Bibliographic Details
| Title: |
How generative artificial intelligence supported reflective strategies promote middle school students' conceptual knowledge learning: an empirical study from China. |
| 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 |
