View in EDS HTML Full Text PDF Full Text

Human–AI collaborative learning in mixed reality: Examining the cognitive and socio‐emotional interactions.

Saved in:
Bibliographic Details
Title: Human–AI collaborative learning in mixed reality: Examining the cognitive and socio‐emotional interactions.
Authors: Dang, Belle1 (AUTHOR) belle.dang@oulu.fi, Huynh, Luna1 (AUTHOR), Gul, Faaiz1 (AUTHOR), Rosé, Carolyn2 (AUTHOR), Järvelä, Sanna1 (AUTHOR), Nguyen, Andy1 (AUTHOR)
Source: British Journal of Educational Technology. Sep2025, Vol. 56 Issue 5, p2078-2101. 24p.
Subject Terms: *Generative artificial intelligence, *Collaborative learning, *Artificial intelligence, *Higher education, Mixed reality, Mental work, Swarm intelligence, Social interaction
Abstract: The rise of generative artificial intelligence (GAI), especially with multimodal large language models like GPT‐4o, sparked transformative potential and challenges for learning and teaching. With potential as a cognitive offloading tool, GAI can enable learners to focus on higher‐order thinking and creativity. Yet, this also raises questions about integration into traditional education due to the limited research on learners' interactions with GAI. Some studies with GAI focus on text‐based human–AI interactions, while research on embodied GAI in immersive environments like mixed reality (MR) remains unexplored. To address this, this study investigates interaction dynamics between learners and embodied GAI agents in MR, examining cognitive and socio‐emotional interactions during collaborative learning. We investigated the paired interactive patterns between a student and an embodied GAI agent in MR, based on data from 26 higher education students with 1317 recorded activities. Data were analysed using a multi‐layered learning analytics approach, including quantitative content analysis, sequence analysis via hierarchical clustering and pattern analysis through ordered network analysis (ONA). Our findings identified two interaction patterns: type (1) AI‐led Supported Exploratory Questioning (AISQ) and type (2) Learner‐Initiated Inquiry (LII) group. Despite their distinction in characteristic, both types demonstrated comparable levels of socio‐emotional engagement and exhibited meaningful cognitive engagement, surpassing the superficial content reproduction that can be observed in interactions with GPT models. This study contributes to the human–AI collaboration and learning studies, extending understanding to learning in MR environments and highlighting implications for designing AI‐based educational tools. Practitioner notesWhat is already known about this topic Socio‐emotional interactions are fundamental to cognitive processes and play a critical role in collaborative learning.Generative artificial intelligence (GAI) holds transformative potential for education but raises questions about how learners interact with such technology.Most existing research focuses on text‐based interactions with GAI; there is limited empirical evidence on how embodied GAI agents within immersive environments like Mixed Reality (MR) influence the cognitive and socio‐emotional interactions for learning and regulation.What this paper adds Provides first empirical insights into cognitive and socio‐emotional interaction patterns between learners and embodied GAI agents in MR environments.Identifies two distinct interaction patterns: AISQ type (structured, guided, supportive) and LII type (inquiry‐driven, exploratory, engaging), demonstrating how these patterns influence collaborative learning dynamics.Shows that both interaction types facilitate meaningful cognitive engagement, moving beyond superficial content reproduction commonly associated with GAI interactions.Implications for practice and/or policy Insights from the identified interaction patterns can inform the design of teaching strategies that effectively integrate embodied GAI agents to enhance both cognitive and socio‐emotional engagement.Findings can guide the development of AI‐based educational tools that capitalise on the capabilities of embodied GAI agents, supporting a balance between structured guidance and exploratory learning.Highlights the need for ethical considerations in adopting embodied GAI agents, particularly regarding the human‐like realism of these agents and potential impacts on learner dependency and interaction norms. [ABSTRACT FROM AUTHOR]
Copyright of British Journal of Educational Technology is the property of Wiley-Blackwell 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
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: ehh
DbLabel: Education Research Complete
An: 187257461
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Human–AI collaborative learning in mixed reality: Examining the cognitive and socio‐emotional interactions.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Dang%2C+Belle%22">Dang, Belle</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> belle.dang@oulu.fi</i><br /><searchLink fieldCode="AR" term="%22Huynh%2C+Luna%22">Huynh, Luna</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gul%2C+Faaiz%22">Gul, Faaiz</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rosé%2C+Carolyn%22">Rosé, Carolyn</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Järvelä%2C+Sanna%22">Järvelä, Sanna</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Nguyen%2C+Andy%22">Nguyen, Andy</searchLink><relatesTo>1</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22British+Journal+of+Educational+Technology%22">British Journal of Educational Technology</searchLink>. Sep2025, Vol. 56 Issue 5, p2078-2101. 