Integrating Artificial Intelligence into Education: Opportunities, Challenges, and Response Strategies - Yundong Wu (Al lnnovations and Applications, 2025)
This study systematically examines the key issues arising from the application of artificial intelligence (AI) technology in the education sector and analyzes its potential impacts on academic research and teaching activities. By extensively reviewing a large volume of literature, the paper identifies two major challenges in AI-driven educational practices: first, ethical risks associated with academic research, and second, a decline in learning quality, reduced teacher-student communication, increased technological dependency, and educational inequality resulting from resource disparities due to the integration of AI into teaching. The research findings indicate that while AI enhances learning efficiency, optimizes resource allocation, and supports personalized learning, it exhibits significant shortcomings in data privacy protection, security assurance, learner capability development, and teaching quality assurance. Additionally, AI-generated content lacks traceability of sources and the ability for empirical validation, posing potential risks in academic writing and knowledge dissemination. To address these challenges, the study proposes improvement strategies, including establishing auditable algorithm mechanisms, refining data governance frameworks in educational institutions, strengthening teacher training in digital literacy, and formulating AI ethical guidelines suitable for academic environments. This research deepens the understanding of the limitations of AI in education and provides insights for the healthy integration of AI technology into educational systems.