Pathways for Enhancing Faculty Members’ Practical Teaching Competence at Application-Oriented Universities through Artificial Intelligence

Abstract

Artificial intelligence (AI) is changing higher education at a remarkable pace, and it has created new conditions for strengthening the practical teaching competence of young faculty members. Taking Hankou University as the case and drawing on the literature as well as publicly available institutional information, this study examines why AI-enabled practical teaching is valuable for young teachers and where the main barriers lie. Four stand out: misconceptions about professional roles, insufficient digital competence, inadequate institutional incentives, and superficial industry–education collaboration. The study then builds a multidimensional framework for AI-enabled professional development in practical teaching, organized around three interconnected pathways—cultivating digital literacy, transforming teaching paradigms through the integration of virtual and physical environments, and redesigning evaluation systems with data intelligence. How these pathways reinforce the faculty capacity needed for the dynamic adjustment of academic programs at local application-oriented universities amid industrial upgrading is also discussed. The aim is to provide a theoretical reference and practical insights for the high-quality, substantively driven development of comparable application-oriented undergraduate institutions.