Frontiers in psychology · 2026
Professional knowledge complements general technology acceptance in mathematics teachers' critical behavioral intention toward AI.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Shu Chang, Peng Yangui, Wang Bo, Chen Hui, Bai Shengnan
Аннотация
INTRODUCTION: Artificial Intelligence (AI) is transforming education globally. In mathematics education, AI is being rapidly integrated, making teachers' critical behavioral intention (CBI) toward AI increasingly essential. Mathematics teachers' CBI refers to their ability to evaluate AI-generated outputs for mathematical accuracy and pedagogical appropriateness aligned with students' Zone of Proximal Development (ZPD). Current technology acceptance models rarely incorporate subject-specific factors. This study extends the Unified Theory of Acceptance and Use of Technology (UTAUT) model by adding two core dimensions of Mathematical Knowledge for Teaching (MKT), specifically Specialized Content Knowledge (SCK) and Knowledge of Content and Students (KCS), with perceived trust (PT) as a mediator.
METHODS: A survey was administered to 169 mathematics teachers and PLS-SEM was employed to test models.
RESULTS: In the baseline UTAUT model, both performance expectancy (PE) and effort expectancy (EE) significantly predicted CBI. In the full model (integrating SCK, KCS and PT), only PE remained a significant direct effect. KCS significantly influenced PT, which in turn predicted CBI; SCK showed no such effect. The full model explained 56.3% of the variance in CBI.
DISCUSSION: To better harness AI in mathematics teaching, teacher professional development should ensure solid SCK while placing greater emphasis on KCS, and AI system design should strengthen PT rather than merely prioritize usability and operational efficiency.
METHODS: A survey was administered to 169 mathematics teachers and PLS-SEM was employed to test models.
RESULTS: In the baseline UTAUT model, both performance expectancy (PE) and effort expectancy (EE) significantly predicted CBI. In the full model (integrating SCK, KCS and PT), only PE remained a significant direct effect. KCS significantly influenced PT, which in turn predicted CBI; SCK showed no such effect. The full model explained 56.3% of the variance in CBI.
DISCUSSION: To better harness AI in mathematics teaching, teacher professional development should ensure solid SCK while placing greater emphasis on KCS, and AI system design should strengthen PT rather than merely prioritize usability and operational efficiency.