How generative artificial intelligence supported reflective strategies promote middle school students' conceptual knowledge learning: an empirical study from China.
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| Title: | How generative artificial intelligence supported reflective strategies promote middle school students' conceptual knowledge learning: an empirical study from China. |
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| Authors: | Yang, Gang1 (AUTHOR) hnyg7906@163.com, Rong, Yu-Die1 (AUTHOR), Wang, Ya-Li1 (AUTHOR), Zhang, You-You1 (AUTHOR), Yan, Jun-Jie1 (AUTHOR), Tu, Yun-Fang1 (AUTHOR) |
| Source: | Interactive Learning Environments. May2026, Vol. 34 Issue 3, p1298-1323. 26p. |
| Subjects: | Generative artificial intelligence, Concept learning, Critical thinking, Teaching methods, Middle school education, Information science, Cognitive structures, Student engagement |
| Geographic Terms: | China |
| Abstract: | Integrating generative artificial intelligence into the reflection process can promote the learning of conceptual knowledge. Therefore, this study proposed Generative Artificial Intelligence-Supported Reflective Strategies (GAI-RS) and explored its impact on students' conceptual knowledge learning. The participants were 83 middle school seventh-grade students (42 in the experimental group and 41 in the control group). The experimental group used Generative Artificial Intelligence-Supported Reflective Strategies (GAI-RS), and the control group used Traditional Teacher-Supported Reflective Strategies (TT-RS). The results showed that GAI-RS promoted students' conceptual knowledge learning, and the students in the experimental group had higher levels of reflective thinking and learning engagement. In addition, epistemic network analysis (ENA) revealed that the strategy was beneficial in optimizing students' cognitive network structure. All students in the experimental group showed high learning satisfaction with this strategy. In short, GAI-RS has significant effectiveness in promoting students' conceptual knowledge learning, reflective thinking and learning engagement, and provides new inspiration for research on instructional practices for integrating generative artificial intelligence in information science and technology courses. [ABSTRACT FROM AUTHOR] |
| Copyright of Interactive Learning Environments is the property of Taylor & Francis Ltd 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: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 193489930 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: How generative artificial intelligence supported reflective strategies promote middle school students' conceptual knowledge learning: an empirical study from China. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yang%2C+Gang%22">Yang, Gang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> hnyg7906@163.com</i><br /><searchLink fieldCode="AR" term="%22Rong%2C+Yu-Die%22">Rong, Yu-Die</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Ya-Li%22">Wang, Ya-Li</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+You-You%22">Zhang, You-You</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yan%2C+Jun-Jie%22">Yan, Jun-Jie</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tu%2C+Yun-Fang%22">Tu, Yun-Fang</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Interactive+Learning+Environments%22">Interactive Learning Environments</searchLink>. May2026, Vol. 34 Issue 3, p1298-1323. 26p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Generative+artificial+intelligence%22">Generative artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Concept+learning%22">Concept learning</searchLink><br /><searchLink fieldCode="DE" term="%22Critical+thinking%22">Critical thinking</searchLink><br /><searchLink fieldCode="DE" term="%22Teaching+methods%22">Teaching methods</searchLink><br /><searchLink fieldCode="DE" term="%22Middle+school+education%22">Middle school education</searchLink><br /><searchLink fieldCode="DE" term="%22Information+science%22">Information science</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+structures%22">Cognitive structures</searchLink><br /><searchLink fieldCode="DE" term="%22Student+engagement%22">Student engagement</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Integrating generative artificial intelligence into the reflection process can promote the learning of conceptual knowledge. Therefore, this study proposed Generative Artificial Intelligence-Supported Reflective Strategies (GAI-RS) and explored its impact on students' conceptual knowledge learning. The participants were 83 middle school seventh-grade students (42 in the experimental group and 41 in the control group). The experimental group used Generative Artificial Intelligence-Supported Reflective Strategies (GAI-RS), and the control group used Traditional Teacher-Supported Reflective Strategies (TT-RS). The results showed that GAI-RS promoted students' conceptual knowledge learning, and the students in the experimental group had higher levels of reflective thinking and learning engagement. In addition, epistemic network analysis (ENA) revealed that the strategy was beneficial in optimizing students' cognitive network structure. All students in the experimental group showed high learning satisfaction with this strategy. In short, GAI-RS has significant effectiveness in promoting students' conceptual knowledge learning, reflective thinking and learning engagement, and provides new inspiration for research on instructional practices for integrating generative artificial intelligence in information science and technology courses. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Interactive Learning Environments is the property of Taylor & Francis Ltd 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/10494820.2025.2521339 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 1298 Subjects: – SubjectFull: Generative artificial intelligence Type: general – SubjectFull: Concept learning Type: general – SubjectFull: Critical thinking Type: general – SubjectFull: Teaching methods Type: general – SubjectFull: Middle school education Type: general – SubjectFull: Information science Type: general – SubjectFull: Cognitive structures Type: general – SubjectFull: Student engagement Type: general – SubjectFull: China Type: general Titles: – TitleFull: How generative artificial intelligence supported reflective strategies promote middle school students' conceptual knowledge learning: an empirical study from China. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yang, Gang – PersonEntity: Name: NameFull: Rong, Yu-Die – PersonEntity: Name: NameFull: Wang, Ya-Li – PersonEntity: Name: NameFull: Zhang, You-You – PersonEntity: Name: NameFull: Yan, Jun-Jie – PersonEntity: Name: NameFull: Tu, Yun-Fang IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 10494820 Numbering: – Type: volume Value: 34 – Type: issue Value: 3 Titles: – TitleFull: Interactive Learning Environments Type: main |
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