Volume 17 (2025) Download Cover Page

The Relationship Between Computational and Creative Thinking in Preschool Children: An Application Through Gamification and Artificial Intelligence-Supported Constructivist Personalized Learning Environment

Article Number: e2025337  |  Available Online: July 2025  |  DOI: 10.22521/edupij.2025.17.337

Chinnaphat Junruang , Issara Kanjug

Abstract

Background/purpose. In response to the increasing need to foster future-ready competencies, this study investigates the relationship between computational thinking (CT) and creative thinking (CrT) in early childhood. Traditional early education often overlooks the integration of these higher-order cognitive skills. This research aims to examine how a constructivist personalized learning Environment—integrating gamification and artificial intelligence—can simultaneously develop CT and CrT among preschool learners.

Materials/methods. A mixed-methods design was employed, involving 30 preschool children in Kindergarten Year 2 at a university demonstration school. Participants engaged in adaptive learning tasks delivered through a constructivist, gamified platform enhanced by AI. Quantitative data were collected using validated CT and CrT assessment tools, while qualitative data were derived from protocol-based interviews and performance observations.

Results. The findings indicated high proficiency in both CT (mean = 33.36/40; 83.4%) and CrT (mean = 28.47/40; 71.18%). Strong correlations were observed between CT and CrT total scores (r = .74, p < .01), especially between Pattern Recognition and Fluency (r = .66) and Decomposition and Originality (r = .57). Qualitative data supported these outcomes, revealing that children applied structured problem-solving and imaginative strategies concurrently in open-ended tasks.

Conclusion. CT and CrT are mutually reinforcing cognitive domains that can be effectively developed through constructivist, personalized, and technology-enhanced learning environments. The integration of gamification and AI in early education facilitates engagement, differentiation, and cognitive growth, offering a promising model for cultivating foundational 21st-century thinking skills.

Keywords: Computational thinking, creative thinking, early childhood education, personalized learning environment, gamification, artificial intelligence

References

Anderson, J. R. (1980). Cognitive psychology and its implications. W.H. Freeman.

Bers, M. U. (2018). Coding as a playground: Programming and computational thinking in the early childhood classroom. Routledge. https://doi.org/10.4324/9781315398945

Bocconi, S., Chioccariello, A., Dettori, G., Ferrari, A., Engelhardt, K., Kampylis, P., & Punie, Y. (2016). Developing computational thinking in compulsory education. Publications Office of the European Union. Retrieved from https://publications.jrc.ec.europa.eu/repository/handle/JRC104188

Brennan, K., & Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. In Proceedings of the 2012 Annual Meeting of the American Educational Research Association. Retrieved from http://scratched.gse.harvard.edu/ct/files/AERA2012.pdf

Brown, J. S., Collins, A., & Duguid, P. (1989). Situated cognition and the culture of learning. Educational Researcher, 18(1), 32–42. https://doi.org/10.3102/0013189X018001032

Center on the Developing Child at Harvard University. (2016). From best practices to breakthrough impacts. Retrieved from https://developingchild.harvard.edu/resources/from-best-practices-to-breakthrough-impacts/

Goswami, U. (2006). Neuroscience and education: From research to practice? Nature Reviews Neuroscience, 7(5), 406–413. https://doi.org/10.1038/nrn1907

Grant, P., & Basye, D. (2014). Personalized learning: A guide for engaging students with technology. ISTE.

Guilford, J. P. (1950). Creativity. American Psychologist, 5(9), 444–454. https://doi.org/10.1037/h0063487

Hamari, J., Koivisto, J., & Sarsa, H. (2014). Does gamification work? – A literature review of empirical studies on gamification. In Proceedings of the 47th Hawaii International Conference on System Sciences. https://doi.org/10.1109/HICSS.2014.377

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign. Retrieved from https://curriculumredesign.org/wp-content/uploads/AI-in-Education-Promises-and-Implications_CCR-2019.pdf

Hwang, G. J., & Chien, T. C. (2022). A review of artificial intelligence applications in early childhood education: Learning, assessment, and challenges. Computers & Education, 182, 104463. https://doi.org/10.1016/j.compedu.2022.104463

Jalinus, N., Nabawi, R. A., & Putra, A. S. (2019). The seven components of project-based learning to enhance productive competencies of vocational students. Journal of Technical Education and Training, 11(1), 34–43. https://doi.org/10.30880/jtet.2019.11.01.005

Jonassen, D. H. (1999). Designing constructivist learning environments. In C. M. Reigeluth (Ed.), Instructional-design theories and models: A new paradigm of instructional theory (Vol. II, pp. 215–252). Lawrence Erlbaum Associates.

