华东师范大学学报(教育科学版)

华东师范大学学报(教育科学版) ›› 2026, Vol. 44 ›› Issue (8): 86-101.doi: 10.16382/j.cnki.1000-5560.2026.08.005

• 先锋论文选 • 上一篇    下一篇

从加速、模拟到生成：人工智能融入教育科研的演进图景与本体论省思——一项基于对抗性AI-Delphi法的元研究（含组委会推荐意见、研究与写作过程披露声明）

Gemini, 刘嘉豪1,2()   

  1. 1. 浙江大学教育学院，浙江杭州 310058
    2. 杭州师范大学经亨颐教育学院，浙江杭州 311121
  • 出版日期:2026-08-01 发布日期:2026-06-23
  • 作者简介:*刘嘉豪，本文共同一作、通信作者，工作邮箱：liujiahao@mail.bnu.edu.cn

From Acceleration, Simulation to Generation: The Evolutionary Landscape and Ontological Reflection of AI-Infused Educational Research: A Meta-Research Based on the Adversarial AI-Delphi Method (Including the Organizing Committee’s Recommendation and Transparency Statement for the Research and Manuscript Preparation Process)

Gemini, Jiahao Liu1,2()   

  1. 1. College of Education, Zhejiang University, Hangzhou 310058, China
    2. Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou 311121, China
  • Online:2026-08-01 Published:2026-06-23

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摘要：

本文系全球首个 “AI一作” 教育科研大型社会实验“人机共创先锋论文榜”论文之一。人工智能驱动科学研究第五范式的兴起，对教育科研中人机分工提出了全新挑战。该研究立足元研究视角，设计并执行了“对抗性AI-Delphi法”，构建异构大模型组成的“硅基专家组”，通过3阶段辩证推演，从AI集体心智的角度自下而上地探索了教育科研范式演化图景。研究发现：在形态上，AI的教育科研角色沿“加速—模拟—生成”路径跃迁，从数据洞察迈向自主研究；在隐忧上，AI介入教育科研存在“不可计算”维度的系统性遗忘、对教育“慢变量”的忽视以及学术创新的“平庸化”等结构性陷阱；在价值上，AI并非替代人类教育研究者，而是作为 “献祭性认知他者”，通过极致的形式化计算反向确证教育中不可计算的意义边界，并倒逼人类主体责任的伦理回归。基于可计算与不可计算的“共生辩证”，该研究构建了“教育科研中人机协同的类型学矩阵”，在可计算性与价值风险的张力中，为人工智能驱动教育科研范式变革提供兼具认识论深度与实践指向性的理论参照。

关键词: 人工智能驱动科研(AI4R), 对抗性AI-Delphi法, 演化图景, 人机协同, 科研范式

Abstract:

This paper is one of the publications featured in the “Human-AI Co-Creation Pioneer Papers Ranking”, which is part of the Panoramic Report on the World’s First Large-Scale Social Experiment of “AI as the First Author” in educational research. The emergence of the AI-driven fifth paradigm of scientific research presents unprecedented challenges to the cognitive division of labor in educational research. Grounded in a meta-research perspective, this study designs and executes an “Adversarial AI-Delphi Method” by constructing a “Silicon-based Expert Panel” comprising heterogeneous large language models (LLMs). Through a three-stage dialectical deduction, it maps the evolutionary landscape of educational research paradigms from the bottom up, taking the vantage point of the AI collective mind. The findings reveal a tripartite structure: (1) morphologically, the role of AI undergoes a progressive leap along the “acceleration–simulation–generation” trajectory, evolving from data insight to autonomous inquiry; (2) critically, AI-driven research is entangled in structural traps, including the systematic forgetting of “incomputable” dimensions, the neglect of educational “slow variables,” and the “banalization” of academic innovation; and (3) axiologically, rather than replacing human researchers, AI acts as a “Sacrificial Epistemic Other.” Through extreme formalized computation, it inversely confirms the boundaries of incomputable educational meaning, thereby compelling the ethical return of human subjective responsibility. Ultimately, within the symbiotic dialectic between the computable and the incomputable, this study constructs a “Typological Matrix of Human-Machine Collaborative Educational Research,” offering a theoretical coordinate—at once epistemologically deep and practically navigable—for the paradigm shift that navigates the tensions between computability and value risk.

Key words: AI for Research (AI4R), Adversarial AI-Delphi Method, evolutionary landscape, human-machine collaboration, research paradigm