Utilizing AI models to optimize blended teaching effectiveness in college-level English education

• Published in: Cogent education Vol. 10; no. 2
• Main Authors: Shi, Lizhen, Muhammad Umer, Arshad, Shi, Yanting
• Format: Journal Article
• Language: English
• Published: Abingdon Cogent 11.12.2023; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Artificial intelligence; Blended Learning; blended learning theory; College English; Deep learning; Educational Quality; English Curriculum; Higher education; Independent Study; Learning Theories; Teacher Effectiveness; Teaching
• ISSN: 2331-186X; 2331-186X
• DOI: 10.1080/2331186X.2023.2282804

This paper proposes the adoption of AI technologies in higher education to support student learning. Using multi-modal blended learning theory and independent learning fundamental theory, the study explores the use of AI to evaluate and improve the effectiveness of blended teaching in college English courses. A new model of deep learning and a learning model of human job functions are proposed to explore the hybridization of college English education under the background of artificial intelligence. This study provides a road map for using AI in college-level English courses and offers valuable contributions to the field, including the proposed models of deep learning and human job functions which can be applied to other subjects and fields. By leveraging modern technologies such as cloud computing, big data, and AI. This study highlights the potential for educators to transform the way we teach and learn and improve the quality of education and support student success. Overall, this paper provides valuable insights for future research in the intersection of AI and education and emphasizes the importance of integrating technology in higher education to enhance the learning experience and meet the needs of modern students.