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Description
Although acquiring academic vocabulary for productive use has long been a challenge for Vietnamese IELTS candidates, this difficulty frequently contributes to cognitive overload and inefficient study pacing (Sweller, 1988; Nation, 2001). Despite the growing adoption of Spaced Repetition Systems (SRS) as active recall tools, research has yet to determine how frequently learners should retrieve vocabulary, or which task types best support the transition from passive recognition to active production in reading and writing (Webb, 2007). This paper introduces Beezy, a proposed AI-integrated web platform designed to address this gap by embedding generative-AI-triggered practice tasks within SRS scheduling. The study investigates three variables hypothesized to influence this recognition-to-production transition: (1) retrieval frequency — the number of exposures required before a word can be actively deployed; (2) exercise type — the differential effects of reading-based versus writing-based retrieval tasks; and (3) language of instruction — whether L1 Vietnamese or L2 English glossing more effectively supports productive vocabulary use in IELTS contexts. Using quantitative analysis of backend learner data — including accuracy rates, response times, error patterns, and learning mode — the study aims to identify behavioral thresholds necessary for vocabulary internalization. Findings are expected to offer actionable, data-driven guidance for educators and EdTech developers seeking to optimize vocabulary sequencing within AI-powered digital learning environments.
Keywords: spaced repetition systems, academic vocabulary, IELTS, artificial intelligence, retrieval frequency
