DIGITAL TOOLS VS. PERSONALIZED OUTCOMES: INVESTIGATING THE EFFECTIVENESS OF AI-DRIVEN ADAPTIVE QUIZZING IN ADDRESSING AUDITORY VS. VISUAL LEARNER DISPARITIES IN ENGLISH GRAMMAR ASSESSMENT
Keywords:
Adaptive quizzing, artificial intelligence, learner disparities, auditory learners, visual learners, grammar assessment, personalized learning, digital tools, cognitive load, assessment equity.Abstract
This study investigates the efficacy of AI-Driven Adaptive Quizzing (ADAQ) in achieving assessment equity by targeting differences between auditory and visual ESL learners in grammar evaluation. Drawing on Cognitive Load Theory, the research hypothesized that traditional visual-biased testing imposes an undue extraneous load on auditory learners. ADAQ dynamically adjusted the stimulus modality—providing audio prompts for auditory profiles and graphic organizers for visual profiles—to align the assessment format with the dominant learning style. The findings from a quasi-experimental design revealed a statistically significant reduction in the performance gap between the two groups in the ADAQ environment compared to the control group (traditional text-based testing), thereby confirming that personalized digital tools can effectively mitigate assessment bias and enhance the accuracy of linguistic competence measurement.
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