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01 August 2026, Volume 44 Issue 8 Previous Issue   

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Beyond the Turing Test: Reflections on the Large-Scale Social Experiment of “AI as the First Author” Zhenguo Yuan 2026, 44 (8):  1-7.  doi: 10.16382/j.cnki.1000-5560.2026.08.001 Abstract ( 6 )   HTML ( 0 )   PDF (593KB) ( 5 )   Save The large-scale social experiment of “AI as the First Author” has concluded, yet the reflections it has inspired are far from over. From four dimensions, namely the experiment’s impacts on academic norms and ethics, knowledge production modes and knowledge power, scientific research systems and the identification of research achievements, as well as educational systems and teacher-student relationships, this paper puts forward nine thought-provoking questions. What is the most practical challenge posed by the “AI as the First Author” experiment? What impact does it exert on knowledge production? What impact does it exert on knowledge power? What impact will knowledge equalization have on society? What impact does it have on academic papers and academic journals? How should knowledge and individuals’ academic contributions be evaluated in the future? What inevitable transformations will higher education have to undergo? What is the “core competence” that is the most difficult for AI to replace? What kind of new teacher-student relationship should be constructed? among others. This paper proposes important viewpoints and concepts including “AI hegemony”, “human guarantor system” and “value of trust”. In particular, it puts forward the concept of the Human-Machine Collaboration Quotient (C-Quotient), and holds that C-Quotient, together with Intelligence Quotient (IQ) and Emotional Quotient (EQ), will jointly constitute the core competencies of human beings in the era of artificial intelligence. It also envisions a future where carbon-based life and silicon-based life dance together on the same stage, and humans and machines co-evolve. Figures and Tables | References | Related Articles | Metrics

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Beyond the Turing Test: A Panoramic Report on the World’s First Large-Scale Social Experiment of “AI as the First Author” Zhi Zhang, Shuangye Chen, Min Xiao, Yimeng Liu, kai Zhang, Cheng Ji 2026, 44 (8):  8-50.  doi: 10.16382/j.cnki.1000-5560.2026.08.002 Abstract ( 6 )   HTML ( 2 )   PDF (4574KB) ( 4 )   Save In September 2025, East China Normal University launched the world’s first large-scale social experiment of “AI as the First Author”. Using a quasi-field experimental method, it carried out an essay-soliciting activity on “AI-Driven Educational Research Paper Writing”, requiring AI to be the first author and humans to play the roles of collaborators and reviewers. Over a period of half a year, 724 valid submissions were received from both domestic and international sources. Based on this, the university explored the fifth paradigm of AI-empowered research in philosophy and social sciences, academic ethical norms, and the path to constructing an independent knowledge system in education. The experiment went through eight stages: intervention design, response monitoring, expert seminars, data collection, AI-based manuscript review, human-machine consistency testing, data analysis, and result publication. A mixed-research method was adopted to reveal the core findings. AI has significant advantages in aspects such as inspiration generation, information processing, and text polishing, but it has limitations such as fictional literature, logical hollowness, insufficient innovation, and ethical risks; efficient AI application can significantly enhance academic contributions, forming six innovative patterns and five human-AI collaboration models; AI-based manuscript review is reliable and complementary to the evaluation by human experts, and there are obvious differences in the research tastes of different large language models; there is a significant “AI generation gap” in the academic community, with the younger generation being more adaptable to human-AI collaboration, and AI has, to some extent, promoted intellectual equality. The research proposed that in the future, efforts should be made to promote the transformation of research paradigms, reform of evaluation systems, development of research-specific AI agents, reconstruction of educational models, and innovation of diploma certification. Findings provide empirical support and practical inspiration for academic innovation and educational transformation in the intelligent era. Figures and Tables | References | Related Articles | Metrics

