Artificial Intelligence and Machine Learning in Statistical Education

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Víctor Alfonso Torres-Gordillo
Corporación Universitaria Minuto de Dios - UNIMINUTO, Colombia
https://orcid.org/0009-0009-7557-6761

Abstract

This study analyzes the impact of artificial intelligence (AI) and machine learning in the teaching of statistics in higher education, emphasizing their applicability in enhancing conceptual understanding and personalizing learning. A mixed-methods explanatory sequential design was used, involving a quasi-experimental approach applied to 120 undergraduate students in Colombia. Quantitative instruments (standardized tests, ANOVA, t-test, and logistic regression) and qualitative tools (semi-structured interviews and thematic analysis with NVivo) were employed. The results showed that the group using AI-based platforms achieved statistically significant improvements in performance, concept retention, and knowledge transfer. Immediate feedback and weekly platform engagement were identified as key predictors of academic success. Interviews with instructors revealed positive perceptions of AI as a learning facilitator, although concerns were expressed regarding technological autonomy and the evolving role of educators. It is concluded that AI can enhance statistical education when implemented under clear ethical and pedagogical principles, without replacing human support. These findings reinforce the value of hybrid and adaptive models that integrate advanced technology with reflective teaching practices.

Item information
Published: Jul 18, 2025
Issue: Vol. 4 (2025): Revista de Educación Estadística
Section: Investigación
DOI: https://doi.org/10.29035/redes.4.1.7
Keywords: Artificial Intelligence, Machine Learning, Statistical Education, Learning Personalization, Data Modeling

Article Details

How to Cite
Torres-Gordillo, V. A. (2025). Artificial Intelligence and Machine Learning in Statistical Education. Revista De Educação Estatística, 4, 1-21. https://doi.org/10.29035/redes.4.1.7
References

Campbell, D. T. y Stanley, J. C. (1963). Experimental and quasi-experimental designs for research. Houghton Mifflin.

Chance, B., Ben-Zvi, D., Garfield, J. y Medina, E. (2007). The role of technology in improving student learning of statistics. Technology Innovations in Statistics Education, 1(1), 1-26.

Chen, C., Wang, M., Zou, D. y Cheng, G. (2021). Digital game-based learning of computational thinking: A systematic review on learning effectiveness and scaffolding design. Computers & Education, 182, 104463. https://doi.org/10.1016/j.compedu.2022.104463

Creswell, J.W. y Creswell, J.D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications.

Creswell, J.W. y Plano Clark, V.L. (2018). Diseño y evaluación de investigaciones de métodos mixtos (2.ª ed.). Pearson Educación.

García-Peñalvo, F.J. y Corell, A. (2020). La transformación digital en las universidades: Implicaciones de la COVID-19 sobre la enseñanza y el aprendizaje. Education in the Knowledge Society, 21, 1-14. https://doi.org/10.14201/eks.23013

Garfield, J. y Ben-Zvi, D. (2008). Developing students’ statistical reasoning: Connecting research and teaching practice. Springer.

Holmes, W., Bialik, M. y Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.

Huang, S., Chen, C. y Lin, C. (2023). AI-assisted adaptive learning in statistics education: A meta-analysis of recent advances. Computers & Education, 195, 104782. https://doi.org/10.1016/j.compedu.2023.104782Mayer, R. E. (2021). Multimedia learning (3rd ed.). Cambridge University Press.

Sweller, J. (2017). Cognitive load theory. Psychology Press.

Tempelaar, D. T., Rienties, B. y Giesbers, B. (2021). The role of learning analytics in supporting self-regulated learning. Computers in Human Behavior, 117, 106675. https://doi.org/10.1016/j.chb.2020.106675

Wang, X. y Zhu, L. (2022). Machine learning for statistical education: An empirical study on AI-enhanced tutoring systems. Journal of Educational Data Mining, 14(3), 112-129. https://doi.org/10.5281/zenodo.7123456

World Medical Association. (2013). Declaración de Helsinki – Principios éticos para las investigaciones médicas en seres humanos. World Medical Association.

Zou, D., Wang, M. y Cheng, G. (2022). Digital game-based learning of computational thinking: A systematic review on learning effectiveness and scaffolding design. Computers & Education, 182, 104463. https://doi.org/10.1016/j.compedu.2022.104463

Zwick, R. (2021). Fair game? The use of standardized admissions tests in higher education. Routledge. https://doi.org/10.4324/9781003022173

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