Elementary Teachers’ Acceptance of Digital Learning Platforms for Differentiated and Deep Learning: An Empirical Study Based on the UTAUT Framework

The effective integration of digital technology in differentiated and deep learning requires a clear understanding of the factors influencing teachers’ acceptance of instructional platforms. This study investigates the determinants of elementary school teachers’ adoption of Canva for developing differentiated learning media using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. A quantitative design was employed, involving 133 Indonesian elementary teachers selected through purposive sampling. Data were collected through a validated questionnaire and analyzed using Structural Equation Modeling (SEM) with SmartPLS 4, including measurement and structural model evaluation based on path coefficients, p-values, R², and predictive relevance (Q²). The model explains 68% of the variance in behavioral intention (R² = 0.68), indicating substantial explanatory power. The results show that effort expectancy (β = 0.35, p < 0.01), social influence (β = 0.28, p < 0.01), and self-efficacy (β = 0.22, p < 0.05) significantly and positively influence teachers’ intention to use Canva, whereas performance expectancy and facilitating conditions do not exhibit significant direct effects. These findings confirm the relevance of UTAUT in the Indonesian educational technology context and identify ease of use, social support, and digital confidence as key drivers of technology adoption. The study offers practical implications for professional development programs and educational policy aimed at strengthening technology integration for differentiated instruction.