Optimizing Intrinsic Cognitive Load in AI-Supported EFL Vocabulary Learning: Evidence from a Saudi Preparatory-Year Program
DOI:
https://doi.org/10.53103/cjlls.v6i1.260Keywords:
Cognitive Load Theory, EFL Vocabulary, Artificial Intelligence, Preparatory Year, Saudi ArabiaAbstract
Vocabulary acquisition is a central component of English as a Foreign Language (EFL) learning, yet it places considerable demands on learners’ working memory. Grounded in Cognitive Load Theory (CLT), this study examines whether optimizing intrinsic cognitive load through targeted pedagogical strategies and artificial intelligence (AI) enhances vocabulary learning. Using a quasi‑experimental design, Saudi preparatory‑year students learned 30 target words from Evolve 2: Special Edition under an optimized condition that incorporated AI‑assisted tools and cognitively informed instructional strategies (n = 38), while another 30 words were taught through traditional teacher‑centered instruction in a non‑optimized condition (n = 35). Vocabulary achievement was measured using two parallel 30‑item multiple‑choice tests, each aligned with the vocabulary taught in its respective condition. Results showed a statistically significant advantage for the optimized condition (M = 96%) over the non‑optimized condition (M = 73%), t(71) = 14.85, p < .001, with a very large effect size (d = 3.48). These findings provide strong evidence for the effectiveness of AI‑supported, cognitive load–optimized vocabulary instruction in EFL contexts.
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