The mediating role of Self-Regulated Learning in the relationship between performance expectancy, effort expectancy, and students’ behavioral intention to use ChatGPT for learning

Abstract

The rapid rise of generative artificial intelligence (AI) applications, such as ChatGPT, is transforming higher education. However, the key factors that influence students’ intention to use these applications remain unclear. This study investigates the impact of performance expectancy (PE) and effort expectancy (EE) on students’ behavioral intention (BI) to use ChatGPT for learning, with a focus on the mediating role of self-regulated learning (SRL). Using data from 287 university students, analyzed with structural equation modelling (SEM) in AMOS, we found that both PE and EE directly and significantly influence BI. Students who perceive ChatGPT as useful and easy to use are more likely to intend to adopt it. Notably, SRL mediates the link between EE and BI, but not between PE and BI, highlighting the importance of self-regulation when adoption is influenced by perceived ease of use. This study highlights the motivational and self-regulatory drivers behind AI adoption in education, offering practical guidance for policymakers and educators designing AI-supported learning.