Predicting Teachers’ Intentions for AIGC Integration in Preschool Education: A Hybrid SEM-ANN Approach
This study investigates the key factors influencing preschool teachers’ sustained use of Artificial Intelligence-Generated Content (AIGC) technology in educational settings. While prior research has extensively examined initial adoption, little attention has been given to understanding the continuous intention of preschool teachers with AIGC. To bridge this gap, this study integrates the Technology Acceptance Model (TAM), Expectation-Confirmation Model (ECM), and Flow Theory to develop a comprehensive framework that captures cognitive, affective, and experiential factors shaping continued AIGC adoption.
AIGC has demonstrated immense educational potential, providing personalized learning experiences, real-time feedback, and intelligent student progress tracking. However, most existing research focuses primarily on system usability and feasibility, neglecting the motivational and psychological aspects that determine continuous intention to use AIGC. Specifically, satisfaction, expectation confirmation, and flow experience have been largely overlooked as key determinants of sustained technology use. Given that preschool educators face unique pedagogical challenges, such as adapting AIGC content to young learners and maintaining engagement, understanding the drivers of long-term AIGC use is essential for optimizing its integration into preschool education.
This study employs a mixed-method approach to ensure a rigorous and comprehensive analysis. A total of 433 preschool teachers participated in the survey, and Partial Least Squares-Structural Equation Modeling (PLS-SEM) was used to test the hypothesized relationships. To complement structural modeling, Artificial Neural Network (ANN) modeling was applied to uncover non-linear relationships that traditional statistical methods might overlook. By integrating PLS-SEM and ANN, this study provides a more robust, predictive, and holistic understanding of the factors driving sustained AIGC adoption.
This study makes significant theoretical and practical contributions. Theoretically, it extends TAM and ECM by incorporating Flow Theory. Unlike prior studies focusing primarily on perceived usefulness and ease of use, this research identifies confirmation and satisfaction as the strongest predictors of continued intention to use AIGC. Practically, the findings provide valuable insights for policymakers, school administrators, and ed-tech developers, offering recommendations for designing more engaging, sustainable, and user-friendly AIGC solutions tailored for preschool education.
The results indicate that satisfaction (β = 0.280, p < 0.001) is the strongest predictor of continued AIGC use, followed by attitude (β = 0.262, p < 0.001) and flow experience (β = 0.223, p < 0.001). Expectation confirmation significantly enhances perceived usefulness (β = 0.505, p < 0.001) and satisfaction (β = 0.349, p < 0.001), reinforcing the importance of aligning AIGC tools with teachers’ expectations. ANN analysis further highlights confirmation (95.28%) and satisfaction (82.41%) as the most influential factors, whereas perceived ease of use (22.35%) has a relatively minor impact. These findings suggest that positive user experience, engagement, and expectation fulfillment are key drivers of long-term AIGC adoption. Moreover, ANN analysis revealed complex nonlinear relationships, demonstrating that traditional statistical methods might underestimate the true impact of psychological and experiential factors on technology retention.
For practitioners, this study provides several actionable recommendations. First, AIGC tools should be designed to enhance engagement and intrinsic motivation, integrating gamification elements, interactive features, and adaptive learning support to sustain user interest. Second, ongoing professional development programs should be implemented to train teachers on the pedagogical applications of AIGC, addressing any concerns related to usability or long-term feasibility. Third, AIGC platforms should incorporate customization features, allowing educators to tailor content based on their specific classroom needs and teaching styles. By addressing these factors, AIGC adoption in preschool education can be more sustainable and impactful.
For researchers, this study opens multiple avenues for future exploration. First, future research should adopt a longitudinal approach to examine how preschool teachers’ attitudes and behaviors toward AIGC evolve over time. Second, more research is needed to explore the role of teacher personality traits and digital literacy levels in shaping AIGC adoption patterns. Third, cross-cultural studies could provide deeper insights into how different educational systems and socio-cultural contexts influence preschool teachers’ responses to AIGC technologies. Furthermore, AI-driven predictive analytics should be explored to model behavioral trends and optimize AIGC implementations across diverse learning environments.
This study has significant implications for educational equity, teacher workload, and early childhood learning experiences. By empowering preschool teachers with AIGC, this research promotes more inclusive and accessible preschool education, reducing disparities in educational resources and opportunities. Additionally, AI-driven teaching solutions can alleviate teacher workload, enabling educators to focus on creative and interactive pedagogical strategies rather than administrative tasks. As AIGC continues to evolve, its potential to transform preschool education into a more engaging, adaptive, and learner-centered experience becomes increasingly evident.
While this study provides valuable insights into preschool teachers’ sustained use of AIGC, several areas require further exploration. First, objective usage data should be incorporated into future research rather than relying solely on self-reported surveys to enhance validity. Second, longitudinal studies should examine how teachers’ continuous intention to use AIGC evolves over time in response to technological advancements and policy shifts. Third, as this study focuses on preschool educators, future research should explore whether the identified factors apply to primary and secondary education teachers. Additionally, ethical concerns, AI trust, and data privacy issues should be further investigated, as they may significantly impact the long-term adoption of AIGC in educational settings.
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