Du Wen, Cao Yiming, Tang Muwen, Wang Fang, Wang Guofeng
School of Mathematical Sciences, Beijing Normal University, Beijing, China.
National Research Institute for Mathematics Teaching Materials, Beijing, China.
Sci Rep. 2025 Jul 1;15(1):20429. doi: 10.1038/s41598-025-06476-x.
This study refines the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) to explore the factors influencing the adoption and utilization of artificial intelligence (AI) by Chinese mathematics teachers in STEM education, aiming to promote broader integration of AI within this domain. Utilising structural equation modelling (SEM) on survey data collected from 503 in-service mathematics teachers across China, the findings indicate that performance expectancy (PE), hedonic motivation (HM), and price value (PV) significantly affect teachers' behavioural intention (BI). Moreover, the study finds that effort expectancy (EE), facilitating conditions (FC), and price value (PV) significantly influence teachers' actual usage behaviour (UB). Notably, price value emerges as a crucial factor influencing both BI and UB, underscoring the importance teachers place on balancing the benefits of AI teaching tools with the time investment required for their adoption.
本研究完善了技术接受与使用扩展统一理论（UTAUT2），以探讨影响中国数学教师在STEM教育中采用和使用人工智能（AI）的因素，旨在促进AI在该领域更广泛的整合。利用从中国503名在职数学教师收集的调查数据进行结构方程建模（SEM），研究结果表明，绩效期望（PE）、享乐动机（HM）和价格价值（PV）显著影响教师的行为意向（BI）。此外，研究发现努力期望（EE）、促进条件（FC）和价格价值（PV）显著影响教师的实际使用行为（UB）。值得注意的是，价格价值是影响BI和UB的关键因素，突出了教师在平衡AI教学工具的益处与采用所需的时间投入方面的重视程度。
