Teaching artificial intelligence through machine learning

an active learning approach in primary education classrooms

Authors

DOI:

https://doi.org/10.46661/ijeri.13119

Keywords:

artificial intelligence, coding, elementary education, educational technology, machine learning

Abstract

This research examines the implementation of machine learning concepts in elementary education through a sample of 1,009 fifth-grade students. The intervention involved a set of structured activities related to machine learning using Teachable Machine and RAISE (built on Scratch 3.0). A pre-experimental research design was employed, combining descriptive analysis with statistical inference. Specifically, a Student’s t-test was applied to analyze the first dimension, while the Wilcoxon test was used for the second dimension. The results indicate that elementary school students improved their understanding of machine learning and the ways in which artificial intelligence models are developed. Furthermore, students with prior experience using Scratch in school obtained higher scores and reported greater motivation compared to those without experience in block-based programming environments. The findings suggest that interactive learning activities focused on machine learning are effective for motivating students and facilitating their comprehension of AI, including how it is trained and generated. Additionally, these activities increased engagement and enjoyment during the sessions. Overall, the study demonstrates that implementing pedagogical designs aimed at introducing machine learning and artificial intelligence in primary education is both feasible and beneficial.

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References

Alpaydin, E. (2020). Introduction to machine learning (4th ed.). MIT Press.

Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.

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 (Vol. 1, p. 25). Vancouver, Canada.

Center for Democracy and Technology. (2025). Generative AI systems in education: Uses and misuses. https://cdt.org/insights/generative-ai-systems-in-education-uses-and-misuses/

Carney, M., Webster, B., Alvarado, I., Phillips, K., Howell, N., Griffith, J., Jongejan, J., Pitaru, A., & Chen, A. (2020). Teachable machine: Approachable web-based tool for exploring machine learning classification. Extended abstracts of the 2020 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3334480.3382839

Chen, A. (2020). Teachable machine: Approachable web-based tool for exploring machine learning classification. In Extended abstracts of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–8). Association for Computing Machinery. https://dl.acm.org/doi/10.1145/3334480.3382839 DOI: https://doi.org/10.1145/3334480.3382839

Daher, R. (2025). Integrating AI literacy into teacher education: A critical perspective paper. Discover Artificial Intelligence, 5, Article 217. https://doi.org/10.1007/s44163-025-00475-7 DOI: https://doi.org/10.1007/s44163-025-00475-7

Estevez, J., Garate, G., Guede, J. M., & Graña, M. (2019). Using Scratch to teach undergraduate students’ skills on artificial intelligence. arXiv. https://arxiv.org/abs/1904.00296

European Union. (2024). [A25.1]Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules in the field of artificial intelligence and amending Regulations (EC) No 300/2008, (EU) No 167/2013, (EU) No 168/2013, (EU) 2018/858, (EU) 2018/1139 and (EU) 2019/2144 and Directives 2014/90/EU, (EU) 2016/797 and (EU) 2020/1828 (Artificial Intelligence Regulation). Official Journal of the European Union, L series, number 2024/1689, of 12 July 2024.

Famaye, A., Gupta, S., & Tlili, A. (2023). Concerns about academic integrity and student dependence on AI tools in higher education. Humanities and Social Sciences Communications. https://doi.org/10.1057/s41599-025-05982-7 DOI: https://doi.org/10.1057/s41599-025-05982-7

Forero-Corba, W., & Negre Bennasar, F. (2024). Techniques and applications of machine learning and artificial intelligence in education: A systematic review. RIED-Revista Iberoamericana de Educación a Distancia, 27(1), 209–253. https://doi.org/10.5944/ried.27.1.37491 DOI: https://doi.org/10.5944/ried.27.1.37491

Gerlache, H. A. M., Ger, P. M., & Valentín, L. de la F. (2022). Towards the grade’s prediction. A study of different machine learning approaches to predict grades from student interaction data. International Journal of Interactive Multimedia and Artificial Intelligence, 7(4), 196–204. https://doi.org/10.9781/ijimai.2021.11.007 DOI: https://doi.org/10.9781/ijimai.2021.11.007

Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow (3rd ed.). O’Reilly.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning (2nd ed.). Springer. https://doi.org/10.1007/978-0-387-84858-7 DOI: https://doi.org/10.1007/978-0-387-84858-7

Houngue, P., Hountondji, M., & Dagba, T. (2022). An effective decision-making support for student academic path selection using machine learning. International Journal of Advanced Computer Science and Applications, 13(11), 727–734. https://doi.org/10.14569/IJACSA.2022.0131 184 DOI: https://doi.org/10.14569/IJACSA.2022.0131184

Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260. https://doi.org/10.1126/science.aaa8415 DOI: https://doi.org/10.1126/science.aaa8415

Kahn, K., & Winters, N. (2020). Constructionism and AI: A history and possible futures. MIT Media Lab.

