Paradigm of artificial intelligence based on conversion of tacit to explicit knowledge

Abstract

Aim: This paper proposes a novel paradigm of Artificial Intelligence (AI) grounded in the epistemological process of converting tacit knowledge into explicit knowledge. Drawing on the foundational philosophies of science, particularly the works of Popper, Kuhn, Lakatos, and Gospodarek, the study conceptualizes AI not merely as a computational tool but as a systemic method for epistemic transformation. The paradigm is structured as a Lakatosian Research Programme, with a clearly defined hard core asserting that AI enables the symbolic representation of internalized, experiential knowledge. Surrounding this core is a protective belt of auxiliary hypotheses derived from general systems theory, cybernetics, machine learning, and symbolic processing. The programme's heuristics guide theoretical and technological advancements while preserving its epistemological foundation. By formalizing the tacit-to-explicit knowledge conversion, this paradigm repositions AI as a critical instrument for knowledge creation, management, and application in digital and socio-technical systems. This allows one to build measures and values of generative and language models, which is important from an economic point of view. This research tries to clarify the framework of use AI models for converting tacit knowledge inside a learning data of neural network systems to explicit information requested by the asking. It is important for economic evaluation of AI systems where accuracy considered utility as a criterion. Design / Research methods: Research programme in Lakatos' sense and multidisciplinary heuristic related to the theory of systems. Conclusions / findings: Artificial Intelligence should be understood not only as a technological artefact but as a systemic method for transforming tacit knowledge into explicit knowledge. The proposed AI paradigm adheres to the structure of a Kuhnian paradigm and a Lakatosian research programme. Its hard core is defined by the thesis that AI operationalizes the conversion of experiential, intuitive, or unconscious knowledge into symbolic, formalized, and actionable representations. Lakatosian protective belt as a dynamic epistemic layer. This AI paradigm offers a progressive problem-shift capacity by enabling novel ways of organizing, analyzing, and applying knowledge in digital and socio-technical environments. It also provides a coherent framework for developing AI systems that are more aligned with human cognitive and organizational processes. Originality / value of the article: This paper introduces: new concepts of usefulness of AI systems, new definition of AI systems based on conversion of the knowledge, original conversion paradigm and research program in Lakatos sense. It is original conceptional heuristic based on philosophy of science in relation to economic usefulness of view AI systems.