A Practical Study of the Use of AI-Based E-Book Guides to Improve Early Childhood Mathematics Skills
DOI:
https://doi.org/10.48161/qaj.v6n1a2176Keywords:
AI-based e-book, early mathematics, practicality, early childhood education, technology acceptance.Abstract
Early mathematics is a crucial foundation for children's cognitive development, yet teaching in early childhood education often remains conventional and less engaging. This study aimed to evaluate the practicality of an AI-based e-book designed to support mathematics learning in preschool settings. Employing an explanatory sequential mixed-methods design, quantitative data were collected from 125 preschool teachers in Padang, Indonesia, followed by qualitative interviews with 15 participants. Findings indicated high practicality across all assessed dimensions: design and layout (91.0%), purpose and usefulness (91.6%), ease of use (88.4%), clarity (89.6%), assessment and evaluation (90.4%), and AI-based interactivity (87.6%), with an overall mean score of 89.8%, classified as "Highly Practical." Qualitative insights confirmed that the e-book enhanced engagement, facilitated instructional differentiation, and aligned well with pedagogical objectives, though infrastructural barriers limited its full use in resource-constrained schools. The study highlights that practicality, grounded in the Technology Acceptance Model, is essential for ensuring adoption alongside effectiveness. The findings contribute uniquely to the literature by shifting focus toward feasibility and teacher acceptance. The study concludes that the AI-based e-book is a highly practical tool for teachers, with its successful adoption contingent on addressing infrastructural barriers to ensure equitable implementation.
Downloads
References
Putri, L. D., Rozi, M. F., & Rahman, M. A. (2024). A conceptual family partnership model with PAUD institutions in developing the potential of early children based on blended learning. Ensaio: Avaliação e Políticas Públicas em Educação, 32(125), E0244444.
Bokan, M., Abdimalikkyzy, J., Babaeva, A., Keldibekova, A., Makpyr, S., & Smagulov, Y. (2025). Enhancing Logical Thinking Skills of Future Informatics Teachers through Artificial Intelligence. Qubahan Academic Journal, 5(1), 543-551.
Zhang, S., & Chen, X. (2022). Applying artificial intelligence into early childhood math education: Lesson design and course effect. In 2022 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE) (pp. 635–638). IEEE.
Wang, X., Huang, R. T., Sommer, M., Pei, B., Shidfar, P., Rehman, M. S., ... & Martin, F. (2024). The efficacy of artificial intelligence-enabled adaptive learning systems from 2010 to 2022 on learner outcomes: A meta-analysis. Journal of Educational Computing Research, 62(6), 1348–1383.
Xu, Y., He, K., Levine, J., Ritchie, D., Pan, Z., Bustamante, A., & Warschauer, M. (2024). Artificial intelligence enhances children’s science learning from television shows. Journal of Educational Psychology.
Korat, O., Shamir, A., & Heibal, S. (2013). Expanding the boundaries of shared book reading: E-books and printed books in parent–child reading as support for children’s language. First Language, 33(5), 504–523.
Neumann, S. B. (2023). Early literacy in everyday spaces. Handbook on the Science of Early Literacy, 371.
Morgan, H. (2013). Multimodal children’s e-books help young learners in reading. Early Childhood Education Journal, 41(6), 477–483.
Holmes, W., Persson, J., Chounta, I. A., Wasson, B., & Dimitrova, V. (2022). Artificial intelligence and education: A critical view through the lens of human rights, democracy and the rule of law. Council of Europe.
Popenici, S. A., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(1), 22.
Huang, W., Wang, T., & Tong, Y. (2024). The effect of gamified project-based learning with AIGC in information literacy education. Innovations in Education and Teaching International, 1–15.
McKnight, L., & Morgan, A. (2025). Getting the goods: An analysis of implications of the ‘education as delivery’ metaphor for the AI age. The Australian Educational Researcher, 52(3), 1869–1886.
Guss, S. S., Clements, D. H., & Sarama, J. H. (2022). High-quality early math: Learning and teaching with trajectories and technologies. In Handbook of research on innovative approaches to early childhood development and school readiness (pp. 349–373). IGI Global Scientific Publishing.
Outhwaite, L., Anders, J., & Van Herwegen, J. (2022). Mathematics attainment falls behind reading in the early primary school years.
