Abstract:

Against the backdrop of digital and intelligent transformation reshaping educational paradigms, changes in higher education teaching models have posed new challenges to university students’ learning. How university students adapt to the iterative development of digital and intelligent technologies and dynamically regulate their learning model to improve learning outcomes has become an important issue in academic research. Currently, there remains a clear gap between Chinese university students’ learning initiative, interpersonal communication ability, learning persistence, and self-regulation ability and the developmental requirements of the digital and intelligent era. Taking adaptive control theory as the analytical framework, this study examines the underlying causes of these dilemmas from four dimensions: learning objectives, learning strategies, outcome evaluation, and process regulation. It further proposes four targeted implementation strategies: establishing outcome-oriented and hierarchical learning objectives, constructing a personalized learning model based on human-machine collaboration, improving an evaluation system involving multiple subjects, and establishing a real-time dynamic feedback and regulation mechanism. These strategies aim to promote university students’ adaptive control learning characterized by human-machine collaborative symbiosis in the digital and intelligent era.

Key words: digital and intelligent era, university students, adaptive control learning, learning model