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

doi:10.16382/j.cnki.1000-5560.2026.08.004

Abstract

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

Gemini 3 Pro, Yike Cheng, Jiangming Qian, Yuheng Zhu. Deep Cognitive Scaffolding in Human-AI Collaboration: A Lag Sequential Analysis Based on Learner-GenAI Multi-Turn Dialogues[J]. Journal of East China Normal University(Educationa, 2026, 44(8): 70-85.

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交互主体	行为类别	编码	操作性定义	关键词示例	对话实例
学习者	探究提问	U_INQ	发起新的知识查询，寻求解释、定义或方法指导。	what, how, explain, why, help	“Explain what the each data is in your own words.”
	求证验证	U_VER	提交自己的理解或初步方案，请求AI确认正确性。	right? correct? true? check, intuition	“Does this dfs analysis look right?”
	修正迭代	U_REV	根据AI的反馈，提交修改后的代码或文本，寻求进一步评价。	better? revised, updated, version, try	“Is this better?”
	确认/致谢	U_ACK	简单的确认收到、表示理解或结束当前话题。	ok, thanks, got it, I see, understood	“Okay, pandas is imported now.”
人工智能	肯定反馈	A_AFF	明确确认学习者的输入是正确的，给予积极评价。	yes, correct, indeed, absolutely, right	“Yes, your latest revision is indeed clearer.”
	纠正引导	A_COR	指出学习者输入中的错误、误解或潜在问题，并提出相关建议。	however, mistake, instead, error, not quite	“It looks like you've attempted to install Pandas... which caused an error.”
	知识阐述	A_EXP	提供详细的概念解释、代码示例或步骤说明（通常作为默认回复）。	(长文本解释), to analyze, here is, first...	“Recursive DFS will just explore the first child...”