Exploring Prospective Teachers' Intentions for Artificial Intelligence Integration in Education: The Role of Motivation

This project examines the motivational factors shaping prospective teachers’ intentions to integrate artificial intelligence (AI) into education. Drawing on Expectancy-Value Theory, the study investigates how expectancy, attainment, utility, intrinsic value, and cost predict behavioral intention toward AI integration. Data were collected from 454 prospective teachers from different universities in Turkey during the 2023–2024 academic year. Structural equation modeling (SEM) was conducted using AMOS 24.0. Findings revealed that utility value was the strongest positive predictor, while cost negatively affected intention. Due to ethical restrictions, the dataset cannot be shared publicly; however, the survey instruments and data access information are provided in the deposit.