Purpura, D. J., King, Y. A., Rolan, E., Hornburg, C. B., Schmitt, S. A., Hart, S. A., & Ganley, C. M. (2020). Examining the factor structure of the home mathematics environment to delineate its role in predicting preschool numeracy, mathematical language, and spatial skills. Frontiers in Psychology, 11, 1925.
Nofira, N. R., & Hazizah, N. (2023). Pengaruh media ulat matematika tiga dimensi terhadap kemampuan matematika awal anak usia 5–6 tahun. Buhuts Al Athfal: Jurnal Pendidikan dan Anak Usia Dini, 3(2), 271–282.
Lin, Y. T., & Cheng, C. T. (2022). Effects of technology-enhanced board game in primary mathematics education on students’ learning performance. Applied Sciences, 12(22), 11356.
Merkelbach, I., Plak, R. D., Jong, M. T. S. D., & Rippe, R. C. (2022). Differential efficacy of digital scaffolding of numeracy skills in kindergartners with mild perinatal adversities. In Frontiers in Education (Vol. 7, p. 709809). Frontiers Media SA.
Fitria, D., Asrizal, A., & Lufri, L. (2025). Integrating blended problem-based learning with ethnoscience to develop twenty-first-century skills in middle school science education. International Journal of Pedagogy and Curriculum, 32(2), 165–188.
McKenney, S., Nieveen, N., Van den Akker, J., Gravemeijer, K., McKenney, S., & Nieveen, N. (2013). An introduction to educational design research.
Moore, S., Stefaniak, J., & Reeves, T. C. (2025). The research we need: Barriers, methodologies, and strategic planning for an educational technology and instructional design research agenda. Journal of Computing in Higher Education, 1–10.
Shadiev, R., Yang, M. K., Reynolds, B. L., & Hwang, W. Y. (2022). Improving English as a foreign language–learning performance using mobile devices in unfamiliar environments. Computer Assisted Language Learning, 35(9), 2170–2200.
Fokides, E. (2023). Development and testing of a scale for examining factors affecting the learning experience in the metaverse. Comput. Educ. X Real., 2, 100025.
Tuli, N., & Mantri, A. (2021). Evaluating usability of mobile-based augmented reality learning environments for early childhood. International Journal of Human–Computer Interaction, 37(9), 815–827.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
Creswell, J. W., & Plano Clark, V. L. (2017). Designing and conducting mixed methods research (3rd ed.). Sage Publications.
Padang City Education Office. (2024). Number of kindergarten schools, teachers, and students under the Ministry of Education, Culture, Research, and Technology by district in Padang City, 2024/2025. BPS Kota Padang.
Cheng, L., Li, Y., Su, Y., & Gao, L. (2023). Effect of regulation scripts for dialogic peer assessment on feedback quality, critical thinking and climate of trust. Assessment & Evaluation in Higher Education, 48(4), 451-463.
Aiken, L.W. (1980). Content validity and reliability of single items or questionnaires. Pepperdine University.
Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach's alpha. International Journal of Medical Education, 2, 53-55.
Nunnally, J. C. (1978). An overview of psychological measurement. Clinical diagnosis of mental disorders: A handbook, 97-146.
Fetters, M. D., Curry, L. A., & Creswell, J. W. (2013). Achieving integration in mixed methods designs—principles and practices. Health Services Research, 48, 2134-2156.
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.
Mayer, R. E. (2020). Designing multimedia instruction in anatomy: An evidence‐based approach. Clinical Anatomy, 33(1), 2-11.
Sweller, J. (2020). Cognitive load theory and educational technology. Educational Technology Research and Development, 68(1), 1-16.
Handrianto, C., Jusoh, A. J., Herlina, S., Alfurqan, A., & Nor-Azhar, N. F. (2025). Exploring the factors influencing motivation and understanding in Islamic religious education: A mixed-methods study in urban and rural areas. International Journal of Interdisciplinary Educational Studies, 20(3), 75–94.
Sunarti, V., Jamaris, J., Solfema, S., Iswari, M., Hidayati, A., & Rahman, M. A. (2024). Evaluating the effectiveness of a blended learning system for developing technological andragogical content knowledge (TACK) in community educators. Encontros Bibli, 29, e96419.
Arwin, A., Kenedi, A. K., Anita, Y., Hamimah, H., & Zainil, M. (2024). STEM-based digital disaster learning model for disaster adaptation ability of elementary school students. International Journal of Evaluation and Research in Education, 13(5), 3248–3258.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Qubahan Academic Journal

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.



