AI-enhanced competency transfer hubs: a conceptual framework for university-industry engagement and knowledge sharing

The Journal of Technology Transfer. 2026. No. 51. P. 682–712.

Arya V., Saraf A., Chichkanov N., Papa A., Romano M.

This paper introduces a framework for AI-driven competency transfer hubs, designed to facilitate effective knowledge exchange and collaboration between universities and industries. These hubs leverage artificial intelligence technologies like machine learning and natural language processing to enhance the efficiency and effectiveness of information flows between academic institutions and industry partners, optimizing the whole knowledge-sharing process. Using the TCM-ADO framework the paper consolidates existing perspectives and offers practical suggestions on how to incorporate AI technologies into competency hubs. The discussion further delves into outlining key layers of such hubs including AI-powered knowledge extraction and enrichment, knowledge customization, adaptive project management as well as collaboration outcome enhancement and feedback optimization. A set of key elements for AI-enhanced competency transfer hubs was also developed and presented including the issues of technical alignment, advanced AI integration as well as value aspects. The study wraps up by exploring key areas of application in the establishment of AI-enhanced competency transfer hubs and their wider societal significance.

Keywords: взаимодействие науки и бизнеса, трансфер знаний и технологий, university-industry collaboration, AI-enhanced knowledge transfer, Machine learning in knowledge hubs, Natural language processing (NLP) for innovation, Competency transfer hubs, Long-term competitiveness in AI-driven hubs, хабы знаний

Publication based on the results of: National innovation system 2.0: involving key actors in transformation processes (2025)