Breaking the chain of knowledge transfer: AI shadows implicit, explicit, and tacit exchange

Abstract

This study is a meta-analysis review investigating the notion of adopting applied Artificial Intelligence (AI) systems into” knowledged” roles and practices, where the traditional Knowledge Transfer (KT) from the expert human to successor human is disrupted, thereby the transference of knowledge ceases. Through the transformation of human practice to machine function, the notion of knowledge transference between the layers of KT, exposes the tacit, implicit and explicit exchange with segmentation of a blurred line between the human-machine and machine-human, where this shadows discernment of human knowledge compatibility with generative AI advancements. The belief of advanced generative AI can create a knowledge-gap between human and machine where the impact of human understanding and expertise diminishes and the human capacity of understanding the knowledge-object will be un-transferable to future human successors in their domain. From this perspective it becomes plausible that this can impede advancement of specialized knowledge transfer and method of sole human innovative development in that field. In conclusion, this research hinges on the perspective that specific human knowledge-objects need preservation and capability of direct KT propagation to human successors. This necessitates complete transferable knowledge which carries the seed of human based future innovations. .