Laru, J., Celik, I., Jokela, I., & Mäkitalo, K. (2025). The antecedents of pre-service teachers’ AI literacy: Perceptions about own AI-driven applications, attitude towards AI and knowledge in machine learning. European Journal of Teacher Education, 48(5), 964–986. https://doi.org/10.1080/02619768.2025.2535623 DOI: https://doi.org/10.1080/02619768.2025.2535623

Long, D., & Magerko, B. (2020). Artificial intelligence literacy: Competencies and design considerations. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–16. https://doi.org/10.1145/3313831.3376727 DOI: https://doi.org/10.1145/3313831.3376727

Maya, I., Pearson, J. N., Tapia, T., Wherfel, Q.M., & Reese, G. (2015). Supporting all learners in school-wide computational thinking: A cross-case qualitative analysis. Computers & Education, 82, 263–279. https://doi.org/10.1016/j.compedu.2014.11.022 DOI: https://doi.org/10.1016/j.compedu.2014.11.022

Mitchell, T. M. (1997). Machine learning. McGraw-Hill.

Mustafa, M.Y., Tlili, A., Lampropoulos, G., Huang, R., Jandrić, P., Zhao, J., Salha, S., Xu,L., Panda, S., Kinshuk, López-Pernas, S. & Saqr, M. (2024). A systematic review of literature reviews on artificial intelligence in education (AIED): a roadmap to a future research agenda. Smart Learn. Environ. 11, 59. https://doi.org/10.1186/s40561-024-00350-5 DOI: https://doi.org/10.1186/s40561-024-00350-5

Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). AI literacy: Definition, teaching, evaluation and ethical issues. Proceedings of the Association for Information Science and Technology, 58(1), 504–509. https://doi.org/10.1002/pra2.487 DOI: https://doi.org/10.1002/pra2.487

Román-Graván, P., & Arrifano-Tadeu, P.-J. (2025). Robotics in university teacher training: A comparative analysis of perceptions between Spain and Portugal. IJERI: International Journal of Educational Research and Innovation, (23). https://doi.org/10.46661/ijeri.11021 DOI: https://doi.org/10.46661/ijeri.11021

Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.

Sabuncuoglu, A. (2020). Designing one-year curriculum to teach artificial intelligence for middle school. In Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education (pp. 96–102). Association for Computing Machinery. https://doi.org/10.1145/3341525.3387368 DOI: https://doi.org/10.1145/3341525.3387364

Sáez-López, J.M., Román-González, M. y Vázquez-Cano, E. (2016). Visual programming languages integrated across the curriculum in elementary school. A two year case study using scratch in five schools. Computers & Education, 97, 129-141. https://dx.doi.org/10.1016/j.compedu.2016.03.003 DOI: https://doi.org/10.1016/j.compedu.2016.03.003

Sáez-López, J. M., Gonzalez-Calero, J.A., Del Olmo, J. & Cozar, R. (2023). Scratch and unity design in elementary education: A study in initial teacher training. Journal of Computer Assisted Learning, 39 (5), 1528-1538 https://doi.org/10.1111/jcal.12815 DOI: https://doi.org/10.1111/jcal.12815

Sperling, K., Stenliden, L., Jörgen, N., & Heintz, F. (2022). Still w(AI)ting for the automation of teaching: An exploration of machine learning in Swedish primary education using Actor–Network Theory. European Journal of Education, 57(4), 584–600. https://doi.org/10.1111/ejed.12526 DOI: https://doi.org/10.1111/ejed.12526

UNESCO. (2025). Marco de competencias para estudiantes en materia de IA. https://unesdoc.unesco.org/ark:/48223/pf0000393812

Villarino, R. T. (2025). Artificial Intelligence (AI) integration in Rural Philippine Higher Education: Perspectives, challenges, and ethical considerations. IJERI: International Journal of Educational Research and Innovation, (23). https://doi.org/10.46661/ijeri.10909 DOI: https://doi.org/10.46661/ijeri.10909

Villegas-Ch, W., García-Ortiz, J., & Sánchez-Viteri, S. (2024). Optimizing writing skills in children using a real-time feedback system based on machine learning. IEEE Access, 12, 164634–164651. https://doi.org/10.1109/ACCESS.2024.3492974 DOI: https://doi.org/10.1109/ACCESS.2024.3492974

Wan, X., Zhou, X., Ye, Z., Mortensen, C. K., & Bai, Z. (2020). SmileyCluster: Supporting accessible machine learning in K–12 scientific discovery. In Proceedings of the Interaction Design and Children Conference (pp. 23–35). Association for Computing Machinery. https://doi.org/10.1145/3392063.3394419 DOI: https://doi.org/10.1145/3392063.3394440

Yuan, J., Qiu, X., Wu, J., Guo, J., Li, W., & Wang, Y.-G. (2024). Integrating behavior analysis with machine learning to predict online learning performance: A scientometric review and empirical study. arXiv. https://arxiv.org/abs/2406.11847

Zimmermann-Niefield, A., Polson, S., Moreno, C., & Shapiro, B. (2020). Youth making machine learning models for gesture-controlled interactive media. In Proceedings of the Interaction Design and Children Conference (pp. 63–74). Association for Computing Machinery. https://doi.org/10.1145/3392063.3394438 DOI: https://doi.org/10.1145/3392063.3394438

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Published

2026-05-26

How to Cite

Sáez López, J. M., Redondo Duarte, S., Neubauer Esteban, A., & Pena Garrido, M. (2026). Teaching artificial intelligence through machine learning: an active learning approach in primary education classrooms. IJERI: International Journal of Educational Research and Innovation, (25), 1–14. https://doi.org/10.46661/ijeri.13119

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