THE EFFECTIVENESS OF INTEGRATING ARTIFICIAL INTELLIGENCE (AI) TECHNOLOGIES IN HIGHER EDUCATION TO DEVELOP STUDENTS' INDEPENDENT LEARNING: A SYSTEMATIC LITERATURE REVIEW AND THEORETICAL FRAMEWORK
Ключевые слова:
Artificial Intelligence, Systematic Review, Comparative Pedagogy, Independent Learning, Cognitive Offloading, Conceptual Framework, Credit-Module SystemАннотация
This study provides a systematic and theoretical analysis of the integration of Artificial Intelligence (AI) technologies into higher education to enhance students’ independent learning frameworks. Distinct from empirical interventions, this research employs a non-empirical, systematic literature review (SLR) methodology, synthesizing critical data from over 20 seminal peer-reviewed studies published between 2020 and 2026 indexed in Scopus, Web of Science, and eLIBRARY.RU. The primary objective is to resolve the pedagogical tension between AI-assisted cognitive offloading and the development of high-order self-regulated learning (SRL) competencies. The theoretical analysis reveals that unmonitored AI utilization leads to epistemological passivity, whereas structured pedagogical integration fosters meta-cognitive tracking and analytical synthesis. By establishing a comparative matrix of global educational systems and the maturing ECTS-based credit-module framework of Uzbekistan, this paper proposes a novel, scaffolded conceptual model for asynchronous independent study. The discussion addresses systemic risks, including generative plagiarism and the digital divide. The paper concludes with actionable structural recommendations for shifting institutional assessment metrics from static text preservation to dynamic process evaluation
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