Artificial Intelligence Readiness of Teachers in Inclusive Early Childhood Education

Adilah Wina Fitria, Arismunandar Arismunandar, Syamsuddin Syamsuddin, Herlina Herlina

Abstract

Teachers’ capacity to integrate artificial intelligence safely and pedagogically in inclusive early childhood education remains underexamined. This study tested an extended Technology Acceptance Model linking digital competence, inclusive attitude, school policy, perceived usefulness, perceived ease of use, and artificial intelligence readiness. Cross-sectional questionnaire data from 167 early childhood teachers in Indonesia were analyzed using partial least squares structural equation modeling. The measurement model demonstrated high reliability (composite reliability=0.973–0.975), convergent validity (average variance extracted=0.817–0.833), and discriminant validity (heterotrait-monotrait ratio=0.126–0.652). The model explained 53.9% of the variance in artificial intelligence readiness. Perceived ease of use and perceived usefulness showed the largest direct effects on readiness, while school policy produced the largest total effect. Digital competence strongly predicted perceived usefulness, whereas inclusive attitude did not. The findings extend technology acceptance research by showing that institutional support and inclusive pedagogical orientation complement usefulness and ease perceptions. Early childhood institutions should provide sustained training, accessible infrastructure, child-data governance, and supervised, developmentally appropriate implementation. 

Keywords

Digital Competence; Educational Technology; Perceived Ease Of Use; Perceived Usefulness; School Policy; Technology Acceptance Model

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References

Ayanwale, M. A., Sanusi, I. T., Adelana, O. P., Aruleba, K. D., & Oyelere, S. S. (2022). Teachers’ readiness and intention to teach artificial intelligence in schools. Computers and Education: Artificial Intelligence, 3. https://doi.org/10.1016/j.caeai.2022.100099

Berson, I. R., Berson, M. J., & Luo, W. (2025). Innovating responsibly: Ethical considerations for artificial intelligence in early childhood education. AI, Brain and Child, 1. https://doi.org/10.1007/s44436-025-00003-5

Colomo-Magaña, E., Cabero-Almenara, J., Ruiz-Palmero, J., & Palacios-Rodríguez, A. (2026). Ethical and responsible use of generative artificial intelligence by university students: Design and validation of an instrument using the expert competence coefficient. International Journal of Educational Research and Innovation, (25), 1–20. https://doi.org/10.46661/ijeri.13253

Copur-Gencturk, Y., Li, J., & Atabaş, Ş. (2024). Improving teaching at scale: Can artificial intelligence be incorporated into professional development to create interactive, personalized learning for teachers? American Educational Research Journal, 61(4), 767–802. https://doi.org/10.3102/00028312241248514

Curran, F. C., Carlo, S. D., & Harris-Walls, K. (2024). Making the data visible: A systematic review of systems-level data dashboards for leadership and policy in education. Review of Educational Research, 96(1), 3–52. https://doi.org/10.3102/00346543241288249

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

DeLuca, C. (2025). The AI3 model: Future directions for artificial intelligence, assessment innovation, and academic integrity. Educational Researcher. https://doi.org/10.3102/0013189X251385537

Dilek, M., Baran, E., & Aleman, E. (2025). AI literacy in teacher education: Empowering educators through critical co-discovery. Journal of Teacher Education, 76(3), 294–311. https://doi.org/10.1177/00224871251325083

Durham, C. (2024). Centering equity for multilingual learners in preservice teachers’ technological pedagogical content knowledge. Journal of Teacher Education, 75(3), 347–360. https://doi.org/10.1177/00224871231223460

Flavin, E., Hwang, S., & Morales, M. (2025). Let’s ask the robot: Epistemic stance between teacher candidates toward artificial intelligence in mathematics lesson planning. Journal of Teacher Education, 76(3), 262–279. https://doi.org/10.1177/00224871251325079