Jonassen, D. H., Carr, C., & Yueh, H. P. (1998). Computers as mindtools for engaging learners in critical thinking. TechTrends, 43(2), 24–32. https://doi.org/10.1007/BF02818172

Junruang, C., Kanjug, I., Samat, C. (2021). The Development of a Computational Thinking Learning Package that Integrates a Learning Experience Design for Grade K. In: Huang, YM., Lai, CF., Rocha, T. (eds) Innovative Technologies and Learning. ICITL 2021. Lecture Notes in Computer Science, vol 13117. Springer, Cham. https://doi.org/10.1007/978-3-030-91540-7_16

Kalelioglu, F., Gülbahar, Y., & Kukul, V. (2016). A framework for computational thinking based on a systematic research review. Baltic Journal of Modern Computing, 4(3), 583–596.

Kong, S. C., Lai, M., & Sun, D. (2022). Developing computational and creative thinking through the integration of coding activities and maker education. British Journal of Educational Technology, 53(1), 96–113. https://doi.org/10.1111/bjet.13171

Krath, J., Günther, S., & Süß, H. M. (2023). Adaptive learning with AI in education: Benefits and challenges. Computers & Education, 184, 104545. https://doi.org/10.1016/j.compedu.2022.104545

OECD. (2021). OECD future of education and skills 2030. https://doi.org/10.1787/bf31d04a-en

Ouyang, F., & Jiao, P. (2021). Artificial intelligence in education: Opportunities and challenges for personalized learning. Educational Technology Research and Development, 69(4), 2109–2133. https://doi.org/10.1007/s11423-021-09985-4

Papadakis, S., & Kalogiannakis, M. (2023). Editorial: The Impact of Smart Screen Technologies and Accompanied Apps on Young Children's Learning and Developmental Outcomes, Volume 2. Frontiers in Education, 8, Article ID: 1249116. https://doi.org/10.3389/feduc.2023.1249116

Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. Basic Books.

Phungching, S., & Laddaklom, T. (2020). Developing creative thinking through nature-integrated art-based learning in early childhood. Journal of Early Childhood Education Research, 9(2), 321–336.

Piaget, J. (1954). The construction of reality in the child. Basic Books.

Resnick, M. (2017). Lifelong kindergarten: Cultivating creativity through projects, passion, peers, and play. MIT Press.

Romero, M., Lille, B., & Vernay, C. (2023). Supporting creative thinking through computational thinking activities in early childhood. British Journal of Educational Technology, 54(1), 145–165. https://doi.org/10.1111/bjet.13210

Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation. American Psychologist, 55(1), 68–78. https://doi.org/10.1037/0003-066X.55.1.68

Saenboonsong, W., & Poonsawad, P. (2024). Gamification for enhancing creative thinking skills in early childhood education. Early Child Development and Care.

Shonkoff, J. P., & Phillips, D. A. (2000). From neurons to neighborhoods: The science of early child development. National Academies Press. https://doi.org/10.17226/9824

Shonkoff, J. P., et al. (2012). The lifelong effects of early childhood adversity and toxic stress. Pediatrics, 129(1), e232–e246. https://doi.org/10.1542/peds.2011-2663

Tikva, C., & Tambouris, E. (2021). Fostering computational thinking through interdisciplinary teaching in early childhood. Computers & Education, 164, 104112. https://doi.org/10.1016/j.compedu.2020.104112

Torrance, E. P. (1974). Torrance tests of creative thinking. Scholastic Testing Service.

UNESCO. (2022). Reimagining our futures together: A new social contract for education. UNESCO Publishing. https://unesdoc.unesco.org/ark:/48223/pf0000379707

Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.

Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35. https://doi.org/10.1145/1118178.1118215

World Economic Forum. (2020). The future of jobs report 2020. Retrieved from https://www.weforum.org/reports/the-future-of-jobs-report-2020

Yildiz Durak, H., & Saritepeci, M. (2020). The effect of adaptive digital learning environment on student engagement and achievement. Interactive Learning Environments, 28(6), 713–727. https://doi.org/10.1080/10494820.2018.1552873

Zainuddin, Z. (2018). Students’ learning performance and perceived motivation in gamified flipped-class instruction. Computers & Education, 126, 75–88. https://doi.org/10.1016/j.compedu.2018.07.003