethical and governance challenges of AI-enhanced KMSs.

This Special Issue aims to bridge the research–practice gap by highlighting real-world applications and theoretical advancements that demonstrate how GenAI can transform fragmented knowledge silos into dynamic, intelligent ecosystems. Contributions should also consider the implications for organizational culture, human–AI collaboration, and the balance between tacit and explicit knowledge.

By fostering dialogue among academics, technologists, and practitioners, this Special Issue seeks to chart the future of KMSs in a world increasingly shaped by intelligent systems, offering insights into sustainable innovation and lifelong learning.

Prof. Dr. Eric Tsui
Dr. Gang Liu
Dr. Muhammad Saleem Sumbal
Guest Editors

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Systems 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 2400 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 management systems
  • generative AI
  • knowledge processes
  • innovation
  • learning

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Published Papers (3 papers)

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Research

24 pages, 11472 KB  
Article
TriageRAG: Confidence-Based Triage for Idea-Stage §103-Propensity Screening
by Kyung-Yul Lee and Juho Bai
Systems 2026, 14(9), 1160; https://doi.org/10.3390/systems14091160 - 16 Sep 2026
Viewed by 70
Abstract
Screening for §103 propensity at the idea stage is inherently difficult: obviousness is a context-sensitive legal determination over prior art combinations that surface-level text cannot fully capture. Yet because §103 concerns the inventive step of the underlying idea rather than only the wording [...] Read more.
Screening for §103 propensity at the idea stage is inherently difficult: obviousness is a context-sensitive legal determination over prior art combinations that surface-level text cannot fully capture. Yet because §103 concerns the inventive step of the underlying idea rather than only the wording of the claims, an application as filed may already carry a weak signal of §103 propensity—motivating a screening tool at the idea stage. We present TriageRAG (T-RAG), a confidence-based decision-support framework. A fine-tuned ModernBERT-large classifier produces a prediction together with a confidence score; high-confidence cases are delivered directly, while only low-confidence cases are escalated to a large language model (LLM), which is supplied with the classifier’s own prediction as the primary signal together with retrieved similar prior applications, and is instructed to verify the classifier rather than replace it. We evaluate under deliberately leakage-free conditions—a same-era corpus, a temporal hold-out, and a contamination-free label set in which the §103 label follows the USPTO Office Action Research Dataset and the two classes are disjoint by construction. Under these strict conditions the system remains useful: the classifier confidence rank-orders correctness well enough to support high-precision automatic decisions at low coverage, and classifier-primary escalation improves accuracy precisely on the uncertain cases where the classifier is weakest, without degrading it overall. We position the contribution as a triage architecture—turning a deliberately commodity classifier into an auditable decision-support tool—rather than as a new classifier. Ablation studies isolate the roles of confidence routing, retrieval design, and the escalation prompt, and characterize the accuracy–cost trade-off across the escalation threshold. Full article
(This article belongs to the Special Issue Redefining Knowledge Management Systems: The Role of Generative AI in Innovation, Learning, and Knowledge Processes)
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27 pages, 970 KB  
Article
An Evidence-Centric Knowledge Management System for Humanities Research Powered by Multimodal Large Language Models
by Bo An
Systems 2026, 14(5), 552; https://doi.org/10.3390/systems14050552 - 13 May 2026
Viewed by 532
Abstract
Humanities research depends on linking claims to precise evidence, such as juan, pages, passages, or time-stamped media segments. While large language models can assist with extraction and synthesis, their outputs remain difficult to use in scholarly work unless provenance, citation, and verification [...] Read more.
Humanities research depends on linking claims to precise evidence, such as juan, pages, passages, or time-stamped media segments. While large language models can assist with extraction and synthesis, their outputs remain difficult to use in scholarly work unless provenance, citation, and verification are explicitly controlled. This paper proposes an evidence-centric knowledge management system for humanities research. The system models sources as stable EvidenceUnits, extracts entities, relations, and events under schema constraints, and admits generated knowledge only after structural validation, evidence-pointer checking, and claim-level verification. Ambiguous or conflicting cases are routed to human review and retained in an audit trail. The main evaluation is a controlled document-modality study on an annotated subset of the Shiji. The system achieves micro-F1 scores of 0.84 for named entity recognition, 0.78 for relation extraction, and 0.82 for event trigger detection. Governance-layer analysis shows that evidence-pointer resolvability rises from 42.3% to 94.2%, while mis-citation and overreach fall to 4.1% and 5.3%. A minimal oral history audio smoke test further demonstrates that timestamped Audio EvidenceUnits can pass through the same governance workflow; though, it is not a full multimodal benchmark. Full article
(This article belongs to the Special Issue Redefining Knowledge Management Systems: The Role of Generative AI in Innovation, Learning, and Knowledge Processes)
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26 pages, 3579 KB  
Article
Game Knowledge Management System: Schema-Governed LLM Pipeline for Executable Narrative Generation in RPGs
by Aynigar Rahman, Aihe Yu and Kyungeun Cho
Systems 2026, 14(2), 175; https://doi.org/10.3390/systems14020175 - 5 Feb 2026
Cited by 1 | Viewed by 3798
Abstract
Procedural approaches have long been used in game development to reduce authoring costs and increase content diversity; however, traditional rule-based systems struggle to scale narrative complexity, whereas recent large language model (LLM)-based methods often produce outputs that are structurally invalid or incompatible with [...] Read more.
Procedural approaches have long been used in game development to reduce authoring costs and increase content diversity; however, traditional rule-based systems struggle to scale narrative complexity, whereas recent large language model (LLM)-based methods often produce outputs that are structurally invalid or incompatible with real-time game engines. This gap reflects a fundamental limitation in current practice: generative models lack systematic mechanisms for managing executable game knowledge rather than merely producing free-form narrative texts. To address this issue, we propose a Game Knowledge Management System (G-KMS) that reformulates LLM-based narrative generation as a structured knowledge management process. The proposed framework integrates knowledge grounding, schema-governed generation, normalization-based repair, engine-aligned knowledge admission, and application within a unified pipeline. The system was evaluated on a compact 2D Unity-based RPG benchmark using automated structural and semantic analyses, engine-level playability probes, and a controlled human player study. The experimental results demonstrated high reliability in knowledge admission, stable procedural structures, controlled expressive diversity, and a strong alignment between system-level metrics and player-perceived narrative quality, indicating that LLMs can function as dependable knowledge-construction components when embedded within a governed management pipeline. Beyond the evaluated RPG setting, this study suggests a practical and reproducible approach that may be extended to other executable systems, such as interactive simulations and training environments. Full article
(This article belongs to the Special Issue Redefining Knowledge Management Systems: The Role of Generative AI in Innovation, Learning, and Knowledge Processes)
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