Integrating learner characteristics and generative AI affordances to enhance self-regulated learning: a configurational analysis
Xiu-Yi Wu, Thomas K. F. Chiu
Abstract
Abstract This study investigates the configurational impact of generative artificial intelligence (GenAI) tools on self-regulated learning (SRL) across various educational levels using a 28-week fuzzy-set qualitative comparative analysis (fsQCA) approach. The research explores how factors such as technological proficiency, user engagement, research skills, and feedback quality interact with the functionalities of GenAI tools to enhance SRL capacities. Data were collected through semi-structured surveys and qualitative assessments from a diverse sample of undergraduate and postgraduate students. The findings reveal that the synergistic relationship between learner characteristics and GenAI tool affordances significantly boosts SRL skills. Key configurations identified include the critical role of high-quality feedback and tool functionalities, the importance of positive user attitudes and engagement, and the moderating effect of user interface experience. This study underscores the necessity of tailoring GenAI tools to meet individual learner needs and highlights the potential of these technologies to create adaptive, personalized learning environments. The results advocate for the strategic integration of GenAI tools in educational practices to support diverse learning pathways, contributing to the global discourse on digital pedagogy and the enhancement of self-regulated learning.