Enhancing Oral English Proficiency Through Human-Computer Interaction

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Detalles Bibliográficos
Publicado en:	International Journal of Web-Based Learning and Teaching Technologies vol. 20, no. 1 (2025), p. 1-19
Autor principal:	Tang, Nan
Publicado:	IGI Global
Materias:	Teaching Speech recognition Students Language proficiency Human technology relationship Feedback Conversation Human-computer interaction Machine learning Technology Comparative studies Hypotheses Motivation Voice recognition Personalized learning Algorithms Learning Real time Speech English language Pronunciation Education Customization Language acquisition Fluency User experience Competence Grammar Educational objectives User satisfaction English proficiency Language instruction Natural language processing Design Ability Satisfaction Oral Language Literature Reviews Comparative Analysis Language Processing Educational Environment Learner Engagement English Language Role Language Usage Guidance Educational Resources Influence of Technology Learning Processes Computers Learning Experience Learning Theories Intelligence Oral English Educational Experience Educational Facilities Improvement
Acceso en línea:	Citation/Abstract Full Text - PDF
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Descripción
Resumen:	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.
ISSN:	1548-1093 1548-1107
DOI:	10.4018/IJWLTT.377130
Fuente:	Engineering Database