ARTIFICIAL INTELLIGENCE AND SELF-REGULATED LEARNING IN OUTCOME-BASED EDUCATION: UNDERGRADUATE STUDENTS' PERCEPTIONS

Abstract

Artificial intelligence (AI) has become an increasingly important tool for supporting undergraduate learning through concept explanation, feedback, assessment, and self-regulated learning (SRL). However, limited empirical research has examined students' perceptions of AI as a support mechanism for SRL within outcome-based education (OBE), particularly across demographic and academic groups. Guided by self-regulated learning theory, the Technology Acceptance Model, and constructive alignment, this study investigated the relationships among perceived usefulness, perceived ease of use, trust in AI feedback, ethical concerns, perceived OBE alignment, AI-supported SRL, learning engagement, and behavioral intention to use AI. A quantitative cross-sectional survey was conducted with 320 undergraduate students. Data were analyzed using descriptive statistics, reliability analysis, Pearson correlation, multiple regression, independent-samples t tests, one-way analysis of variance (ANOVA), and mediation analysis. The findings indicated that students generally held positive perceptions of AI and reported moderate to high levels of AI-supported SRL, learning engagement, and intention to use AI. Perceived usefulness, ease of use, trust in AI, perceived OBE alignment, AI use frequency, and OBE awareness significantly predicted AI-supported SRL. Significant differences in AI-supported SRL were observed across AI use frequency, previous AI training, OBE awareness, and year of study. Furthermore, positive perceptions of AI significantly mediated the relationship between AI-supported SRL and learning engagement. The findings suggest that AI can effectively enhance undergraduate students' self-regulated learning within an OBE framework when implemented with appropriate ethical safeguards, AI literacy, and transparent assessment practices.