Integrating AI into Education

Artificial intelligence (AI) is profoundly changing the way knowledge is produced, disseminated, and applied, continuously reshaping the organizational logic, supply forms, and governance models in education. The core of “AI + Education” lies in two words: “integration” and “transformation.” Integration aims to establish a deep fusion of AI and education, allowing AI to enter the basic structure, chain, and space of educational operations. Transformation seeks to leverage AI as an engine for educational reform, driving systematic changes in school models, teaching methods, management systems, and support mechanisms. Specifically, the concepts of comprehensive elements, processes, and scenarios are the concrete manifestations of integration, while school management, teaching, and support form the practical focus of transformation.

Comprehensive Elements

Comprehensive elements refer to the deep integration of AI into key educational components. Education is not a simple addition of individual segments but a complex system comprising students, teachers, environments, and more. The “AI + Education Action Plan” emphasizes AI’s empowering role at critical nodes such as teaching, learning, management, research, and internationalization. For teachers, it highlights the importance of enhancing digital literacy and intelligent application capabilities, enabling them to optimize teaching design, improve methods, and elevate professional standards. For students, it stresses the need for AI literacy education to enhance their learning abilities, thinking quality, and complex problem-solving skills. In terms of disciplines and research, it calls for adapting to changes in knowledge production methods, promoting interdisciplinary integration, dynamic adjustments of specialties, and innovative research paradigms. Thus, the emphasis on comprehensive elements is not merely about adding technology to existing educational structures but about fostering a deeper coupling between AI and key educational components, enhancing the adaptability, resilience, and innovation capacity of the educational system.

Comprehensive Processes

Comprehensive processes mean that AI should permeate all stages and segments of educational development. The Action Plan outlines AI education across all educational stages and general education for society: in primary and secondary education, the focus is on popularizing AI literacy, solidifying digital literacy and cognitive foundations, helping students establish a basic understanding and correct attitude towards AI; in vocational education, the emphasis is on aligning with industry needs, promoting professional upgrades and skill restructuring, enhancing students’ practical abilities in intelligent production, services, and management; in higher education, the focus is on strengthening basic research, interdisciplinary integration, and cultivating top innovative talents, making AI a crucial support for public foundational courses and interdisciplinary studies; and in lifelong education, the emphasis is on providing general education and skills training for all societal members, enhancing overall AI literacy and adaptability to technological changes. This forms a developmental pattern that connects from basic to professional education, from school to society. Furthermore, this connection is not merely a temporal extension but a systematic linkage of educational goals, curriculum content, training methods, and evaluation mechanisms. AI should not only enter every educational stage but also develop a progressive and spiraling training system according to the educational functions and developmental tasks of different stages.

Comprehensive Scenarios

Comprehensive scenarios signify that AI applications in education must transcend traditional classroom and school boundaries, entering more open, complex, and collaborative educational spaces. With profound changes in learning methods, resource forms, and educational organization, educational activities increasingly occur in interactions among schools, families, society, online spaces, and blended environments. The Action Plan proposes building future classrooms, schools, learning centers, and training centers, promoting thematic learning scenarios, virtual simulation experiments, smart MOOCs, and collaborative applications of intelligent terminals. The core aim is to construct a new educational ecosystem with multidimensional interactions, allowing learning activities to unfold in more authentic, richer, and personalized scenarios. Comprehensive scenarios not only expand the application space of technology but also reconstruct educational organization and resource supply methods. Notably, the document specifically addresses rural schools, remote areas, special education groups, and social learners, indicating that AI educational applications are not solely for high-level schools and advantageous regions but have a clear inclusive orientation, aiming to lower the barriers to accessing quality educational resources through technological means and promote educational equity from opportunity to process and quality.

Practical Implementation

From a practical perspective, promoting the integration of AI into education across comprehensive elements, processes, and scenarios essentially requires achieving deep transformations in four areas.

