Artificial Intelligence and Teaching Strategies: A Comparative Study of Higher Education in China and the United States
Year: 2024
Type: article
Abstract: This study explores the integration of artificial intelligence in higher education teaching strategies in China and the United States, providing theoretical insights for global educational advancement. By systematically reviewing existing literature, the study identifies theoretical and practical pathways for embedding AI in these countries' educational systems. It examines the impact of AI on improving teaching strategies and educational quality. Utilizing qualitative research, policy analysis, and comparative methods, the research highlights specific differences and common challenges in AI's educational applications between the two nations. The analysis notes that the U.S. prioritizes personalized, data-driven teaching approaches due to its early adoption of AI, while China focuses on strategic investments to enhance educational quality and efficiency. Cultural distinctions and policy priorities influence how educational resources are allocated and teaching strategies are chosen, reflecting each country's strategic goals and educational values. The study concludes with recommendations for optimizing global teaching strategies through international collaboration and technology exchange. It emphasizes that AI in education is still in an exploratory stage, requiring ongoing assessment of its effectiveness and sustainability to foster a more innovative and inclusive teaching landscape.
Authors: Fanlong Meng, Wenxun Luo
Institutions: Delaware State University, University College London
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W4409891478