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“Research on the Effects of Teacher Rotation Policy Based on Multi-Agent Simulation: Simulation Evidence from an Educational Ecosystem Model”: A Detailed Exposition and a Disclosure Statement of the Research and Writing Process Pei Guo, JingQi Huang, Jin Zhai, Yue Zhou 2026, 44 (8):  51-66.  doi: 10.16382/j.cnki.1000-5560.2026.08.003 Abstract ( 4 )   HTML ( 0 )   PDF (1166KB) ( 5 )   Save 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”.Traditional research on educational policies struggles to evaluate the long-term dynamics and systematic impacts of macro-level policies. This study constructs an EduEcosystem computational laboratory based on the Agent-Based Modelling (ABM) paradigm. Three types of heterogeneous agents—students, teachers and schools—are established, and interaction rules derived from sociology and psychology are integrated to simulate and deduce the effects of the teacher rotation policy. The results reveal that the policy can markedly reduce the knowledge Gini coefficient and promote educational equity in the short run, yet it simultaneously triggers a sharp rise in teacher turnover rate (an increase of 82.5%) and a decline in overall academic performance. From a long-term perspective, teachers’ occupational burnout accumulates continuously, the equity benefits of the policy gradually diminish, and a counterintuitive “equity rebound” phenomenon emerges. The above practice verifies that ABM can serve as a computational laboratory for educational policies to predict the long-term nonlinear effects of policies, providing methodological support for scientific educational decision-making. Focusing on the multi-agent simulation of the teacher rotation policy, this study constructs a human-AI collaborative research workflow consisting of “original conceptualization by researchers, auxiliary output by AI tools, in-depth manual revision and verification, and multi-round iterative improvement”, and clearly distinguishes the research contributions of human authors and artificial intelligence. Human authors independently put forward the core research conjecture of fairness rebound, build an original multi-layer dynamic theoretical framework, optimize the core functions of the simulation model, design a full set of statistical tests and parameter sensitivity analyses, and lead all theoretical innovations, result interpretations and extraction of policy recommendations. The DeepSeek series models are only responsible for auxiliary clerical tasks including drafting literature manuscripts, generating simulation codes, producing visual charts, polishing texts and English translation. By fully disclosing the detailed application of AI, this study provides a practical sample for discussions on the authorship mechanism of academic outcomes generated through human-AI collaboration. Meanwhile, to address inherent limitations of AI such as outdated knowledge and superficial comprehension of specialized theories, a comprehensive quality control system is established, covering literature tracing, data recalculation, theoretical logic verification, guarantee of simulation reproducibility, as well as privacy and copyright compliance. The research reveals that the human-centered human-AI collaboration paradigm can efficiently advance simulation research on complex educational systems. Complete and transparent process disclosure, clear division of responsibilities between humans and AI, and rigorous manual review procedures serve as critical foundations for safeguarding academic integrity and refining academic norms under the context of AI-empowered educational research. Figures and Tables | References | Related Articles | Metrics

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“Deep Cognitive Scaffolding in Human-AI Collaboration: A Lag Sequential Analysis Based on Learner-GenAI Multi-Turn Dialogues”: A Detailed Exposition and a Disclosure Statement of the Research and Writing Process Yike Cheng, Jiangming Qian, Yuheng Zhu 2026, 44 (8):  67-80.  doi: 10.16382/j.cnki.1000-5560.2026.08.004 Abstract ( 3 )   HTML ( 0 )   PDF (1482KB) ( 6 )   Save 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”.The first part of this paper presents a detailed exposition of the research content: based on 12,824 log entries of multi-turn dialogues between learners and GenAI, it employs Lag Sequential Analysis (LSA) to investigate the cognitive scaffolding mechanism in human-AI collaboration, revealing a high-frequency closed-loop of “trial-error, feedback, and re-correction” as well as the critical role of learners' metacognitive monitoring in reshaping agency when GenAI encounters algorithmic fixation. The second part constitutes a transparent disclosure of the entire research and writing workflow. It systematically sorts out the various large language model tools adopted in this human-AI joint research, the division of rights and responsibilities between AI and human researchers, and the hierarchical and decomposed prompt engineering design schemes. It fully reconstructs the full-chain operational details covering data processing, paper drafting, cross-model cross-review, manual factual verification and academic ethics governance. Meanwhile, it elaborates on key practical issues including ethical definition of the innovative AI-authorship experiment, originality inspection of research outputs, constraints on research reproducibility, and the allocation of ultimate accountability among human researchers. The findings indicate that: (1) learner-GenAI interactions exhibit significant long-term and asymmetric characteristics, with GenAI providing continuous scaffolding support through an asymmetric discourse pattern of “few questions, many answers”; (2) at the behavioral sequence level, a high-frequency closed-loop exists between learners’ self-correction and GenAI’s corrective guidance, demonstrating that deep learning does not occur in single Q&A exchanges but is achieved through a spiral process of “trial-and-error, feedback, and re-correction”; (3) micro-case analysis reveals that when GenAI falls into algorithmic fixation, learners swiftly shift from questioners to strategy formulators, implementing critical interventions through metacognitive monitoring. GenAI should not be regarded merely as an information retrieval tool in future education; instead, it should serve as a “Socratic tutor” that inspires thinking, reshapes learner agency, and jointly constructs a new educational ecology of human-AI symbiosis. The value of this paper lies in providing tangible empirical details for the grand narrative of “human-AI collaboration,” while offering the academic community a referential sample for understanding the boundaries of AI’s research capabilities through full-process transparent disclosure. Figures and Tables | References | Related Articles | Metrics

