From surface to deep learning approaches with Generative AI in higher education: an analytical framework of student agency

Abstract

ABSTRACT Recent emergence of generative artificial intelligence (GenAI) technology has stimulated interests as well as concerns in their potential in teaching and learning. Situated in the new and transforming context, this study provides an avenue for students to introspectively explore their use of GenAI in a postgraduate course. Seventy-four students from three Chinese universities participated in this study. By analyzing student interviews conducted pre- and post-course, alongside their chat logs with GenAI and reflective journal entries detailing their learning approaches, the research uncovers a spectrum of student perspectives on GenAI’s impact, ranging from beneficial optimism, to cautious skepticism and adaptable pragmatism. Notably, student agency is identified as a crucial element in relation to these themes. This was articulated in four types of learning activities: receptive, resistive, resourceful, and reflective. The research underscores the importance of supporting and empowering student agency in the learning approaches aided by GenAI in education, highlighting its role in optimizing its use and enhancing autonomous, lifelong learning skills amidst the evolving technologically advanced learning landscape.