The rapid advancement of artificial intelligence technology is exerting unprecedented pressure for transformation in the field of education. Traditional teaching models, challenged by technological disruption, evolving educational demands, and societal digital transformation, urgently require exploration of new pathways adapted to the contemporary era. This article examines the inevitability of AI-driven teaching reform from three perspectives: technological impetus, educational needs, and social transformation. By analyzing specific application scenarios—such as intelligent tutoring systems, classroom management tools, and virtual instructors—it explores the reconstruction of teaching models across dimensions including subject relationships, teaching processes, evaluation systems, and learning spaces. The study finds that AI not only enables personalized instruction through adaptive learning platforms but also enhances classroom management efficiency via technologies such as emotion recognition. However, it also encounters practical challenges, including ethical concerns, data privacy issues, and a potential deficit in humanistic care. In response, this article proposes strategies such as establishing a human-machine collaborative teaching framework, refining educational ethical guidelines, and strengthening university-industry collaboration. The study emphasizes that future education should construct a ternary collaborative ecosystem involving teachers, AI, and students. While integrating emerging technologies such as the metaverse, education should prioritize cultivating core competencies beyond AI’s reach—such as creativity and critical thinking—thereby achieving a balanced development of technological empowerment and the essence of education.

Pingshan Wang. Research on the Transformation and Reconstruction of Modern Teaching Mode under the Background of Artificial Intelligence. Frontiers in Educational Research (2025), Vol. 8, Issue 10: 114-119. https://doi.org/10.25236/FER.2025.081017.

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