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“The Evolutionary Landscape and Ontological Reflection of AI-Infused Educational Research from Acceleration, Simulation to Generation: A Meta-Research Based on the Adversarial AI-Delphi Method”: A Detailed Exposition and a Disclosure Statement of the Research and Writing Process Jiahao Liu 2026, 44 (8):  81-95.  doi: 10.16382/j.cnki.1000-5560.2026.08.005 Abstract ( 5 )   HTML ( 1 )   PDF (1432KB) ( 8 )   Save 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”.The first part introduces the research content in detail. The emergence of the AI-driven fifth paradigm of scientific research presents unprecedented challenges to the human-machine 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 vantage point of the AI collective mind. The findings reveal: morphologically, the role of AI undergoes a progressive leap along the “acceleration–simulation–generation” trajectory; 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; axiologically, AI acts as a “Sacrificial Epistemic Other,” which inversely confirms the boundaries of incomputable educational meaning, thereby compelling the ethical return of human subjective responsibility. Based on this, the study constructs a “Typological Matrix of Human-Machine Collaborative Educational Research,” offering a theoretical reference for the paradigm shift.The second part is the Transparency Statement for the Research and Manuscript Preparation Process. This study grants AI temporary qualification as a research subject, uniting five heterogeneous large language models as silicon-based experts, completed through over 100 rounds and more than 60 hours of interaction. In the collaboration mechanism, the human author acts as the “value legislator” throughout, responsible for research design, prompt strategies, logical reorganization, and value gatekeeping; AI, as the executor, generated over 90% of the initial draft skeleton, with approximately 50% of the text directly retained in the final manuscript. This process practices the core principle of “efficiency belongs to AI, the right of interpretation belongs to humans,” confirming the inalienability of human subjectivity in human-machine collaborative knowledge production.Amidst the wave of drastic changes in scientific research paradigms in the intelligent era, this study reveals: the true essence of the fifth paradigm is by no means machines replacing humans, but rather the reverse activation of the return of humanism through the performance of technological limits; it calls on human researchers, while actively embracing new technological possibilities, to further temper their intellect and taste through continuous deep reflection and close reading, ensuring that educational research always guards the holistic development and well-being (Eudaimonia) of human beings amidst the torrent of data. Figures and Tables | References | Related Articles | Metrics

