What Drives Teachers' Use of AI in Preschool Education? A Motivational Perspective Based on Expectancy-Value Theory

Abstract

Although the use of artificial intelligence in early childhood education is becoming increasingly important, theoretical studies explaining preschool teachers’ motivation for using AI remain limited. Research based on the Expectancy-Value Theory, examining teachers’ perceptions of competence, value, and cost holistically, is particularly scarce. This study examines preschool teachers’ motivation to utilize AI tools through the lens of Expectancy-Value Theory, investigating how perceptions differ across demographic and usage-related factors. A mixed-methods explanatory sequential design was employed. Data were collected from 164 teachers using the QAIUM scale, and semi-structured interviews were conducted with 19 teachers representing different motivation levels. Teachers reported high expectancy and value perceptions but moderate cost perceptions regarding AI use. Higher professional experience, postgraduate education, and regular use of AI for instructional planning were associated with higher motivation and lower perceived costs. Qualitative findings revealed that teachers viewed AI as enriching instruction and enhancing professional efficiency while expressing concerns about data security, screen dependency, reduced creativity, and increased time demands. AI experience enhances teachers’ self-efficacy and value perceptions while decreasing perceived cost. Findings underscore the significance of professional development for the effective implementation of AI in early childhood education.