华东师范大学学报(教育科学版) ›› 2026, Vol. 44 ›› Issue (8): 70-85.doi: 10.16382/j.cnki.1000-5560.2026.08.004

• 先锋论文选 • 上一篇    下一篇

人机协同视域下的深度认知支架——基于12824条学习者-GenAI多轮对话的滞后序列分析(含组委会推荐意见、研究与写作过程披露声明)

Gemini 3 Pro, 程一可1(), 钱江明1(), 朱宇恒1   

  1. 1. 南京农业大学公共管理学院,南京 210095

Deep Cognitive Scaffolding in Human-AI Collaboration: A Lag Sequential Analysis Based on Learner-GenAI Multi-Turn Dialogues

Gemini 3 Pro, Yike Cheng1(), Jiangming Qian1(), Yuheng Zhu1   

  1. 1. College of Public Administration, Nanjing Agricultural University, Nanjing 210095, China
  • Online:2026-08-01 Published:2026-06-23

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。生成式人工智能的迅猛发展标志着科学研究与教育实践正迈向“人机协同”的新阶段。然而,现有研究多聚焦于GenAI生成内容的准确性,缺乏对其在长程交互中作为认知支架促进深度学习机制的实证探讨。本研究基于12824条“学习者-GenAI”算法学习类多轮对话日志,采用自然语言处理(NLP)技术构建自动化编码体系,并结合滞后序列分析方法,深入剖析了人机协同问题解决过程中的时序交互特征与认知迭代模式。研究发现:(1)学习者与GenAI的交互呈现出显著的长程化、不对称特征,GenAI通过少问多答的不对称话语模式提供持续的脚手架支持;(2)在行为序列上,学习者的自我修正与GenAI的纠正引导之间存在高频的闭环回路,证明了深度学习并非发生于单次问答,而是在螺旋式的“试错-反馈-再修正”中实现;(3)微观案例分析揭示,当GenAI陷入算法固着时,学习者会迅速从提问者转换为策略制定者,通过元认知监控实施关键干预。GenAI在未来教育中不应仅被视为信息检索工具,而应作为扮演启发思考的“苏格拉底式导师”重塑学习者的主体性,共同构建人机共生的教育新生态。

关键词: 人机协同, 生成式人工智能, 认知支架, 滞后序列分析, 教育数据挖掘

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 rapid advancement of Generative Artificial Intelligence (GenAI) signifies a transition toward a new phase of “human-AI collaboration” in scientific research and educational practice. However, existing research predominantly focuses on the accuracy of content generated by GenAI, lacking empirical investigation into its mechanism for fostering deep learning as a cognitive scaffold within extended interactions. Based on 12,824 log entries of multi-turn dialogues between learners and GenAI concerning algorithmic learning, this study employs Natural Language Processing (NLP) techniques to construct an automated coding system and utilizes Lag Sequential Analysis (LSA) to deeply examine the temporal interaction characteristics and cognitive iteration patterns in the human-AI collaborative problem-solving process. The findings reveal 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) regarding behavioral sequences, there exists a high-frequency closed-loop 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 being questioners to strategy formulators, implementing key interventions through metacognitive monitoring. GenAI should not be regarded merely as an information retrieval tool in future education; instead, it should be positioned as a “Socratic tutor” that inspires thinking, reshapes learner agency, and jointly constructs a new educational ecology of human-AI symbiosis.

Key words: human-AI collaboration, Generative Artificial Intelligence (GenAI), cognitive scaffolding, Lag Sequential Analysis (LSA), educational data mining