Exploring Teacher Perceptions of Deep Learning for Professional Development: A Technology Acceptance Model Approach

Abstract

Teacher professional development is a critical component for enhancing educational quality, and the integration of deep learning technologies has emerged as a transformative tool, although its implementation presents challenges. This study aimed to analyze the acceptance and usage of deep learning technologies in professional development programs using the Technology Acceptance Model (TAM) by investigating associated challenges and opportunities. Adopting a convergent parallel mixed-methods research design, data were collected through semi-structured interviews, focus group discussions (FGDs), and surveys measuring Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) from 30 participants comprising teachers, school administrators, and policymakers. Qualitative data underwent thematic analysis, while quantitative survey data were analyzed descriptively. Key findings reveal significant challenges such as technological infrastructure gaps (including internet access issues and inadequate hardware), limited digital literacy (compounded by training complexity), and resistance to change (including fear of being replaced by AI). Conversely, opportunities include personalized learning paths (such as personalized training recommendations), enhanced pedagogical insights, and scalable training programs. Although stakeholders perceived deep learning platforms as generally useful and easy to use, concerns about practical usability among policymakers and the impact of external barriers persist. The study concludes that deep learning offers substantial potential, but its successful integration necessitates addressing these structural, systemic, and human-centric barriers. It also offers actionable insights for educators, policymakers, and technology developers to foster more effective and inclusive deep learning-enhanced professional development.