Omslagsbillede

Enhancing Oral English Proficiency Through Human-Computer Interaction

Guardado en:
Udgivet i:International Journal of Web-Based Learning and Teaching Technologies vol. 20, no. 1 (2025), p. 1-19
Hovedforfatter: Tang, Nan
Udgivet:
IGI Global
Fag:
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
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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