Five government departments have jointly issued the "Artificial Intelligence + Education" Action Plan, proposing the inclusion of artificial intelligence in teacher qualification examinations and certification processes. The plan, released in April 2026, sends a clear signal that future educators must not only impart knowledge but also master intelligent tools and conduct human-machine collaborative teaching.
The action plan emphasizes leveraging AI to empower teaching practices across pre-class, in-class, and post-class educational activities. It advocates for strengthening intelligent teaching system applications to reduce teacher workload while improving efficiency, assisting teachers in homework management through automated grading, Q&A support, and tutoring services. Additionally, the plan recommends using smart technology to analyze classroom teaching behaviors to help teachers enhance instructional quality.
Education experts believe incorporating AI application theory into teacher qualification standards will systematically improve the teaching workforce's theoretical understanding of AI skills utilization and technical ethics. This approach will enable teachers to scientifically guide students in the proper use of AI products throughout the educational process. However, the actual effectiveness and potential risks of integrating AI technology in education require ongoing observation, evaluation, and optimization in future teaching practices.
At a primary school in Beijing, fifth-grade mathematics teacher Liu demonstrates the practical implementation of the action plan. Each day after class, Liu uses an AI learning device to conduct comprehensive assessments of students' mastery of mathematical concepts. The system automatically generates detailed data analysis reports highlighting individual student weaknesses—some struggle with geometry word problems while others frequently err in calculation steps.
"Previously, grading assignments and analyzing learning conditions required at least two hours daily. With AI assistance, this time can be redirected toward focusing more attention on students," Liu explained. The saved time allows for deeper analysis of learning data and addressing students' emotional needs. For students who work hard but progress slowly, Liu now writes personalized encouraging comments and distributes campus currency rewards to acknowledge their improvements.
"All 40 children in my class are unique individuals. Understanding their personalities and experiences requires nuanced emotional care that AI cannot provide," Liu noted, emphasizing clear boundaries in AI usage—the technology handles error correction and data analysis within set parameters, while teachers focus on value guidance and emotional support.
In November 2025, the Ministry of Education's Teacher Development Expert Committee released China's first national-level guidelines for generative AI applications in teaching. Experts view this as marking the systematic entry of generative AI into education following initial exploration phases.
From the teacher perspective, AI-era education activities aim for human-machine collaboration, promoting deep integration of technology with educational scenarios. For students, AI technology facilitates multidimensional interaction among teachers, students, and technology, enabling personalized learning based on individual characteristics to improve learning efficiency and core competencies. AI-supported instant feedback and adaptive learning paths help shift students from passive reception to active inquiry, cultivating self-directed learning capabilities.
From an educational equity standpoint, deep integration of AI technology into teaching scenarios can effectively break through spatiotemporal limitations and resource circulation barriers, helping address imbalances in educational resources across regions, urban-rural areas, and schools. Regarding educational governance and evaluation, AI technology can specifically resolve issues in traditional education management such as subjective experience dominance, extensive resource allocation, and delayed teaching quality monitoring, thereby optimizing education governance systems. Simultaneously, AI assists in digital collection and intelligent analysis of teaching behaviors, combining outcome evaluation with process evaluation and comprehensive assessment to promote scientific education decision-making and refined management.