Artificial Intelligence Learning Companion and Self-Regulated Learning as Predictors of Student Academic Engagement in Fully Online Higher Education

Anak Agung Gede Pranata; I Wayan Adi Putra; I Komang Aditya Saputra

Authors

Anak Agung Gede Pranata Department of Educational Technology, Ganesha University of Education, Singaraja, Bali, Indonesia
I Wayan Adi Putra Department of Educational Technology, Ganesha University of Education, Singaraja, Bali, Indonesia
I Komang Aditya Saputra Department of Educational Technology, Ganesha University of Education, Singaraja, Bali, Indonesia

Keywords:

artificial intelligence learning companion, self-regulated learning, student academic engagement, fully online higher education, PLS-SEM

Abstract

The increasing adoption of Artificial Intelligence (AI) in fully online higher education has transformed the way students interact with digital learning environments and manage their learning processes. However, empirical evidence regarding the combined influence of Artificial Intelligence Learning Companion (AILC) and Self-Regulated Learning (SRL) on Student Academic Engagement (SAE) remains limited. This study aimed to examine the predictive effects of AILC and SRL on SAE among undergraduate students participating in fully online higher education. A quantitative explanatory research design with a cross-sectional survey was employed. Data were collected from 356 undergraduate students who had experience using AI learning companions for academic purposes. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings revealed that both AILC and SRL positively and significantly predicted Student Academic Engagement. Self-Regulated Learning demonstrated a stronger predictive effect than Artificial Intelligence Learning Companion, while the proposed structural model exhibited substantial explanatory and predictive power. The results indicate that students' engagement in fully online learning is influenced by the complementary roles of AI-supported learning and self-regulatory competencies. The study contributes to the emerging literature by providing an integrated predictive model that explains student engagement through both technological and learner-related factors. The findings also offer practical implications for higher education institutions seeking to design AI-enhanced online learning environments that promote active participation, autonomous learning, and sustainable academic engagement.

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