1. School Models: Transition from relatively closed, singular supply traditional schooling to open, shared, collaborative, and boundary-less intelligent schooling. AI does not merely bring minor adjustments to a teaching segment but profoundly reconstructs school organizational forms and resource supply systems. In the past, schools relied more on internal courses, teachers, and spaces. In the intelligent era, quality educational resources will flow in broader forms, linking courses, teachers, platforms, and industry resources over a wider range. Schools must shift from resource-occupying models to resource-integrating and platform-supporting models, fostering a resource supply pattern involving diverse participation from government, schools, enterprises, research institutions, and society. Especially in vocational and higher education, there should be further promotion of the integration of science and education, and industry and education, exploring new mechanisms for collaborative curriculum development, project co-construction, and talent co-cultivation, making school education more aligned with technological frontiers, industrial changes, and real-world problem contexts. AI is driving school models from clearly defined closed systems to open and shared ecosystems, essentially reconstructing school organizations and serving as a crucial breakthrough for educational reform.

2. Teaching Methods: Shift from knowledge transmission to personalized learning, competency orientation, and human-machine collaborative education. In the intelligent era, the threshold for knowledge acquisition is significantly lowered, making traditional teaching methods focused solely on knowledge points and standardized answer training inadequate for future talent requirements. The focus of education must shift from knowledge transmission to competency cultivation, from uniform pacing to tailored instruction and individual development. AI can provide more precise learning support through learning situation analysis, path recommendations, and process evaluations, creating conditions for teachers to implement differentiated teaching and precise education. Future classrooms should emphasize problem-based learning (PBL) and project-based learning, promoting the triadic collaboration of teachers, machines, and students, facilitating students’ learning, exploration, cooperation, and creation in real tasks and complex situations, while focusing on cultivating students’ judgment, deep questioning, and innovative reconstruction abilities. AI can take on repetitive and procedural tasks but cannot replace teachers’ core roles in value guidance, emotional support, ethical judgment, and character development. Therefore, the transformation of teaching methods must closely integrate the construction of future teachers and future classrooms, encouraging teachers to shift from knowledge transmitters to learning designers, growth facilitators, and innovation guides.

3. Management Systems: Transition from hierarchical, experience-based, and relatively extensive traditional management to flat, agile, data-driven, and precise decision-making modern governance. For a long time, educational management in many scenarios has relied heavily on experiential judgment, static statistics, and segmented management, often facing challenges such as insufficient responsiveness, lack of smooth collaboration, and imprecise resource allocation. The introduction of AI provides important conditions for reconstructing educational governance processes. The Action Plan uses an intelligent educational brain to drive reforms in talent supply and demand, examination evaluation, employment services, and safety warnings, promoting governance from fragmentation to integration, from experience-driven to data-driven, and from reactive to proactive analysis. Future schools should not only have more technical equipment and richer application scenarios but also possess more scientific governance structures, efficient management operations, and precise decision-making mechanisms. Through intelligent analysis and decision support, schools and educational administrative departments can better grasp student development patterns, optimize resource allocation, adjust professional structures, and enhance management efficiency, pushing management systems towards a flatter, more agile, and collaborative direction. However, humans will always be the main body of governance, and it is essential to maintain that technology aids decision-making while responsibility ultimately lies with people, ensuring educational equity, adherence to educational laws, and ethical boundaries while enhancing governance capabilities.

4. Support Mechanisms: Transition from decentralized construction, partial support, and element stacking to systematic support through institutional coordination, standard guidance, platform integration, and collaborative innovation. The deep integration of AI into education requires a complete support system covering policies, standards, infrastructure, teacher training, research support, safety governance, and ecological collaboration. On one hand, it is necessary to strengthen the construction of new educational infrastructure, such as computing power, data, platforms, and models, and to improve data governance, algorithm standards, privacy protection, content safety, and risk prevention mechanisms to provide a reliable foundation for educational intelligence. On the other hand, the development of the teaching workforce must be prioritized, enhancing teachers’ abilities to harness intelligent tools, optimize teaching processes, and implement human-machine collaborative education in light of the new roles and missions assigned by AI. Additionally, collaboration among universities, research institutions, government, enterprises, and schools must be strengthened to form a multi-party supported UGBS collaborative innovation model, creating synergy in basic research, technology development, scenario implementation, and evaluation reform. Only through coordinated efforts in institutional, resource, teacher, and innovation ecological support can the integration of “AI + Education” move from pilot exploration to large-scale application and from technology being usable to being effectively utilized in education.