View in EDS PDF Full Text

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

Saved in:
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]
Copyright of Frontiers of Education in China is the property of Higher Education Press Limited Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
FullText Links:
  – Type: pdflink
Text:
  Availability: 0
Header DbId: ehh
DbLabel: Education Research Complete
An: 195105032
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: The Analysis of Interaction Modes in Human– Machine Collaborative Learning Supported by Generative Artificial Intelligence.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22SUN%2C+Yanyan%22">SUN, Yanyan</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22HUANG%2C+Yingfen%22">HUANG, Yingfen</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22WEN%2C+Sifan%22">WEN, Sifan</searchLink><relatesTo>1</relatesTo>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Frontiers+of+Education+in+China%22">Frontiers of Education in China</searchLink>. Jun2026, Vol. 21 Issue 2, p175-198. 24p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Generative+artificial+intelligence%22">Generative artificial intelligence</searchLink><br />*<searchLink fieldCode="DE" term="%22Instructional+systems+design%22">Instructional systems design</searchLink><br />*<searchLink fieldCode="DE" term="%22Collaborative+learning%22">Collaborative learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Educational+technology%22">Educational technology</searchLink><br />*<searchLink fieldCode="DE" term="%22Information-seeking+behavior%22">Information-seeking behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Human-artificial+intelligence+interaction%22">Human-artificial intelligence interaction</searchLink><br /><searchLink fieldCode="DE" term="%22Participation%22">Participation</searchLink><br /><searchLink fieldCode="DE" term="%22Social+interaction%22">Social interaction</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Frontiers of Education in China is the property of Higher Education Press Limited Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=195105032
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3868/s110-021-026-0009-6
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 24
        StartPage: 175
    Subjects:
      – SubjectFull: Generative artificial intelligence
        Type: general
      – SubjectFull: Instructional systems design
        Type: general
      – SubjectFull: Collaborative learning
        Type: general
      – SubjectFull: Educational technology
        Type: general
      – SubjectFull: Information-seeking behavior
        Type: general
      – SubjectFull: Human-artificial intelligence interaction
        Type: general
      – SubjectFull: Participation
        Type: general
      – SubjectFull: Social interaction
        Type: general
    Titles:
      – TitleFull: The Analysis of Interaction Modes in Human– Machine Collaborative Learning Supported by Generative Artificial Intelligence.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: SUN, Yanyan
      – PersonEntity:
          Name:
            NameFull: HUANG, Yingfen
      – PersonEntity:
          Name:
            NameFull: WEN, Sifan
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 1673341X
          Numbering:
            – Type: volume
              Value: 21
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: Frontiers of Education in China
              Type: main
ResultId 1