Exploring The Impact Of AI-Related Motivation on Self-Regulated Learning Among University Student in Samarinda

Abstract

The rapid advancement of artificial intelligence (AI) technology has transformed the landscape of higher education, prompting students to integrate AI into academic activities. This study examines the influence of AI use motivation on students’ self-regulated learning (SRL) among university students in Samarinda, Indonesia, a developing urban context that remains underrepresented in empirical research on AI-enhanced learning. Employing a quantitative approach and causal associative design, data were collected from 384 active university students who had prior experience using AI tools. The instruments used were the QAIUM scale and SRL scale, both of which were validated for reliability and construct accuracy. Results from simple linear regression analysis revealed a significant negative effect of AI use motivation on SRL, with a contribution of 10.8%. These findings indicate that higher motivation to use AI may be associated with reduced capacity for independent planning, monitoring, and evaluation of learning processes, suggesting a potential risk of digital dependency. From a practical perspective, the results highlight the need for educational practices and policies that promote balanced and reflective AI use, emphasizing the development of students’ self-regulatory skills alongside technological integration. This study contributes theoretically by integrating the Expectancy-Value framework with Zimmerman’s SRL model and provides context-specific empirical evidence to inform AI-related educational strategies in similar emerging higher education settings.