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="%22Collaborative+learning%22">Collaborative learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br />*<searchLink fieldCode="DE" term="%22Higher+education%22">Higher education</searchLink><br /><searchLink fieldCode="DE" term="%22Mixed+reality%22">Mixed reality</searchLink><br /><searchLink fieldCode="DE" term="%22Mental+work%22">Mental work</searchLink><br /><searchLink fieldCode="DE" term="%22Swarm+intelligence%22">Swarm intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Social+interaction%22">Social interaction</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The rise of generative artificial intelligence (GAI), especially with multimodal large language models like GPT‐4o, sparked transformative potential and challenges for learning and teaching. With potential as a cognitive offloading tool, GAI can enable learners to focus on higher‐order thinking and creativity. Yet, this also raises questions about integration into traditional education due to the limited research on learners' interactions with GAI. Some studies with GAI focus on text‐based human–AI interactions, while research on embodied GAI in immersive environments like mixed reality (MR) remains unexplored. To address this, this study investigates interaction dynamics between learners and embodied GAI agents in MR, examining cognitive and socio‐emotional interactions during collaborative learning. We investigated the paired interactive patterns between a student and an embodied GAI agent in MR, based on data from 26 higher education students with 1317 recorded activities. Data were analysed using a multi‐layered learning analytics approach, including quantitative content analysis, sequence analysis via hierarchical clustering and pattern analysis through ordered network analysis (ONA). Our findings identified two interaction patterns: type (1) AI‐led Supported Exploratory Questioning (AISQ) and type (2) Learner‐Initiated Inquiry (LII) group. Despite their distinction in characteristic, both types demonstrated comparable levels of socio‐emotional engagement and exhibited meaningful cognitive engagement, surpassing the superficial content reproduction that can be observed in interactions with GPT models. This study contributes to the human–AI collaboration and learning studies, extending understanding to learning in MR environments and highlighting implications for designing AI‐based educational tools. Practitioner notesWhat is already known about this topic Socio‐emotional interactions are fundamental to cognitive processes and play a critical role in collaborative learning.Generative artificial intelligence (GAI) holds transformative potential for education but raises questions about how learners interact with such technology.Most existing research focuses on text‐based interactions with GAI; there is limited empirical evidence on how embodied GAI agents within immersive environments like Mixed Reality (MR) influence the cognitive and socio‐emotional interactions for learning and regulation.What this paper adds Provides first empirical insights into cognitive and socio‐emotional interaction patterns between learners and embodied GAI agents in MR environments.Identifies two distinct interaction patterns: AISQ type (structured, guided, supportive) and LII type (inquiry‐driven, exploratory, engaging), demonstrating how these patterns influence collaborative learning dynamics.Shows that both interaction types facilitate meaningful cognitive engagement, moving beyond superficial content reproduction commonly associated with GAI interactions.Implications for practice and/or policy Insights from the identified interaction patterns can inform the design of teaching strategies that effectively integrate embodied GAI agents to enhance both cognitive and socio‐emotional engagement.Findings can guide the development of AI‐based educational tools that capitalise on the capabilities of embodied GAI agents, supporting a balance between structured guidance and exploratory learning.Highlights the need for ethical considerations in adopting embodied GAI agents, particularly regarding the human‐like realism of these agents and potential impacts on learner dependency and interaction norms. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of British Journal of Educational Technology is the property of Wiley-Blackwell 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=187257461
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1111/bjet.13607
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 24
        StartPage: 2078
    Subjects:
      – SubjectFull: Generative artificial intelligence
        Type: general
      – SubjectFull: Collaborative learning
        Type: general
      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Higher education
        Type: general
      – SubjectFull: Mixed reality
        Type: general
      – SubjectFull: Mental work
        Type: general
      – SubjectFull: Swarm intelligence
        Type: general
      – SubjectFull: Social interaction
        Type: general
    Titles:
      – TitleFull: Human–AI collaborative learning in mixed reality: Examining the cognitive and socio‐emotional interactions.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Dang, Belle
      – PersonEntity:
          Name:
            NameFull: Huynh, Luna
      – PersonEntity:
          Name:
            NameFull: Gul, Faaiz
      – PersonEntity:
          Name:
            NameFull: Rosé, Carolyn
      – PersonEntity:
          Name:
            NameFull: Järvelä, Sanna
      – PersonEntity:
          Name:
            NameFull: Nguyen, Andy
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 09
              Text: Sep2025
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 00071013
          Numbering:
            – Type: volume
              Value: 56
            – Type: issue
              Value: 5
          Titles:
            – TitleFull: British Journal of Educational Technology
              Type: main
ResultId 1