NEUROCOGNITIVE AND DIGITAL PATHWAYS TO ENGLISH FLUENCY: A SAMR-BASED CONSTRUCTIVIST APPROACH FOR RURAL LEARNERS
DOI:
https://doi.org/10.29121/granthaalayah.v14.i2SE.2026.6615Keywords:
Neurocognitive Processing, Phonological Awareness, Phoneme Visualization, Working Memory, Auditory Discrimination, Samr Model, ConstructivismAbstract [English]
This theoretical study examines English speech through a neurocognitive perspective. It seeks to elucidate how Tamil-speaking students in the rural areas process phonological, auditory and working memory processes as they adapt to an English-medium environment. Through the application of phonological loop principles and auditory written language, the paper underscores issues like limited phoneme differentiation, constraints in working memory processing, and challenges in assessing rhythm and stress in the English language. To assist these learners in overcoming their challenges, a constructivist framework that aligns with SAMR (Substitution, Augmentation, Modification, Redefinition) will be provided by implementing digital tools that serve as neurocognitive aids. At the substitution and augmentation stages, software that provides visual depictions of phonemes, converting speech into visuals like waveforms, pitch contours, and spectrograms allows learners to compare their spoken output with native speakers’ pronunciation in English. This comparison improves their auditory perception and the motor aspects involved in sound production. The input stimulates visual and auditory pathways to enhance phonological awareness and verbal accuracy. During the modification and redefine phase, tools like virtual reality speaking (or social skills) simulations and AI-powered pronunciation trainers will be utilized to improve speaking skills, develop speech motor functions, and strengthen neural connections—all aimed at enhancing communicative competence (bilingual fluency and speaking self-assurance) in language learners by maximizing the time they engage in the target language through pattern recognition, metacognitive self-evaluation, and input adjustments.
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