Against the backdrop of educational digital transformation, AI-enabled teaching has become an important path to advance educational reform. Based on the CiteSpace tool, this paper conducts a bibliometric analysis of 2,119 articles indexed in CNKI (Peking University Core and CSSCI journals) from 2001 to 2025, and systematically reviews the research evolution, core research groups, hot topics, and cutting-edge trends in this field. The findings show that the number of publications in this field has maintained continuous growth, experiencing three stages: initial germination (2001–2016), rapid development (2017–2020), and deepening maturity (2021–2025). Several high-impact research teams have emerged, but the overall cooperation network remains relatively loose. Research hotspots focus on technology application models, personalized teaching, teaching model innovation, teaching evaluation optimization, and the adaptation of teachers’ and students’ competencies. Current research is challenged by homogenized technology application, inadequate empirical research, and insufficient adaptation of teachers’ and students' competencies. Future research should focus on the in-depth integration of technology with disciplines, multi-dimensional empirical testing, the construction of a teacher and student competency improvement system, and multi-scenario adaptation, so as to promote the development of AI-enabled teaching toward refinement, personalization, and normalization.