Writing amid the rapid maturation of large-scale AI systems, Wang frames the question in structural rather than incremental terms. Traditional education rests on a chain of transmission: experts hold knowledge, schools organize that knowledge into curricula, and teachers deliver it to students who reconstruct it through study and practice. AI now inserts itself into every link of that chain. A student driven by curiosity can, in principle, move from building knowledge from scratch to profound understanding through continuous interaction with an AI system, bypassing many of the institutional intermediaries that once stood between question and answer. It is this displacement of the transmission channel, Wang contends, that constitutes AI’s deepest restructuring of education’s underlying logic.

The technical mechanisms behind this shift are worth unpacking. Modern conversational AI systems, built on large language models trained on vast corpora of human writing, can generate explanations tailored to a learner’s current state of understanding, answer follow-up questions instantly, produce worked examples on demand, and adapt the difficulty and framing of material in real time. In effect, such systems replicate, at scale and at negligible marginal cost, many of the dialogic functions of a skilled tutor. When a learner can interrogate an inexhaustible, ever-available interlocutor that responds to each input with increasingly coherent and contextually calibrated feedback, the bottleneck that historically made institutional instruction indispensable, namely scarcity of expert attention, begins to dissolve.

Wang anticipates that as these capabilities mature, AI will become the dominant organizing force in education, and that human teachers will therefore be compelled to seek a path of symbiosis with AI rather than compete with it on the terrain of information delivery. The teacher’s role migrates up the cognitive and affective hierarchy: away from dispensing facts and toward cultivating curiosity, judgment, motivation, ethical orientation, and the metacognitive skills needed to learn effectively alongside, and through, intelligent systems. Symbiosis in this reading is not a slogan but a division of labor, in which AI supplies the adaptive, encyclopedic, and endlessly patient layer of instruction while humans supply purpose, mentorship, and socialization.

Yet the commentary also identifies a tension at the heart of this transformation. If AI progressively takes over the knowledge-imparting function, the long-term primacy of competency development may weaken. Education systems have traditionally justified themselves not merely by what students know but by what they can do: the competencies, habits of mind, and capacities for collaboration and problem-solving that schools are supposed to cultivate. When knowledge acquisition becomes nearly frictionless, the developmental work of turning information into capability risks being underemphasized, because the institutional scaffolding that once forced students to struggle productively with material erodes along with the delivery bottleneck. Wang argues that this weakening could lead to substantive alterations across multiple dimensions of the traditional educational model, altering assessment, curriculum design, and the metrics by which learning itself is judged.

The analysis is explicitly situated in Chinese policy. Wang draws inspiration from the Opinions on deepening the implementation of the ‘AI Plus’ initiative issued by the State Council of the People’s Republic of China in 2025, using it as evidence that the continued development of AI is a deliberate societal project rather than an autonomous technological drift. The commentary further argues that China’s digital education transformation is synchronized with the national goal, set out in the 2024–2035 master plan on building China into a leading country in education, of achieving world-class educational strength. In this framing, AI-driven restructuring is not only a pedagogical question but a strategic one, tied to state ambitions and the pace of national digital infrastructure.

To organize the field, Wang adopts and clarifies the analytical framework laid out in the White Paper on China’s Smart Education, published by the Ministry of Education of the People’s Republic of China in 2025. The framework spans four key dimensions arranged from the micro to the macro level: future teachers, future classrooms, future schools, and future learning centers. At the micro end, the future teacher is reconceived as a professional who orchestrates human and machine intelligences in tandem. At the classroom level, the unit of instructional design becomes a hybrid environment in which AI-mediated interaction is a first-class component rather than an add-on. At the school level, governance, staffing, and organizational structure must accommodate learners whose primary instructional relationship may be with a machine. At the macro end, future learning centers suggest a decoupling of learning from the physical and temporal constraints of the traditional school, with institutions repositioned as hubs for guidance, certification, and community rather than as sole gatekeepers of knowledge.

