AI-Driven Vocabulary Acquisition in EFL Higher Education: Interdisciplinary Insights into Technological Innovation, Ethical Challenges, and Equitable Access

Authors

  • Omer Elsheikh Hago Elmahdi

    Department of Languages and Translation, Open University of Sudan (Affiliated with Taibah University), Janadah Bin Umayyah Road, Tayba, Madinah 42353, Saudi Arabia
  • Asjad Ahmed Saeed Balla

    Department of English Language & Literature, College of Languages and Humanities, Qassim University, P.O. Box 6611, Buraidah 51452, Saudi Arabia
  • Abbas Hussein Abdelrady

    Department of English Language & Literature, College of Languages and Humanities, Qassim University, P.O. Box 6611, Buraidah 51452, Saudi Arabia
  • Eshraga Othman

    English Department, Applied College, King Khalid University, P.O. Box 9004, Abha 61413, Saudi Arabia
  • Awwad Othman Abdelaziz Ahmed

    Foreign Languages Department, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia

DOI:

https://doi.org/10.30564/fls.v7i4.8760
Received: 16 February 2025 | Revised: 18 March 2025 | Accepted: 27 March 2025 | Published Online: 10 April 2025

Abstract

This study investigates the efficacy, cultural relevance, and ethical implications of AI-driven vocabulary learning tools through a mixed-methods approach combining a PRISMA-guided systematic review of 58 studies and controlled experiments across six global contexts. Results demonstrate that AI tools significantly outperform traditional methods, with a pooled Cohen’s d of 0.61 (95% CI: 0.52–0.70) for retention gains. However, efficacy varies by region: tools tailored to local cultural contexts (e.g., dialect-aware chatbots in Nigeria) achieved effect sizes up to d = 0.85, while culturally generic systems lagged (d = 0.38). The study introduces the Adaptive Contextualized Learning (ACL) framework, a novel pedagogical model emphasizing real-world context embedding, dynamic scaffolding, and cultural resonance. ACL-driven interventions improved proficiency benchmarks by 35% compared to static AI systems, addressing gaps in temporally adaptive and culturally sustaining AI education. Ethical risks, including algorithmic bias (e.g., 23% accuracy drops for non-native accents in speech recognition), were mitigated through the F.A.I.R. Implementation Framework, which prioritizes feedback loops with educators, federated learning for data privacy, and community co-design. Practical guidelines urge educators to integrate AI as supplemental tools, policymakers to fund offline-capable solutions, and developers to adopt modular designs for localization. Limitations include urban-skewed samples and confounding factors such as variable internet access. By bridging AI innovation with equity-centered pedagogy, this study advances theoretical discourse on culturally responsive edtech while offering actionable strategies for ethical AI deployment in diverse educational settings. Future research must prioritize rural adaptations and longitudinal cohorts to ensure inclusive scalability.

Keywords:

Context-Sensitive; Equity; Ethics; Interdisciplinary; Vocabulary

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How to Cite

Elsheikh Hago Elmahdi, O., Ahmed Saeed Balla, A., Hussein Abdelrady, A., Othman , E., & Othman Abdelaziz Ahmed, A. (2025). AI-Driven Vocabulary Acquisition in EFL Higher Education: Interdisciplinary Insights into Technological Innovation, Ethical Challenges, and Equitable Access. Forum for Linguistic Studies, 7(4), 477–491. https://doi.org/10.30564/fls.v7i4.8760

Issue

Vol. 7 , Iss. 4 (April 2025): In Progress

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Article

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Copyright © 2025 Omer Elsheikh Hago Elmahdi, Asjad Ahmed Saeed Balla, Abbas Hussein Abdelrady, Eshraga Othman , Awwad Othman Abdelaziz Ahmed

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This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.