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

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).