IDEAS home Printed from https://ideas.repec.org/a/igg/jwltt0/v20y2025i1p1-18.html
   My bibliography  Save this article

Enhancing Oral English Proficiency Through Human-Computer Interaction

Author

Listed:
  • Nan Tang

    (Foreign Language College, Zhengzhou Normal University, China)

Abstract

Human-Machine Interaction (HMI) technology has revolutionized the landscape of oral English education, offering new possibilities for improving learning efficiency and experiences. This paper presents an innovative teaching system that integrates real-time speech recognition and feedback capabilities with advanced natural language processing (NLP) and machine learning algorithms. The system is designed to provide personalized learning paths based on learners' performance data, ensuring tailored resources and guidance. Emphasizing user experience and interactive design, it aims to stimulate learner interest and motivation. Research findings indicate significant improvements in students' pronunciation, fluency, and grammar, alongside high levels of user satisfaction. However, challenges remain in fully replicating genuine human interactions and addressing technical limitations. Future work will focus on enhancing conversational abilities, personalization, and multimodal feedback mechanisms to better prepare students for real-world communication scenarios.

Suggested Citation

  • Nan Tang, 2025. "Enhancing Oral English Proficiency Through Human-Computer Interaction," International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), IGI Global, vol. 20(1), pages 1-18, January.
    • Handle: RePEc:igg:jwltt0:v:20:y:2025:i:1:p:1-18
    as

    Download full text from publisher

    File URL: https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJWLTT.377130
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hyun Suk Lee & Junga Lee, 2021. "Applying Artificial Intelligence in Physical Education and Future Perspectives," Sustainability, MDPI, vol. 13(1), pages 1-16, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nadire Cavus & Yakubu Bala Mohammed & Mohammed Nasiru Yakubu, 2021. "An Artificial Intelligence-Based Model for Prediction of Parameters Affecting Sustainable Growth of Mobile Banking Apps," Sustainability, MDPI, vol. 13(11), pages 1-21, May.
    2. Taofeng Liu & Dominika Wilczyńska & Mariusz Lipowski & Zijian Zhao, 2021. "Optimization of a Sports Activity Development Model Using Artificial Intelligence under New Curriculum Reform," IJERPH, MDPI, vol. 18(17), pages 1-13, August.
    3. Leilei Zhao & Xiaofan Wu & Heng Luo, 2022. "Developing AI Literacy for Primary and Middle School Teachers in China: Based on a Structural Equation Modeling Analysis," Sustainability, MDPI, vol. 14(21), pages 1-16, November.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jwltt0:v:20:y:2025:i:1:p:1-18. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.