AI-Powered Intelligent Transformation of Teaching: A Case Study

Abstract

nature of certain concepts that elude conventional pedagogical approaches. As noted in a systematic review of educational transformation with AI, research in this domain can be organized into four categories: AI as a transformative tool in education, personalization of learning, ethical challenges and digita l divides, and the impact of AI. The personalization dimension is particularly significant. AI systems can analyze individual learner data to identify knowledge gaps, generate targeted interventions, and adapt instructional content to match learners' readiness levels. AI can scale personalized learning (Katiyar et al., 2024), all while gaining students' trust, representing a step toward precision education, meaning the tailoring of instruction to each learn er's specific needs and context . This capacity for precision education addresses a fundamental limitation of Abstract The integration of artificial intelligence (AI) into educational practice represents one of the most significant pedagogical shifts of the twenty -first century, yet the translation of AI capabilities into actionable classroom strategies remains inadequatel y documented. This article presents a case study of AI-powered intelligent transformation in teaching, drawing upon practical implementations in both science and humanities education. Through detailed examination of two instructional cases -a second- grade science lesson on magnets and a fifth-grade humanities lesson on Li Shizhen-the study illustrates how generative AI tools can be strategically deployed to enhance diagnostic assessment, personalize learning, create interactive content, and facilitate conver sational inquiry. The findings demonstrate that AI integration need not be comprehensive to be transformative; rather, targeted deployment at specific instructional junctures-pre-class diagnostics, misconception remediation, and interactive exploration- can yield significant pedagogical benefits while respecting teachers' limited preparation time. The article contributes to the growing body of literature on AI-enhanced pedagogy by providing concrete, replicable examples of AI integration across disciplinary contexts, and by articulating a framework for thoughtful, balanced AI adoption that preserves teacher agency while leveraging AI's unique capabilities for personalization, scalability, and interactive engagement. The study also critically examines the limitations and ethical considerations surrounding AI integration in education, including concerns about content quality, algorithmic bias, data privacy, and the digital divide, thereby offering a balanced perspective on the promises and perils of AI in teaching.