Cognitive Foundations of Immersive MALL: How Extended Reality Shapes Language Processing in Mobile Contexts

Antony Desilva D.; Vijayakumar Selvaraj; Sathikulameen A.; Emmanuel Rajkumar B.

doi:10.3991/ijim.v20i04.59895

Authors

Antony Desilva D. B.S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, India https://orcid.org/0009-0001-4390-6596
Vijayakumar Selvaraj B.S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, India https://orcid.org/0000-0002-5932-3467
Sathikulameen A. The New College, Chennai, India https://orcid.org/0000-0002-9633-7585
Emmanuel Rajkumar B. The New College, Chennai, India https://orcid.org/0000-0001-5312-5761

DOI:

https://doi.org/10.3991/ijim.v20i04.59895

Keywords:

Extended Reality (XR), Mobile-Assisted Language Learning (MALL), cognitive scaffolding, neurolinguistics, lexical access, semantic integration, eye-tracking

Abstract

This study examines how extended reality (XR), which includes both virtual and augmented reality, alters adult English language learners’ real-time language processing in an ESL setting. We investigate whether immersive and spatially anchored XR environments can promote deeper lexical retrieval and more fluid semantic integration during every day, context-rich language practice, going beyond the flat and screen-bound interactions common in mobile learning apps. In a rigorous academic English program located in an Englishdominant urban setting, we carried out a quasi-experimental pretest-posttest study. In one group, two complete classes (N = 68) used mobile-tethered XR to interact with vocabulary and sentence comprehension materials, while the other group used standard smartphone interfaces. Notably, every participant lived and studied in a real-world ESL environment where learning English is a daily necessity rather than merely a subject in the classroom. We recorded response latencies and eye movements during comprehension exercises. ANCOVA and linear mixed-effects models that controlled for working memory capacity, first-language background, and baseline proficiency were used to analyze the data. The findings demonstrated that learners who used XR-MALL (mobile-assisted language learning) processed target input much more quickly and accurately than those in the control group: contextual inference accuracy increased by 18% (p = 0.002), and lexical decision times decreased by an average of 92 milliseconds (p < 0.001). Eye-tracking patterns also revealed that speakers of Tamil and Hindi had better visual-linguistic alignment in XR, focusing on semantically relevant objects faster and keeping their eyes on them longer when speaking. This shows that XR is a powerful cognitive framework that aids students in overcoming enduring difficulties with referential grounding, especially those brought on by the linguistic divide between L1 and L2. XR reconfigures meaning, access, and integration by grounding language learning in embodied, spatial contexts, rather than just adding novelty. Our results provide useful advice for developing fair, cognitively responsive MALL tools that appeal to a variety of real-world learners and theoretically shed light on how situated cognition influences language comprehension in immersive settings.

Author Biographies

Antony Desilva D., B.S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, India

Antony Desilva D is a research scholar at the Department of English at B.S. Abdur Rahman Crescent Institute of Science and Technology, India. His research area is Neurolinguistic Programming. His orcid is https://orcid.org/0009-0001-4390-6596

Vijayakumar Selvaraj, B.S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, India

Dr. S. Vijayakumar is an Associate Professor in the Department of English at B.S. Abdur Rahman Crescent Institute of Science and Technology, India, with a PhD in Applied Linguistics. His research is anchored in computer-assisted language learning, AI-driven pedagogical innovation, and inclusive digital education. Over 18 publications have appeared in Q1 journals such as The International Review of Research in Open and Distributed Learning, Humanities & Social Sciences Communications, and Transactions on Emerging Telecommunications Technologies. His interdisciplinary work integrates natural language processing, neuro-linguistic programming, and adaptive learning systems to enhance L2 proficiency, accessibility, and equity in digital learning environments. Several patents have been filed on AI-based educational tools, including assistive technologies for visually impaired learners. He supervises six doctoral candidates and serves as a peer reviewer for multiple Q1 journals. His scholarly output reflects a sustained commitment to evidence-based interventions that bridge pedagogy, technology, and linguistic equity.

Sathikulameen A., The New College, Chennai, India

Dr. A. Sathikulameen is an Assistant Professor and Research Supervisor in the Postgraduate & Research Department of English at The New College, Chennai. With 16 years of experience in education, he specializes in English Language Teaching, Language Acquisition and Pedagogy, Multimedia and Technology-Enhanced Language Learning, and Cultural Studies. He has published 18 research papers in various indexed journals, contributed to conference proceedings, and authored several books and chapters in edited volumes. He has also been actively involved in organizing and coordinating academic programs, workshops, and conferences. He has served as a resource person, keynote speaker, and examiner in numerous academic and professional development events. His Orcid ID is https://orcid.org/0000-0002-9633-7585

Emmanuel Rajkumar B., The New College, Chennai, India

Emmanuel Rajkumar B, Postgraduate and Research Department of English at The New College, University of Madras. His research areas include Ecocriticism, Cultural Studies, and English Language Teaching. His ORCID id is https://orcid.org/0000-0001-5312-5761

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