Akademik Veri Yönetim Sistemi
DEGRES, cilt.10, sa.6, ss.193-215, 2025 (AHCI)
Abstract
The integration of artificial intelligence (AI) into English language learning has revolutionized pedagogy, fostering adaptive, multimodal, and personalized experiences. Yet, concerns regarding cognitive overload and neural fatigue remain underexplored. This study synthesizes empirical findings and theoretical discourses to assess whether artificial intelligence-enhanced learning environments optimize cognitive load or exacerbate cognitive saturation, thereby influencing learner motivation and retention. Employing a systematic literature review (SLR) methodology, this research study follows Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, critically evaluating peer-reviewed articles, meta-analyses, and experimental studies from the past two decades. Data were sourced from Scopus, Web of Science, ERIC, and Google Scholar using predefined search parameters such as “Artificial intelligence in language learning,” “cognitive overload in digital education,” and “adaptive learning and cognitive strain.” Inclusion criteria encompassed empirical studies on artificial intelligence -mediated cognitive load, adaptive scaffolding, and cognitive fatigue in digital learning. Findings reveal a paradox: Artificial intelligence -driven personalization and real-time feedback alleviate extraneous cognitive load yet induce cognitive saturation through excessive multimodal stimulation, fragmented attention, and algorithmic redundancy. Grounded in Cognitive Load Theory (Sweller, 1988), Multimedia Learning Theory (Mayer, 2005), and neurocognitive frameworks, this study delineates how artificial intelligence-enhanced instruction oscillates between cognitive efficiency and mental exhaustion. It underscores the necessity of pedagogical equilibrium, advocating hybrid models that balance artificial intelligence efficiency with human-led metacognitive intervention. Advancing discourse on artificial intelligence -driven cognitive architecture in foreign language learning, this study posits that well-calibrated artificial intelligence ecosystems enhance linguistic proficiency while mitigating cognitive strain. Future research should examine the longitudinal cognitive effects of artificial intelligence -assisted learning, incorporating neurophysiological methodologies and affective computing to deepen insights into artificial intelligence -mediated cognition and learner autonomy.
Keywords: Artificial intelligence in language learning, cognitive load theory, neural fatigue in digital education, adaptive learning and cognitive overload, ai-driven pedagogical scaffolding