A Generative AI-Driven Approach to Integrating the SECI Model for Knowledge-Based Systems
DOI:
https://doi.org/10.30865/ijics.v10i2.9717Keywords:
Generative Artificial Intelligence, Knowledge Management, SECI Model, Knowledge Conversion, Systematic Literature ReviewAbstract
This study examines the potential role of Generative Artificial Intelligence (Gen-AI) in extending the Socialization, Externalization, Combination, and Internalization (SECI) model within knowledge management. Using a Systematic Literature Review (SLR) of studies published between 2024 and 2026, it maps the functions of technologies such as Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Logic Augmented Generation (LAG) into Nonaka and Takeuchi’s knowledge spiral. The findings indicate that Gen-AI can support knowledge processes such as retrieval, interpretation, structuring, and integration, which may contribute to more continuous and digitally mediated knowledge flows. Some reviewed studies report improvements in handling tacit and explicit knowledge; for example, a simulation-based study reports a tacit knowledge recall rate of up to 94.9%, although this result is derived from a specific experimental context and should not be generalized. The review also identifies challenges, including AI-generated inaccuracies, overreliance on automated systems, and data security concerns, highlighting the continued importance of human oversight. This study contributes a conceptual mapping of a Generative AI-based Knowledge Framework (GRAI) as an extension of the SECI model; however, this contribution remains theoretical and requires further empirical validation across organizational contexts.
References
Management Paradigm," Human Technology, vol. 20(1), pp. 131-154, 2024. https://doi.org/10.14254/1795-6889.2024.20-1.7
[2] H. Lee, "Why Managing Knowledge Dynamics in Open Innovation Matters in the Digital Transformation Era: A Case Study of Distributed Manufacturing in Fine Ceramics," Journal of Open Innovation: Technology, Market, and Complexity, vol. 11, no. 4,
pp. 1-11, 2025. https://doi.org/10.1016/j.joitmc.2025.100643
[3] Grisna. A. Debrian Ruhut Saragih, "From Walking Libraries to Answer Engines: Optimizing Knowledge Internalization in Public Financing with RAG-Based AI Chatbots," Jurnal Manajemen Bisnis, vol. 13, no. 1, pp. 189-207, 2026. https://doi.org/10.33096/jmb.v13i1.1366
[4] Aldo Gangemi. &. A. Giovanni Nuzzolese, "Logic Augmented Generation: Integrating Semantic Knowledge Graphs with Reactive Continuous Knowledge Graphs," ScienceDirect, vol. 85, pp. 1-6, 2025. https://doi.org/10.1016/j.websem.2024.100859
[5] Liccardo. &. Roberto Cerchione, "GenAI-Assisted Knowledge Generation: A Case Study on Human–Machine Collaboration Through the SECI Model," The Proceedings of the 26th European Conference on Knowledge Management, pp. 1217-1225, 2024. https://doi.org/10.34190/eckm.26.2.3774
[6] Erick-Nicolae. Furduescu, "Strategic Management of LLM-Based Chatbots: Transforming Internal Collaboration and Decision-Making in Organizations," Business Excellence and Management, vol. 15, no. 5, pp. 81-91, 2025. https://doi.org/10.24818/beman/2025.s.i.5-08
[7] Urpi Barreto. &. Yasser Abarca, "Integration of the SECI model and ChatGPT in higher education," Heliyon A Cell Press journal, vol. 11, no. 4, pp. 1-13, 2025. https://doi.org/10.1016/j.heliyon.2025.e42814
[8] Karsten. Bohm. &. Susanne. Durst, "Knowledge Management in the Age of Generative Artificial Intelligence – From SECI to GRAI," VINE Journal of Information and Knowledge Management Systems, vol. 56, no. 1, pp. 106-121, 2025. https://doi.org/10.1108/VJIKMS-10-2024-0357
[9] Ettore. B. Tomas. Cherkos. K. Enrico. S. Nima. T. Kathrin Kirchner, "Generative AI Meets Knowledge Management: InsightsFrom Software Development Practices," Knowledge and Process Management, vol. 32, no. 4, pp. 223-235, 2025. https://doi.org/10.1002/kpm.70004
[10] Feyza. B. Arif. S. Berna Gurler, "Integrating Knowledge Management Practices in Higher Education with Sustainable Smart Hydrological Ecosystems: An AI-Driven Approach to Green Innovation," Hydrology Research, vol. 56, no. 9, pp. 817-836, 2025. https://doi.org/10.2166/nh.2025.147
[11] M. Manate. Hashim, "Generative Artificial Intelligence in Contemporary Project Management," Academia Open, vol. 11, no. 1,
pp. 1-7, 2026. http://doi.org/10.21070/acopen.11.2026.13541
[12] Zhaopeng. J. Yang. B. Zhou. F. Zhongde Huang, "Does Public Environmental Education and Advocacy Reinforce Conservation Behavior Value in Rural Southwest China?," mdpi, pp. 1-17, 2022. https://doi.org/10.3390/su14095505
[13] H. Taherdoost. &. M. Madanchian, "Artificial Intelligence and Knowledge Management: Impacts, Benefits, and Implementation," mdpi, vol. 12, no. 4, pp. 1-18, 2023. https://doi.org/10.3390/computers12040072
[14] M. Lazzar. a. M. Al. Aachhab. Ouissale Zaoui Seghroucheni, "Using AI and NLP for Tacit Knowledge Conversion in Knowledge Management Systems: A Comparative Analysis," MDPI, pp. 1-17, 2025.
https://doi.org/10.3390/technologies13020087
[15] M. Nakash. a. E. Bolisani, "Knowledge Management Meets Artificial Intelligence: A Systematic Review and Future Research Agenda," pp. 544-552, 2024.
[16] L. &. Chen, "Artificial Intelligence Capability and Knowledge Management: Evidence from Knowledge-Intensive Firms," 2024.
[17] Khalid. H. Alshammari. a. Abdulhamid. F. Alshammari, "From Digitalization to Knowledge Innovation: Integrated Model of AI Knowledge Agility and Organizational Learning Culture," MDPI, pp. 1-22, 2026. https://doi.org/10.3390/systems14010067
[18] P. Gebhard. C. Keller. C. Benjamin. Nakhosteen. S. Schaffer. T. Scheeberger. Martin Benderoth, "Socially Interactive Agents for Preserving and Transferring Tacit Knowledge in Organizations," pp. 1-4, 2025. https://doi.org/10.48550/arXiv.2508.19942
[19] S. Mastelini. T. ́ Loures. A. Veloso. Gianluca Zuin, "Leveraging Large Language Models for Tacit Knowledge Discovery in Organizational Contexts," pp. 1-8, 2025. https://doi.org/10.48550/arXiv.2507.03811
[20] M. Rezaei, "Artificial intelligence in knowledge management: Identifying and addressing the key implementation challenges,"
ScienceDirect, vol. 217, pp. 1-18, 2025. https://doi.org/10.1016/j.techfore.2025.124183
[21] Haddaway, N. R., Page, M. J., Pritchard, C. C., & McGuinness, L. A. (2022). PRISMA2020: An R package and Shiny app for producing PRISMA 2020-compliant flow diagrams, with interactivity for optimised digital transparency and Open Synthesis Campbell Systematic Reviews, 18, e1230. https://doi.org/10.1002/cl2.1230
