Beijing Releases 2026 Blue Paper on AI in Basic Education as Educators Warn Against Outcome-First Learning
On October 29, at the 2026 Beijing seminar on AI-empowered basic education practice held at Beijing No. 5 Experimental School, the Beijing Blue Paper on AI Applications in Basic Education (2026) and two companion lists of typical and pilot application scenarios were released, jointly produced by the School of Artificial Intelligence at Beijing Normal University and the Beijing Digital Education Center.
The typical-scenario list covers six major areas, including homework management with "heuristic-guided" intelligent question answering, language learning, classroom teaching evaluation, and physical education and health. A separate list of eight pilot experimental scenarios includes multi-role educational agents collaborating on teaching research, AI-empowered interdisciplinary inquiry, and campus AI application ethics and academic integrity governance.
Huang Hua, dean of Beijing Normal University's School of Artificial Intelligence, said the blue paper analyzes both the opportunities of AI in basic education and its real-world challenges — including blurred human-machine boundaries, insufficient adaptation to teaching scenarios, data security and education equity — and recommends approaches that prioritize competency, coordinated planning, data-driven decision-making and safety and controllability.
The blueprint lands at a moment when AI tools are spreading rapidly through Chinese classrooms. Against that backdrop, Jiemodui's interview column Ercha spoke with Ding Haibin, a Beijing-based art-education entrepreneur, whose remarks capture a strand of practitioner unease that official documents also acknowledge. "Don't focus on the technology — pay attention to every present moment," Ding said.
Ding's core argument is that results cannot substitute for the process of reaching them: a painting can be generated before a child picks up a brush and a solution can appear before a child has finished thinking, but receiving an output is not the same as going through observation, selection and expression. He argued that school curricula, however polished, fail to answer two more basic questions — whether a child dares to draw and loves drawing. He also warned that teachers squeezed by quantified performance metrics end up "quantifying children just as brutally," narrowing students' choices.
Column host Zuo Xi framed the conversation with Western theory: Martin Heidegger's notion of technology as an "enframing" that renders children as measurable data, Ivan Illich's warning that institutions eventually redefine the very needs they were built to serve, and philosopher Bernard Stiegler's concept of "proletarianization" — the loss of knowledge and skills as technology extends human capability. All three echo debates now running through school systems worldwide about cognitive offloading and what students lose when answers arrive on demand.
The blue paper's inclusion of AI ethics and academic-integrity governance among its pilot scenarios suggests Beijing's regulators are alert to those risks. For Ding, the deeper question is what education is for when results can be had on demand — a question that AI adoption is forcing educators far beyond China to answer.
The typical-scenario list covers six major areas, including homework management with "heuristic-guided" intelligent question answering, language learning, classroom teaching evaluation, and physical education and health. A separate list of eight pilot experimental scenarios includes multi-role educational agents collaborating on teaching research, AI-empowered interdisciplinary inquiry, and campus AI application ethics and academic integrity governance.
Huang Hua, dean of Beijing Normal University's School of Artificial Intelligence, said the blue paper analyzes both the opportunities of AI in basic education and its real-world challenges — including blurred human-machine boundaries, insufficient adaptation to teaching scenarios, data security and education equity — and recommends approaches that prioritize competency, coordinated planning, data-driven decision-making and safety and controllability.
The blueprint lands at a moment when AI tools are spreading rapidly through Chinese classrooms. Against that backdrop, Jiemodui's interview column Ercha spoke with Ding Haibin, a Beijing-based art-education entrepreneur, whose remarks capture a strand of practitioner unease that official documents also acknowledge. "Don't focus on the technology — pay attention to every present moment," Ding said.
Ding's core argument is that results cannot substitute for the process of reaching them: a painting can be generated before a child picks up a brush and a solution can appear before a child has finished thinking, but receiving an output is not the same as going through observation, selection and expression. He argued that school curricula, however polished, fail to answer two more basic questions — whether a child dares to draw and loves drawing. He also warned that teachers squeezed by quantified performance metrics end up "quantifying children just as brutally," narrowing students' choices.
Column host Zuo Xi framed the conversation with Western theory: Martin Heidegger's notion of technology as an "enframing" that renders children as measurable data, Ivan Illich's warning that institutions eventually redefine the very needs they were built to serve, and philosopher Bernard Stiegler's concept of "proletarianization" — the loss of knowledge and skills as technology extends human capability. All three echo debates now running through school systems worldwide about cognitive offloading and what students lose when answers arrive on demand.
The blue paper's inclusion of AI ethics and academic-integrity governance among its pilot scenarios suggests Beijing's regulators are alert to those risks. For Ding, the deeper question is what education is for when results can be had on demand — a question that AI adoption is forcing educators far beyond China to answer.
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