The Analysis of Interaction Modes in Human– Machine Collaborative Learning Supported by Generative Artificial Intelligence.

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Bibliographic Details
Title:	The Analysis of Interaction Modes in Human– Machine Collaborative Learning Supported by Generative Artificial Intelligence.
Authors:	SUN, Yanyan¹, HUANG, Yingfen¹, WEN, Sifan¹
Source:	Frontiers of Education in China. Jun2026, Vol. 21 Issue 2, p175-198. 24p.
Subject Terms:	Generative artificial intelligence, Instructional systems design, Collaborative learning, Educational technology, *Information-seeking behavior, Human-artificial intelligence interaction, Participation, Social interaction
Abstract:	Human–machine collaboration is a development trend in the future of education. Generative artificial intelligence (GenAI), represented by ChatGPT, has redefined the interaction modes in human–machine collaborative learning. However, existing research is deficient in a systematic discussion on the interaction modes between learning groups and GenAI. Based on the analysis of dialogue texts from human–machine collaborative learning supported by GenAI, three common types of questions asked by students to GenAI are application, analysis, and examples. In student–machine interaction, three primary types of interactive behaviors emerge: exploratory questioning, optimization questioning, and information integration, each of which shows a clear pattern of cognitive engagement shift. In student–student interaction, three major interactive behaviors emerge: task comprehension, theme discussion, and non-task communication, with atypical cognitive engagement shift patterns. Based on the co-occurrence of question types and human–machine interaction behaviors, three interaction modes are observed when the learning group collaborates with GenAI to complete learning tasks: the dominant-analytical mode, the integrative-thinking mode, and the answer-oriented mode. The dominant-analytical mode focuses on in-depth exploration and critical thinking, the integrative-thinking mode highlights the key role of in-group communication for integrating diverse information and reflective thinking, and the answer-oriented mode centers around human–machine interaction based on the information provided by GenAI. To enhance the effectiveness of human–machine collaborative learning supported by GenAI, it is necessary to promote high-cognitive learning with an emphasis on thinking skills and innovative awareness, foster human–machine dialogic learning centered on questioning, and adjust instructional design according to the demands of learning tasks. [ABSTRACT FROM AUTHOR]
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Database:	Education Research Complete

Description
Abstract:	Human–machine collaboration is a development trend in the future of education. Generative artificial intelligence (GenAI), represented by ChatGPT, has redefined the interaction modes in human–machine collaborative learning. However, existing research is deficient in a systematic discussion on the interaction modes between learning groups and GenAI. Based on the analysis of dialogue texts from human–machine collaborative learning supported by GenAI, three common types of questions asked by students to GenAI are application, analysis, and examples. In student–machine interaction, three primary types of interactive behaviors emerge: exploratory questioning, optimization questioning, and information integration, each of which shows a clear pattern of cognitive engagement shift. In student–student interaction, three major interactive behaviors emerge: task comprehension, theme discussion, and non-task communication, with atypical cognitive engagement shift patterns. Based on the co-occurrence of question types and human–machine interaction behaviors, three interaction modes are observed when the learning group collaborates with GenAI to complete learning tasks: the dominant-analytical mode, the integrative-thinking mode, and the answer-oriented mode. The dominant-analytical mode focuses on in-depth exploration and critical thinking, the integrative-thinking mode highlights the key role of in-group communication for integrating diverse information and reflective thinking, and the answer-oriented mode centers around human–machine interaction based on the information provided by GenAI. To enhance the effectiveness of human–machine collaborative learning supported by GenAI, it is necessary to promote high-cognitive learning with an emphasis on thinking skills and innovative awareness, foster human–machine dialogic learning centered on questioning, and adjust instructional design according to the demands of learning tasks. [ABSTRACT FROM AUTHOR]
ISSN:	1673341X
DOI:	10.3868/s110-021-026-0009-6