Online Collaborative Learning With Deep Learning In Fostering Critical Thinking And Motivation: Meta Synthetic Analysis - Novarita Novarita; Abdurrahman Abdurrahman; Cucu Sutarsyah; Budi Kadaryanto; Bambang Setiyadi (Yayasan Darussalam Bengkulu, 2026)
Online learning has expanded rapidly, yet many digital courses still struggle to cultivate sustained motivation and higher-order thinking because interaction is often limited to content delivery, fragmented discussion, or individual task completion. This article develops an integrative meta-synthesis on how Online Collaborative Learning (OCL), when supported by deep-learning-enabled artificial intelligence (AI), can strengthen critical thinking and learner motivation in higher education. The review synthesizes theoretical and empirical literature on OCL, computer-supported collaborative learning, learning analytics, adaptive feedback, and self-determination theory. Rather than treating AI as a replacement for pedagogy, this article conceptualizes deep learning as an adaptive scaffolding layer that can support peer dialogue, feedback, grouping, early-warning signals, and personalized learning paths. The synthesis identifies four mechanisms through which OCL-AI integration may enhance learning: argumentative knowledge construction, socially shared regulation, adaptive formative feedback, and motivational need support. The article also argues that these benefits are conditional on ethical data governance, transparent algorithms, teacher facilitation, and equitable access to digital infrastructure. The proposed framework contributes to online pedagogy by clarifying the relationship between social collaboration and machine-supported personalization. It offers practical design principles for lecturers, instructional designers, and higher education institutions seeking to develop online learning environments that are cognitively challenging, motivationally supportive, and ethically responsible.