The commentary does not shy away from the more provocative questions raised by the technology’s trajectory. It references the keynote delivered by computer scientist Geoffrey Hinton at the 2025 World Artificial Intelligence Conference and Global AI Governance High-Level Meeting in Shanghai, which asked whether digital intelligence will replace biological intelligence. Wang uses this framing to underline the stakes: if the systems being built approach or exceed human cognitive performance in domains relevant to instruction, then the question is not whether education will change but whether human institutions can steer the change toward outcomes that preserve human developmental goals. The commentary’s answer is a call for anticipatory theoretical work, laying a foundation now, before the restructuring hardens into defaults that no one deliberately chose.

For researchers, the paper’s principal contribution is the overarching framework it establishes for subsequent study. By mapping AI’s impact from the individual learner’s interaction loop up through classrooms, schools, and system-wide learning centers, it provides a common vocabulary for a field that has often produced fragmented findings: studies of tutoring chatbots here, studies of teacher workload there, policy analyses elsewhere, with little integration. The four-dimension structure, anchored in China’s smart education white paper but generalizable in scope, is intended to guide empirical and theoretical research on how the knowledge-imparting function migrates to machines and what replaces it as the core function of human educators.

What emerges is a picture of education at an inflection point comparable to the invention of writing or the printing press, moments when the technology of transmission restructured the institution built around it. The printing press democratized access to text but left the teacher in charge of interpretation; AI threatens to automate interpretation itself. If Wang is right, the institutions that survive will be those that redefine their value proposition, from imparting knowledge, a function machines increasingly perform, to developing the competencies, character, and curiosity that no machine can supply on a student’s behalf. The commentary, published as Volume 3, article 19 of Frontiers of Digital Education, is less a prediction of obsolescence than a blueprint for symbiosis, an argument that the future of teaching depends on deciding, deliberately and soon, what humans should keep for themselves.

Publication details underscore the commentary’s place in a rapidly consolidating research conversation. The article was received on 24 February 2026, revised on 6 March, accepted on 16 March, and published on 18 June 2026 as article 19 in Volume 3 of the journal, with 99 accesses recorded at the time of indexing. The author declares no competing interests and notes that no funding was received for the manuscript, and no datasets were generated or analyzed, consistent with its character as a theoretical and policy-oriented analysis rather than an empirical study.

The work also sits within a broader cluster of recent scholarship on AI and Chinese education. Springer lists related content including chapters on higher education with AI and technological innovation in China, on top-level design empowering AI as a strategic approach to educational transformation, and on content-analysis reviews of Chinese AI education policies dating back to 2021. This surrounding literature suggests that the questions Wang raises about restructuring and symbiosis are being examined in parallel by policy analysts and education researchers, giving the commentary’s four-dimension framework a ready audience of empirical studies to test and refine it.

The disciplinary keywords attached to the article, spanning the anthropology of education, the history of education, the logic of AI, intelligence augmentation, and the philosophy of artificial intelligence, signal its intended breadth. Rather than a technical contribution to machine learning, the piece is positioned as humanistic and theoretical groundwork, inviting historians and philosophers of education to treat AI-driven restructuring as the latest chapter in the long relationship between transmission technologies and the institutions built around them.

Subject of Research: AI-driven restructuring of the knowledge-imparting function of education and future human–AI educational symbiosis

Article Title: AI’s Restructuring of the Fundamental Knowledge-Imparting Function of Education

Article References: Wang, Q. (2026). AI’s Restructuring of the Fundamental Knowledge-Imparting Function of Education. Frontiers of Digital Education, 3(3), Article 19. https://doi.org/10.1007/s44366-026-0093-z

Image Credits: AI Generated

DOI: 10.1007/s44366-026-0093-z

Keywords: artificial intelligence, education, knowledge-imparting, human-AI symbiosis, smart education, future teachers, future classrooms, future schools, learning centers, competency development, digital education transformation, China education policy