ENHANCING VOCABULARY ACQUISITION FOR NEURODIVERGENT LANGUAGE LEARNERS USING COGNITIVE LOAD OPTIMIZATION IN AUGMENTED REALITY

0Citations
Citations of this article
39Readers
Mendeley users who have this article in their library.
Get full text

Abstract

This paper examines whether or not Augmented Reality (AR) is effective in the acquisition of vocabulary in neurodivergent language learners using the Cognitive Load Theory. The conventional teaching methods tend to cause a lot of extraneous cognitive load that poses a great obstacle to students with ADHD and Autism Spectrum Disorder. To combat this, an AR-based learning environment was created with the particular aim of maximizing cognitive resources through the reduction of visual clutter and delivery of spatial, just-in-time linguistic cues. The quantitative experimental design was used, whereby 60 neurodivergent individuals were randomly allocated to either an AR-enhanced experimental group or a conventional digital interface control group. The instrument of data collection was the NASA-TLX (Task Load Index), which was used to determine the cited perceived cognitive effort, and pre-and post-tests were conducted to determine the short-and long-term vocabulary retention. Statistical analysis is a strong indicator of the success of the intervention. Results from independent samples t-tests indicate that the AR group experienced a statistically significant reduction in cognitive load (t (58) =-8.92, p < 0.001) and a massive improvement in long-term retention (t (58) = 11.45, p < 0.001) compared to the control group. Specifically, the experimental group maintained a mean delayed post-test score of 82.4%, while the control group dropped significantly to 54.3%, yielding a large effect size of Cohen's d = 1.42. Moreover, the total task load and delayed recall accuracy are correlated (r =-0.74). Such results imply that the AR with the optimization of the cognitive load is a better pedagogical approach to inclusive language education. The study can be utilized in developing a data-driven, scalable model of technologically neuro-inclusive designs, where cognitive accessibility is a priority, as opposed to conventional rote memorization.

Cite

CITATION STYLE

APA

Niyozova, D., Mizrobov, D., Allakuliev, A., Khamdamova, S., Ismailov, A., Toshboev, Z., & Khasanov, S. (2026). ENHANCING VOCABULARY ACQUISITION FOR NEURODIVERGENT LANGUAGE LEARNERS USING COGNITIVE LOAD OPTIMIZATION IN AUGMENTED REALITY. Archives for Technical Sciences, 2026-May(35), 584–597. https://doi.org/10.70102/afts.2026.1835.584

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free