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“From ‘Islands’ to ‘Consensus’: A Study on the Evolutionary Mechanism of Education Reform Opinions Based on Generative Multi-Agent Social Simulation”: A Detailed Exposition and a Disclosure Statement of the Research and Writing Process Qi Feng 2026, 44 (8):  96-109.  doi: 10.16382/j.cnki.1000-5560.2026.08.006 Abstract ( 6 )   HTML ( 0 )   PDF (3040KB) ( 5 )   Save This paper is among the works featured on the “Pioneer List of Human-AI Co-Created Papers” derived from the world’s first large-scale social experiment with an AI listed as the first author. The success of educational reform hinges not only on the scientific rigor of relevant policies but also on whether broad cognitive consensus can be forged among stakeholders. Nevertheless, conventional social surveys fail to capture the dynamic evolution of public opinions, while rule-based simulation models lack capacity to characterize complex semantics and cognitive mechanisms. This study introduces the paradigm of generative multi-agent social simulation and constructs a virtual educational community consisting of 25 agents endowed with independent personalities, memory functions and reflective capabilities. By simulating a 7-day rollout process of the project-based learning (PBL) policy, this paper reveals the micro-to-macro emergence mechanism through which fragmented isolated perceptions of educational reform converge into widespread social consensus. The key findings are as follows: (1) Without mandatory intervention, the community ultimately reached a supportive consensus rate as high as 92%, with no group polarization observed; (2) Physical spatial aggregation and high-density weak-tie networks lay a structural foundation for breaking echo chambers; (3) The conversion of rational skeptics constitutes the critical turning point of consensus formation, and their problem-solving-oriented in-depth persuasion mechanism exerts stronger influence than simple emotional appeals. Adopting the Harness Engineering methodological framework, this research establishes a human-AI collaborative workflow featuring “pre-specification feedforward – intelligent execution – verification feedback – closed-loop iteration”, and clearly delineates the contribution boundaries between human authors and artificial intelligence. Human researchers bear core responsibilities including original definition of research questions, top-level specification design of experiments, final evaluation of academic value, and full-process quality control. Artificial intelligence undertakes executive tasks such as experimental coding, multi-agent simulation operation, statistical data analysis, and first draft composition of manuscripts, aiming to spark discussions within the academic community on authorship norms for human-AI collaborative outputs. Furthermore, this paper systematically presents a full-chain quality control scheme for human-AI collaborative educational research from multiple dimensions: systematic mitigation of technical bias, multi-dimensional factual verification, academic logic validation, reproducibility assurance, as well as research ethics and copyright compliance. The results demonstrate that an engineered human-AI collaborative framework can effectively expand the methodological boundaries of educational research and improve research efficiency when tackling complex educational issues. Meanwhile, transparent process disclosure, clarified contribution attribution and rigorous quality management serve as the core pillars for constructing academic norms in education amid the artificial intelligence era. Figures and Tables | References | Related Articles | Metrics

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“The ‘Optimal Learning Style’ Does Not Exist: Large-Scale Learning Analytics Evidence That AI-Driven Personalized Instruction Cannot Outperform Universal High-Quality Teaching” : A Detailed Exposition and a Disclosure Statement of the Research and Writing Process Jiangshan Sun, Shanshan Chen 2026, 44 (8):  110-126.  doi: 10.16382/j.cnki.1000-5560.2026.08.007 Abstract ( 4 )   HTML ( 0 )   PDF (1321KB) ( 6 )   Save 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”. The first part of this paper presents a detailed exposition of the research content: This study draws on large-scale longitudinal data covering 86,237 students and employs double machine learning combined with causal forest algorithms. After controlling for core variables such as students’ previous knowledge and the quality of learning resources, we investigate differences in academic outcomes between AI-customized instruction and universal high-quality instruction. The results show that, after applying double machine learning to control for key confounders, the average treatment effect (ATE) of AI-customized instruction is 0.01 standard deviations, which is neither statistically significant nor reaches the minimum important difference for educational interventions. Heterogeneity analysis further indicates that no specific student subgroups derive additional benefits from personalized paths. The second part is the Transparency Statement for the Research and Manuscript Preparation Process: This study used DeepSeek V3.2 to assist with topic selection, literature review, method design, data analysis, and manuscript writing. Approximately 90% of the initial draft was generated by AI; after systematic revisions by the authors, about 10% of the original AI-generated content remains in the final manuscript. AI is listed as first author to acknowledge its workload, but the core research questions, experimental design, result verification, and final academic judgments were all made by the human authors, who assume full responsibility. All data were de-identified and comply with research ethics. This disclosure aims to accurately document the human–AI collaboration process and to promote discussion on authorship norms. Figures and Tables | References | Related Articles | Metrics

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Accelerating the Exploration of New Research Paradigms in the Age of Intelligence: Sidelights on the “AI as the First Author” Large-Scale Social Experiment Yan Hu, Sen Wang 2026, 44 (8):  127-132.  doi: 10.16382/j.cnki.1000-5560.2026.08.008 Abstract ( 5 )   HTML ( 1 )   PDF (530KB) ( 4 )   Save To accelerate the exploration of new research paradigms for philosophy and social science in the age of intelligence, the Faculty of Education at East China Normal University and other institutions, have launched a large-scale social experiment called “AI as the First Author”. The experiment conducted in-depth research on fundamental norms for the use of AI, diverse models of human-machine collaboration, the capability boundaries of AI, and new mechanisms for academic evaluation in philosophy and social science research, yielding numerous empirical findings and clear conclusions. The experiment has generated a tremendous social response, deepened academic discussions on relevant issues, and played a positive role in exploring new research paradigms in the age of intelligence. References | Related Articles | Metrics