Iqbal Javed, Hashmi Zarqa Farooq, Asghar Muhammad Zaheer, Abid Muhammad Naseem
School of English Studies, Zhejiang International Studies University, Hangzhou, People's Republic of China.
School of Education, Huazhong University of Science and Technology, Wuhan, People's Republic of China.
Sci Rep. 2025 May 13;15(1):16610. doi: 10.1038/s41598-025-01676-x.
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.
生成式人工智能工具在教育中的整合已成为提升学习成果的一种变革性方法，尤其是在可持续发展目标（SDG4）的背景下。因此，本研究在SDG4的背景下调查了生成式人工智能工具使用（GenAITU）与学业成绩（AA）之间的联系。我们评估了共享元认知（SMC）和认知卸载（COL）在职前教师（PSTs）这种关系中的中介作用。包括绩效期望（PE）、努力期望（EE）、促进条件（FC）和使用行为（UB）在内的指标，是通过对技术接受与使用统一理论2（UTAUT2）进行改编以适用于GenAITU而得出的。作者通过时间滞后设计，使用7点李克特量表对中国武汉五所大学的465名学生进行了调查。通过偏最小二乘结构方程模型（PLS - SEM）进行统计分析，以确定变量之间的关系。研究结果表明，GenAITU的两个组成部分，即PE和UB，与AA呈显著正相关，而另外两个部分，EE和FC，与AA没有显著正相关关系。结果还表明，GenAITU的三个维度，即EE、FC和UB与SMC呈正显著相关，而PE与SMC呈正显著相关。GenAITU的所有四个组成部分，如PE、EE、FC和UB，与COL都有正显著联系。SMC和COL与AA呈正显著关系。结果还表明，SMC介导了GenAITU（EE、FC和UB）与AA之间的联系。结果还表明，COL介导了GenAITU（PE、EE、FC和UB）与AA之间的联系。当前研究表明，SMC和COL是GenAITU与AA之间关联的强有力中介。我们的研究结果为教师、课程规划者和大学管理层成功将GenAITU整合到PSTs的教育中提供了指导。
