How generative artificial intelligence empowers Generation Z college students' learning

Abstract

To investigate the mechanisms through which Generative Artificial Intelligence (GenAI) empowers Generation Z college students' learning through human-AI collaboration, this study surveyed undergraduate and postgraduate students born between 1995 and 2009. A questionnaire was administered across four dimensions, including learning behaviors, awareness of the recommendation-based postgraduate admission policy, preferences for prospective postgraduate institutions, and the use of AI tools, yielding 300 valid responses. An empirical analysis was conducted using a combination of reliability and validity tests, correlation analysis, two-way analysis of variance, and multiple linear regression. The results show that the measurement scale demonstrates good reliability and validity. Students' use of GenAI remains largely at a superficial level, such as assisting with assignments, while the pursuit of recommendation-based postgraduate admission constitutes a core driver of digital learning. The four dimensions are significantly and positively correlated, and the sample exhibits pronounced group stratification. Academic ranking has a stable positive effect across different year levels, whereas year level itself has no independent effect. Awareness of the recommendation-based postgraduate admission policy, AI use, and postgraduate study planning positively predict learning behaviors, with policy awareness exerting the strongest predictive effect. Simply providing open access to AI tools may exacerbate academic stratification, while digital literacy does not develop spontaneously with students' progression through different year levels. Accordingly, this study proposes several reform pathways, including targeted and stratified education, deeper integration of AI driven by students' postgraduate admission needs, staged cultivation of digital literacy, and coordinated and integrated education involving multiple departments.