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Leveraging Network Analytics to Examine the Impact of Generative Artificial Intelligence-Assisted Feedback on Inquiry-Based Discussion

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Title: Leveraging Network Analytics to Examine the Impact of Generative Artificial Intelligence-Assisted Feedback on Inquiry-Based Discussion
Language: English
Authors: Shen Ba (ORCID 0000-0001-6535-8335), Guoqing Lu, Norman Biliwang Mendoza (ORCID 0000-0003-0344-0709), Yin Yang, Zilong Pan (ORCID 0000-0001-7641-0362), Yu Wang
Source: Journal of Educational Computing Research. 2026 64(2):403-438.
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 36
Publication Date: 2026
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Network Analysis, Artificial Intelligence, Technology Uses in Education, Feedback (Response), Inquiry, Discussion, Preservice Teachers, Preservice Teacher Education, Foreign Countries, Self Efficacy, Computer Mediated Communication, Performance Factors, Thinking Skills
Geographic Terms: China
DOI: 10.1177/07356331251396357
ISSN: 0735-6331
1541-4140
Abstract: In inquiry-based discussion (IBD), it is essential to provide participants with effective feedback to promote engagement in knowledge construction and enhance the development of higher-order thinking. However, university instructors often struggle with monitoring multiple groups and delivering prompt and personalized feedback. Generative artificial intelligence (GAI), which can analyze text data and generate humanlike responses, offers potential solutions to mitigate these challenges. This study investigates the influence of GAI-assisted feedback on the IBD processes of pre-service teachers. A quasi-experiment was conducted with two classes (experimental: n = 53; control: n = 55) at a Chinese university. Epistemic network analysis was employed to model group IBD processes and compare groups with different characteristics (e.g., with/without GAI-assisted feedback, high/low engagement, high/low performance). Results show that GAI-assisted feedback significantly altered IBD dynamics. Collaboration self-efficacy was crucial for distinguishing group interaction patterns with the GAI chatbot. Moreover, groups in the experimental condition with high or low learning performance, engagement, and cognitive load showed diverse IBD interaction patterns. For example, groups with higher performance relied heavily on the GAI chatbot for idea generation without significant improvements in higher-order thinking. This study contributes detailed, process-oriented insights and implications on the adoption of GAI tools in IBD contexts.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1496970
Database: ERIC
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  Data: Leveraging Network Analytics to Examine the Impact of Generative Artificial Intelligence-Assisted Feedback on Inquiry-Based Discussion
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  Data: <searchLink fieldCode="AR" term="%22Shen+Ba%22">Shen Ba</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-6535-8335">0000-0001-6535-8335</externalLink>)<br /><searchLink fieldCode="AR" term="%22Guoqing+Lu%22">Guoqing Lu</searchLink><br /><searchLink fieldCode="AR" term="%22Norman+Biliwang+Mendoza%22">Norman Biliwang Mendoza</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-0344-0709">0000-0003-0344-0709</externalLink>)<br /><searchLink fieldCode="AR" term="%22Yin+Yang%22">Yin Yang</searchLink><br /><searchLink fieldCode="AR" term="%22Zilong+Pan%22">Zilong Pan</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-7641-0362">0000-0001-7641-0362</externalLink>)<br /><searchLink fieldCode="AR" term="%22Yu+Wang%22">Yu Wang</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Educational+Computing+Research%22"><i>Journal of Educational Computing Research</i></searchLink>. 2026 64(2):403-438.
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  Data: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
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  Data: 36
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  Data: <searchLink fieldCode="DE" term="%22Network+Analysis%22">Network Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Uses+in+Education%22">Technology Uses in Education</searchLink><br /><searchLink fieldCode="DE" term="%22Feedback+%28Response%29%22">Feedback (Response)</searchLink><br /><searchLink fieldCode="DE" term="%22Inquiry%22">Inquiry</searchLink><br /><searchLink fieldCode="DE" term="%22Discussion%22">Discussion</searchLink><br /><searchLink fieldCode="DE" term="%22Preservice+Teachers%22">Preservice Teachers</searchLink><br /><searchLink fieldCode="DE" term="%22Preservice+Teacher+Education%22">Preservice Teacher Education</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Self+Efficacy%22">Self Efficacy</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Mediated+Communication%22">Computer Mediated Communication</searchLink><br /><searchLink fieldCode="DE" term="%22Performance+Factors%22">Performance Factors</searchLink><br /><searchLink fieldCode="DE" term="%22Thinking+Skills%22">Thinking Skills</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink>
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  Data: 10.1177/07356331251396357
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  Data: 0735-6331<br />1541-4140
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  Data: In inquiry-based discussion (IBD), it is essential to provide participants with effective feedback to promote engagement in knowledge construction and enhance the development of higher-order thinking. However, university instructors often struggle with monitoring multiple groups and delivering prompt and personalized feedback. Generative artificial intelligence (GAI), which can analyze text data and generate humanlike responses, offers potential solutions to mitigate these challenges. This study investigates the influence of GAI-assisted feedback on the IBD processes of pre-service teachers. A quasi-experiment was conducted with two classes (experimental: n = 53; control: n = 55) at a Chinese university. Epistemic network analysis was employed to model group IBD processes and compare groups with different characteristics (e.g., with/without GAI-assisted feedback, high/low engagement, high/low performance). Results show that GAI-assisted feedback significantly altered IBD dynamics. Collaboration self-efficacy was crucial for distinguishing group interaction patterns with the GAI chatbot. Moreover, groups in the experimental condition with high or low learning performance, engagement, and cognitive load showed diverse IBD interaction patterns. For example, groups with higher performance relied heavily on the GAI chatbot for idea generation without significant improvements in higher-order thinking. This study contributes detailed, process-oriented insights and implications on the adoption of GAI tools in IBD contexts.
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  Data: 2026
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        Value: 10.1177/07356331251396357
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      – SubjectFull: Artificial Intelligence
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      – SubjectFull: Technology Uses in Education
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      – SubjectFull: Feedback (Response)
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      – SubjectFull: China
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