Journal of East China Normal University(Educationa ›› 2026, Vol. 44 ›› Issue (8): 86-101.doi: 10.16382/j.cnki.1000-5560.2026.08.005

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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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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