The influencing factors, diversification trends, and motivation-enhancement strategies of learning motivation among undergraduate students in Chinese application-oriented universities in the digital age: a grounded theory analysis based on Y University

Vollständiger Abstract

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Artificial intelligence technology is accelerating the transformation of the teaching ecosystem in universities, and as a result, the learning motivation of college students is profoundly affected. To reveal the core influencing factors and mechanism of learning motivation for Chinese application-oriented university students in the digital era, using grounded theory, 30 undergraduate students from Y University were selected as the research subjects. Through semi-structured interviews, original data were collected. Through open coding, axial coding, and selective coding, concepts and categories were refined, and a theoretical saturation test was conducted. The study found that the learning motivation of undergraduate students in application-oriented universities in the digital era is driven by four main categories: the core driving layer (AI-specific dimension, professional cognition and expectations, self-awareness, self-achievement, internal drive), the basic guarantee layer (school resource conditions, course evaluation methods), the relationship influence layer (teacher and peer influence, family influence), and the external constraint layer (student behavior constraints, autonomous time allocation). It presents a diversified trend and ultimately forms a “cognition-context” dual-drive influence mechanism model. Based on this, from the aspects of internal cognitive stimulation, external condition improvement, interpersonal relationship drive, and learning situation creation, practical paths are provided for students in application-oriented universities to adapt to the development trend of the digital era and stimulate their learning motivation.