Generative AI tool use enhances academic achievement in sustainable education through shared metacognition and cognitive offloading among preservice teachers
Iqbal, Javed; Hashmi, Zarqa Farooq; Asghar, Muhammad Zaheer; Abid, Muhammad Naseem (2025-05-13)
Springer
13.05.2025
Iqbal, J., Hashmi, Z.F., Asghar, M.Z. et al. Generative AI tool use enhances academic achievement in sustainable education through shared metacognition and cognitive offloading among preservice teachers. Sci Rep 15, 16610 (2025). https://doi.org/10.1038/s41598-025-01676-x
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https://creativecommons.org/licenses/by/4.0/
© The Author(s) 2025. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202505153452
https://urn.fi/URN:NBN:fi:oulu-202505153452
Tiivistelmä
Abstract
The integration of generative artificial intelligence tools in education has emerged as a transformative approach to enhancing learning outcomes, particularly in the context of sustainable development goals (SDG4). Therefore, the present study investigates the connection between generative artificial intelligence tool usage (GenAITU) and academic achievement (AA) in the context of SDG4. We assessed the mediating role of shared metacognition (SMC) and cognitive offloading (COL) in this relationship among preservice teachers (PSTs). The indicators, including performance expectancy (PE), effort expectancy (EE), facilitating conditions (FC), and use behavior (UB), are derived from adapting the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) for GenAITU. The authors surveyed 465 students from five universities in Wuhan, China, using a 7-point Likert scale through a time-lag design. Statistical analysis was performed through partial least squares structural equation modeling (PLS-SEM), to determine the relationship between variables. Findings indicated that two components of GenAITU, namely PE and UB, showed significant positive associations with AA, while the other two, EE and FC, did not show significant and positive relationships with AA. Results also showed that three dimensions of GenAITU, namely EE, FC, and UB have a positive and significant association with SMC while PE has a positive and significant connection with SMC. All four components of GenAITU like PE, EE, FC, and UB have positive and significant links with COL. SMC and COL have a positive and significant relationship with AA. Results also indicated that SMC mediated the connections between GenAITU (EE, FC, and UB) and AA. Outcomes also indicated that COL mediated the connections between GenAITU (PE, EE, FC, and UB) and AA. The current study shows that SMC and COL were strong mediators of the association between GenAITU and AA. The results of our study provide guidance to teachers, curriculum planners, and university management to successfully integrate GenAITU into the education for PSTs.
The integration of generative artificial intelligence tools in education has emerged as a transformative approach to enhancing learning outcomes, particularly in the context of sustainable development goals (SDG4). Therefore, the present study investigates the connection between generative artificial intelligence tool usage (GenAITU) and academic achievement (AA) in the context of SDG4. We assessed the mediating role of shared metacognition (SMC) and cognitive offloading (COL) in this relationship among preservice teachers (PSTs). The indicators, including performance expectancy (PE), effort expectancy (EE), facilitating conditions (FC), and use behavior (UB), are derived from adapting the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) for GenAITU. The authors surveyed 465 students from five universities in Wuhan, China, using a 7-point Likert scale through a time-lag design. Statistical analysis was performed through partial least squares structural equation modeling (PLS-SEM), to determine the relationship between variables. Findings indicated that two components of GenAITU, namely PE and UB, showed significant positive associations with AA, while the other two, EE and FC, did not show significant and positive relationships with AA. Results also showed that three dimensions of GenAITU, namely EE, FC, and UB have a positive and significant association with SMC while PE has a positive and significant connection with SMC. All four components of GenAITU like PE, EE, FC, and UB have positive and significant links with COL. SMC and COL have a positive and significant relationship with AA. Results also indicated that SMC mediated the connections between GenAITU (EE, FC, and UB) and AA. Outcomes also indicated that COL mediated the connections between GenAITU (PE, EE, FC, and UB) and AA. The current study shows that SMC and COL were strong mediators of the association between GenAITU and AA. The results of our study provide guidance to teachers, curriculum planners, and university management to successfully integrate GenAITU into the education for PSTs.
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