Sage Journals: Discover world-class research

Abstract

This study investigates the role of generative Artificial Intelligence (AI) in enhancing Apprenticeship Systems (ASs) by transforming Tacit Knowledge (TK) into Explicit Knowledge (EK), thereby improving Knowledge Transfer (KT) efficiency. A controlled experiment was conducted with 50 novice live-stream hosts, divided into the Experimental Group (EG) and the Control Group (CG). The EG used to train tools augmented with AI, while the CG used traditional methods. The experimental design included competency tests in seven areas, including on-camera presence, communication skills, and learning ability, and the use of statistical methods to compare the performance results of the two groups. The results established a significant improvement within the EG. The resultant indicators for expressiveness in shots (85 vs. 70), verbal expression (88 vs. 72), and learning capacity (86 vs. 71) exhibited statistically significant differences (p-values < 0.01). These outcomes suggest that the utilization of AI tools effectively enhances the development of various competencies, accelerates learning, enhances adaptability, and provides instant corrective feedback. The study's implication includes the utilization of AI in apprenticeship models, which have the potential for higher scalability, preservation of crucial TK, and workforce development, especially in industries that require Experiential Learning (EL).

Keywords

generative artificial intelligence tacit knowledge explicit knowledge knowledge transfer experiential learning

Get full access to this article

View all access options for this article.

References

Oranga

. Tacit knowledge transfer and sharing: characteristics and benefits of tacit & explicit knowledge. J Account Res, Utility Fin Dig Assets 2023; 2: 736–740.

Nupap

. Knowledge management system by applying knowledge creating company: transforming tacit to explicit knowledge. In: 2022 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON), IEEE, 2022, pp.439–444.

Terhorst

Krumpholz

. Role of tacit knowledge in innovation: a systematic literature review. IUP J Knowl Manage 2025; 23: 50–90.

Zhang

Yuan

, et al. Transformation paths and influencing factors of tacit knowledge into explicit knowledge in real estate companies: a qualitative study. Eng, Construct Archit Manage 2022; 29: 1319–1342.

Poitras

Crane

BGC

Dempsey

, et al. Cognitive apprenticeship and artificial intelligence coding assistants. In: Navigating computer science education in the 21st century. Hershey, PA, USA: IGI Global Scientific Publishing, 2024, pp.261–281.

Rott

Lao

Petridou

, et al. Needs and requirements for an additional AI qualification during dual vocational training: results from studies of apprentices and teachers. Comput Educ: Artif Intell2022; 3: 100102.

. Investigating approaches of transferring tacit knowledge to novices with A theoretical view. In: Ideas. Baku, Azerbaijan: Jomard Publishing, 2023, pp.5–20.

Owens

. Employing artificial intelligence to increase occupational tacit-knowledge through competency-based experiential learning. In AI-GEL@ AIED, CEUR Workshop Proceedings, RWTH Aachen University, Aachen, Germany, 2023, pp. 58–67.

Park

Ghauri

Lee

, et al. Unveiling the black box of IJV innovativeness: the role of explicit and tacit knowledge transfer. J Int Manage 2022; 28: 100956.

10.

Baláž

Williams

Moravčíková

, et al. What competences, which migrants? Tacit and explicit knowledge acquired via migration. J Ethn Migr Stud 2021; 47: 1758–1774.

11.

Silva

. Expanding the current tacit/explicit knowledge dichotomy encompassing situated and theoretical/normative knowledge: a phenomenological perspective. J Doc 2023; 79: 955–972.

12.

Duan

Yang

Huang

, et al. Unveiling the impacts of explicit vs. Tacit knowledge hiding on innovation quality: the moderating role of knowledge flow within a firm. J Bus Res 2022; 139: 1489–1500.

13.

Thomas

Gupta

. Tacit knowledge in organizations: bibliometrics and a framework-based systematic review of antecedents, outcomes, theories, methods and future directions. J Knowl Manag 2022; 26: 1014–1041.

14.

de Almeida Arruda

Rigo

de Souza

GFS

, et al. Transfer of tacit knowledge: review of the dimensions of analysis on the construct. Int J Sci Manage Tourism 2024; 10: e992–e992.

15.

Houlsby

, et al. Parameter-efficient transfer learning for NLP. In: International Conference On Machine Learning, PMLR, 2019, pp.2790–2799.

16.

Rainey

Holland

. Emergent Behavior in System of Systems Engineering: Real-World Applications. Boca Raton, Florida, United States: CRC Press, 2022.

17.

Wei

et al. Emergent abilities of large language models, arXiv preprint arXiv:2206.07682, 2022.

Making tacit knowledge explicit: Generative AI's role in enhancing apprenticeship systems

Abstract

Keywords

Get full access to this article

References