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Developing an AI-Based Learning System for L2 Learners’ Authentic and Ubiquitous Learning in English Language

Author

Listed:
  • Fenglin Jia

    (Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong SAR 999077, China)

  • Daner Sun

    (Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong SAR 999077, China)

  • Qing Ma

    (Department of Linguistics and Modern Language Studies, The Education University of Hong Kong, Hong Kong SAR 999077, China)

  • Chee-Kit Looi

    (National Institute of Education, Nanyang Technological University, Singapore 637616, Singapore)

Abstract

Motivated by the rapid development and application of artificial intelligence (AI) technologies in education and the needs of language learners during the COVID-19 pandemic, an AI-enabled English language learning (AIELL) system featuring authentic and ubiquitous learning for the acquisition of vocabulary and grammar in English as a second language (L2) was developed. The aim of this study was to present the developmental process and methods used to design, develop, evaluate, and validate the AIELL system and to distil key design features for English learning in authentic contexts. There were 20 participants in the tests, with three interviewees in the study. Mixed research methods were employed to analyse the data, including a demonstration test, a usability test, and an interview. The quantitative and qualitative data collected and analysed affirmed the validity and usability of the design and helped identify areas for further improvements to the desired features. This study informs the integration of AI into facilitating language teaching and learning guided by the mobile learning principle.

Suggested Citation

  • Fenglin Jia & Daner Sun & Qing Ma & Chee-Kit Looi, 2022. "Developing an AI-Based Learning System for L2 Learners’ Authentic and Ubiquitous Learning in English Language," Sustainability, MDPI, vol. 14(23), pages 1-18, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:23:p:15527-:d:980544
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    References listed on IDEAS

    as
    1. Qing Ma, 2019. "University L2 Learners' Voices and Experience in Making Use of Dictionary Apps in Mobile Assisted Language Learning (MALL)," International Journal of Computer-Assisted Language Learning and Teaching (IJCALLT), IGI Global, vol. 9(4), pages 18-36, October.
    2. Wei Li, 2012. "Well‐Informed Intermediaries In Strategic Communication," Economic Inquiry, Western Economic Association International, vol. 50(2), pages 380-398, April.
    3. Rustam Shadiev & Xun Wang & Yuliya Halubitskaya & Yueh-Min Huang, 2022. "Enhancing Foreign Language Learning Outcomes and Mitigating Cultural Attributes Inherent in Asian Culture in a Mobile-Assisted Language Learning Environment," Sustainability, MDPI, vol. 14(14), pages 1-17, July.
    4. A. Hariharasudan & Sebastian Kot, 2018. "A Scoping Review on Digital English and Education 4.0 for Industry 4.0," Social Sciences, MDPI, vol. 7(11), pages 1-13, November.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Turgut Karakose & Murat Demirkol & Ramazan Yirci & Hakan Polat & Tuncay Yavuz Ozdemir & Tijen Tülübaş, 2023. "A Conversation with ChatGPT about Digital Leadership and Technology Integration: Comparative Analysis Based on Human–AI Collaboration," Administrative Sciences, MDPI, vol. 13(7), pages 1-19, June.

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    More about this item

    Keywords

    artificial intelligence; mobile learning; authentic and ubiquitous learning; system development; English as L2 learner;
    All these keywords.

    JEL classification:

    • L2 - Industrial Organization - - Firm Objectives, Organization, and Behavior

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