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Overview

Duan, Shan, and Gong examine what shapes preschool teachers' willingness to adopt AI in early childhood classrooms. Grounded in an extended Technology Acceptance Model (TAM), the study uses structural equation modeling to test how perceived usefulness (PU), perceived ease of use (PEOU), subjective norm (SN), AI anxiety (AIA), and AI Self Efficacy (AISE) influence preschool teachers' behavioral intention to use AI.

Key Findings

  • Perceived usefulness and ease of use remain central drivers, consistent with the core TAM.
  • AI self-efficacy emerges as a meaningful predictor of intention, underscoring confidence-building as a lever for early-childhood AI adoption.
  • AI anxiety acts as a deterrent, and subjective norm (social influence from colleagues and leadership) shapes intention.
  • The extended-TAM model is validated in the distinctive early-childhood context, where developmental appropriateness and teacher comfort matter for AI integration.

Implications for Practice

  • For early-childhood administrators: Reducing AI anxiety and building teachers' self-efficacy through hands-on, low-stakes training can increase adoption.
  • For Teacher Education programs: Pre-service preschool teachers should experience AI tools in developmentally grounded ways that build confidence rather than apprehension.
  • For technology designers: Early-childhood AI tools should emphasize perceived usefulness and ease of use to win teacher buy-in.

Connected Concepts

Connected Articles

Citation

Exploring factors influencing preschool teachers' behavioral intention to use AI technologies in early childhood settings — Duan, Z., Shan, Y., & Gong, Y. (2026). Computers and Education: Artificial Intelligence, 10, 100589.

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