Factors influencing AI adoption by Chinese mathematics teachers in STEM education
Factors influencing AI adoption by Chinese mathematics teachers in STEM education
Wen Du,Yiming Cao,2 Authors,Guofeng Wang
TLDR
It is found that performance expectancy, hedonic motivation, and price value significantly affect teachers’ behavioural intention (BI) and teachers’ actual usage behaviour (UB), and price value emerges as a crucial factor influencing both BI and UB.
Abstract
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.