AI-technological Pedagogical Content Knowledge Framework through Structural Equation Modeling and Wearable Sensor Indicators for English Language Educators

We investigated the AI-related technological pedagogical content knowledge (AI-TPACK) of 232 primary and secondary school English teachers in China to understand the effects of AI integration into education on the teachers' professional competencies.In a mixed method, a questionnaire survey was conducted with biometric data collected from wearable sensors from a subset of ten participants who simulated teaching tasks.Descriptive statistics indicate that while teachers possessed robust traditional knowledge, they reported lower proficiency in AI-specific integration knowledge.Structural equation modeling results validated the effectiveness of the AI-TPACK framework to enhance pedagogical knowledge and its effect on AI-TPACK competency, mediated through AI-integrated knowledge.The results provide a reference for the development of sensor technology for objective, real-time physiological monitoring to enhance instructional design.Teachers with higher AI-TPACK proficiency exhibited significantly lower physiological stress, characterized by reduced heart rate variability and skin conductance levels due to lower cognitive load.These results underscore the role of pedagogical AI technology literacy and demonstrate the necessity of wearable sensors for effectively assessing teacher selfefficacy and technological readiness in the age of intelligent education.