Evaluating the Influence of Cognitive Engagement, Academic Performance, and Self-Regulated Learning on the Adoption of Artificial Intelligence–Driven Adaptive Learning Systems among University Students in Sri Lanka

Abstract

Adaptive learning systems (with the role of artificial intelligence, AI) are increasingly becoming a part of higher education to provide personalization of the content, dynamic learning pathways, and timely feedback on student progress. This paper investigates the effect of cognitive engagement, academic performance and self-regulated learning in adopting AI-based adaptive learning systems among students in a Sri Lankan university. The analysis relies on the theory of educational technology adoption and learner-centered theory, the study examines the possibility that the learning behaviors and learning attributes of students influence their adoption and use of AI-enhanced learning conditions. The survey data involving 100 students at a university was used in a quantitative cross-sectional study that was conducted to collect information on the topics of AI-based learning tools exposure and its effect on the students. The analysis of the data was carried out through descriptive statistics, reliability analysis, Pearson correlation, and multiple regression of SPSS. The results indicated that AI adoption had a significant and positive effect on self-regulated learning and academic performance as opposed to cognitive engagement which was found to have a less significant effect on AI adoption in the regression model. The model explains 48.1% of AI adoption, showing that self-directed learning ability and learning orientation influence AI use more than engagement in higher education.