Fontes, C., Corrigan, C. C., & Lütge, C. (2023). Governing artificial intelligence during a pandemic crisis: Initiatives at the European Union level. Technology in Society, 72. https://doi.org/10.1016/j.techsoc.2023.102204

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104

Genovesi, E., Gaches, A., McKenzie, J., Hanlon, C., & Hoekstra, R. A. (2024). Inclusive strategies for children with developmental disabilities in mainstream classrooms in African countries: A systematic review of stakeholder experiences, attitudes, and perspectives. Review of Educational Research, 95(6), 1183–1212. https://doi.org/10.3102/00346543241288247

Guo, C., & Wan, B. (2022). The digital divide in online learning in China during the COVID-19 pandemic. Technology in Society, 71. https://doi.org/10.1016/j.techsoc.2022.102122

Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Evaluation of reflective measurement models. In Partial Least Squares Structural Equation Modeling Using R (pp. 75–90). Springer. https://doi.org/10.1007/978-3-030-80519-7_4

Henriksen, D., Creely, E., Gruber, N., & Leahy, S. (2025). Social-emotional learning and generative AI: A critical literature review and framework for teacher education. Journal of Teacher Education, 76(3), 312–328. https://doi.org/10.1177/00224871251325058

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115–135. https://doi.org/10.1007/s11747-014-0403-8

Johnson, C. C., Walton, J. B., Strickler, L., & Elliott, J. B. (2022). Online teaching in K–12 education in the United States: A systematic review. Review of Educational Research, 93(3), 353–411. https://doi.org/10.3102/00346543221105550

Kölemen, E. B., & Yıldırım, B. (2025). A new era in early childhood education: Teachers’ opinions on the application of artificial intelligence. Education and Information Technologies, 30, 17405–17446. https://doi.org/10.1007/s10639-025-13478-9

Lim, E. M. (2023). The effects of pre-service early childhood teachers’ digital literacy and self-efficacy on their perception of artificial intelligence education for young children. Education and Information Technologies, 28, 12969–12995. https://doi.org/10.1007/s10639-023-11724-6

Mills, K. A., Cope, J., Scholes, L., & Rowe, L. (2024). Coding and computational thinking across the curriculum: A review of educational outcomes. Review of Educational Research, 95(3), 581–618. https://doi.org/10.3102/00346543241241327

O’Meara, J., Rizvi, M., Flores, M. A., Craig, C. J., Samuels, J. H., & Hill-Jackson, V. (2024). The 5Ps of holistic policy development: A way forward for engaging teacher educators? Journal of Teacher Education, 75(3), 253–260. https://doi.org/10.1177/00224871241236001

Panke, S. (2025). How can artificial intelligence research this? An autoethnographic exploration of generative artificial intelligence in research, teaching and instructional design. Journal of Teacher Education, 76(3), 230–244. https://doi.org/10.1177/00224871251325065

Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879

Strogilos, V., King-Sears, M. E., Tragoulia, E., Voulagka, A., & Stefanidis, A. (2023). A meta-synthesis of co-teaching students with and without disabilities. Educational Research Review, 38. https://doi.org/10.1016/j.edurev.2022.100504

Su, J., Ng, D. T. K., & Chu, S. K. W. (2023). Artificial intelligence literacy in early childhood education: The challenges and opportunities. Computers and Education: Artificial Intelligence, 4. https://doi.org/10.1016/j.caeai.2023.100124

Su, J., & Yang, W. (2022). Artificial intelligence in early childhood education: A scoping review. Computers and Education: Artificial Intelligence, 3. https://doi.org/10.1016/j.caeai.2022.100049

Weisberg, L., & Dawson, K. (2023). The intersection of equity pedagogy and technology integration in preservice teacher education: A scoping review. Journal of Teacher Education, 74(4), 327–342. https://doi.org/10.1177/00224871231182129

DOI: https://doi.org/10.20961/paedagogia.v29i3.125580

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