Portraits of Student Types Using GenAI-Assisted Learning: An Empirical Study Based on a Survey of Undergraduates from 20 Higher Education Institutions in China

Abstract

Based on a survey of 12,678 undergraduates across 20 higher education institutions (HEIs) in China, this study shows that most students exhibit a critical use tendency when engaging in higher-order thinking tasks, such as critical and creative tasks with generative artificial intelligence (GenAI), and believe that GenAI-assisted learning positively contributes to their problem-solving ability. Furthermore, a latent profile analysis and regression analysis reveal four distinct types of GenAI-assisted learning users, reflecting the interaction between students’ level of technology acceptance and their self-regulated learning abilities. Firstly, cautious experiencers (42%) exhibit low acceptance, high critical use, and low expectations for skill development. Secondly, labor substituters (24%) demonstrate low acceptance, low critical use, and moderately high expectations for skill development. Thirdly, balanced explorers (21.2%) show high acceptance, low critical use, and moderately high expectations for skill development. Fourthly, deep users (12.8%) exhibit high acceptance, high critical use, and high expectations for skill development. Based on these findings, this study underscores the necessity for HEIs to guide undergraduates in the appropriate use of GenAI tools and proposes effective strategies, such as targeted educational interventions and the cultivation of artificial intelligence literacy. These measures will advance students’ transition toward the ideal profile of high acceptance, high critical use, and high expectations for skill development.

Keywords

generative artificial intelligence (GenAI) / usage patterns / critical use / higher-order thinking skills / latent profile analysis (LPA)

RIGHTS & PERMISSIONS

Higher Education Press