Analyzing The Digital Learning Motivation Ecosystem: Interconnecting Collaborative Learning, Artificial Intelligence Integration, And Student Self-Efficacy

Antonius Lase; Wiputra Cendana

doi:10.63922/ijevss.v5i03.5221

Antonius Lase Universitas Pelita Harapan, Indonesia
Wiputra Cendana Universitas Pelita Harapan, Indonesia

DOI: https://doi.org/10.63922/ijevss.v5i03.5221

Keywords: Artificial Intelligence Integration, Collaborative Learning, Digital Pedagogy, Secondary Education, Student Learning Motivation, Self-Efficacy.

Abstract

This study investigates the socio-technical and psychological dynamics driving student learning motivation within a digitally accelerated educational landscape. Structured around a quantitative causal-associative survey design, this empirical inquiry models the partial and simultaneous predictive boundaries of collaborative learning, artificial intelligence integration, and self-efficacy on student learning motivation among secondary school students. Primary data gathered via structured questionnaires were evaluated using multiple linear regression software following strict verification of classic econometric diagnostic assumptions. The inferential outputs demonstrate that all proposed hypotheses are statistically accepted. Collaborative learning acts as an essential social catalyst for peer engagement, while artificial intelligence integration provides an adaptive yet conservative technical scaffolding for cognitive load management. Crucially, internal psychological conviction operationalized through self-efficacy emerges as the ultimate, overriding determinant of academic drive. Simultaneously, the three predictors form a highly cohesive and robust socio-technical ecosystem that significantly accelerates adolescent learning passion. These findings suggest that sophisticated technological infrastructure cannot compensate for deficits in internal psychological confidence. Pedagogically, institutions must shift from isolated digitalization toward human-centered frameworks that systematically align social peer dynamics and algorithmic support with deliberate self-efficacy reinforcement.

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