How Generative <scp>AI</scp> Affects Team Communication in Secondary Education: A Qualitative Investigation

ABSTRACT Background The integration of Generative AI (GenAI) into collaborative learning in secondary education has created new possibilities for this pedagogical approach. It has also raised concerns about its impact on team communication, particularly, for adolescent learners whose collaborative competencies are still developing. Current research offers limited understanding of how GenAI shapes team communication in educational settings. Objectives This study explores how GenAI influences team communication during group tasks and identifies key factors shaping these dynamics in secondary education. Methods A mixed‐methods design was employed with 24 secondary students (Grades 9–12) from two Hong Kong secondary schools. Data collection included written reflections from all participants and semistructured interviews with 10 students. Thematic analysis addressed GenAI's impacts on communication frequency, timeliness and quality (RQ1), while activity theory guided analysis of influencing factors (RQ2). Results and Conclusions Findings underscore GenAI's dual role as both a facilitator and a potential disruptor of team interaction in secondary education. First, the introduction of GenAI tools improves communication quality by fostering deeper understanding, generating new ideas and resolving disagreements. However, generative AI tools also significantly reduce communication frequency among team members and may introduce delays in team exchanges. Moreover, this study identifies six factors influencing the use of GenAI in team communication as perceived by students, including the perceived value of discussions, task interdependence, team familiarity, attitudes towards AI, guidelines for AI use and role clarity.

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Publication Details

Journal
Journal of Computer Assisted Learning
Published
2026-05-27
DOI
https://doi.org/10.1002/jcal.70270
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
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article

How Generative AI Affects Team Communication in Secondary Education: A Qualitative Investigation

Wenjie Hu, Cecilia Ka Yuk Chan
Journal of Computer Assisted Learning
Artificial Intelligence in Healthcare and Education
article

How Generative AI Affects Team Communication in Secondary Education: A Qualitative Investigation

Wenjie Hu, Cecilia Ka Yuk Chan
article en

Abstract

ABSTRACT Background The integration of Generative AI (GenAI) into collaborative learning in secondary education has created new possibilities for this pedagogical approach. It has also raised concerns about its impact on team communication, particularly, for adolescent learners whose collaborative competencies are still developing. Current research offers limited understanding of how GenAI shapes team communication in educational settings. Objectives This study explores how GenAI influences team communication during group tasks and identifies key factors shaping these dynamics in secondary education. Methods A mixed‐methods design was employed with 24 secondary students (Grades 9–12) from two Hong Kong secondary schools. Data collection included written reflections from all participants and semistructured interviews with 10 students. Thematic analysis addressed GenAI's impacts on communication frequency, timeliness and quality (RQ1), while activity theory guided analysis of influencing factors (RQ2). Results and Conclusions Findings underscore GenAI's dual role as both a facilitator and a potential disruptor of team interaction in secondary education. First, the introduction of GenAI tools improves communication quality by fostering deeper understanding, generating new ideas and resolving disagreements. However, generative AI tools also significantly reduce communication frequency among team members and may introduce delays in team exchanges. Moreover, this study identifies six factors influencing the use of GenAI in team communication as perceived by students, including the perceived value of discussions, task interdependence, team familiarity, attitudes towards AI, guidelines for AI use and role clarity.

Journal of Computer Assisted LearningVol. 42(4)
Education University of Hong Kong (HK), University of Hong Kong (HK)
Quality Education
Openalex Percentile: Top 9%
Artificial Intelligence in Healthcare and Education
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