Machine Learning (ML) and Artificial Intelligence (AI) have rapidly emerged as transformative forces within the educational landscape, offering unprecedented opportunities to innovate teaching and learning processes.
The systematic review by Forero-Corba and Negre Bennasar explores how ML and AI are being integrated into education across various global contexts. Drawing on a dataset of 55 high-impact studies conducted between 2021 and 2023, this article provides a comprehensive analysis of techniques and applications of ML and AI in primary, secondary, and higher education.
The review identifies 33 ML and AI techniques employed to address challenges such as academic performance prediction, dropout rates, and tailored learning interventions.
These studies reveal significant advancements in applying AI-powered tools, including intelligent tutoring systems, adaptive learning platforms, and predictive analytics, which enhance student outcomes and streamline educational management.
The findings also underscore the importance of equipping educators with the skills to implement and leverage these technologies effectively.
Moreover, this research highlights critical trends, including a shift toward inclusive AI applications that support diverse learners, such as those with special needs.
By showcasing the potential of ML and AI to close educational gaps, the review positions these technologies as pivotal in shaping the future of education, emphasizing the need for ongoing research and professional development to harness their full potential.
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How to cite: Forero-Corba, W., & Negre Bennasar, F. (2024). Techniques and applications of Machine Learning and Artificial Intelligence in education: a systematic review. [Técnicas y aplicaciones del Machine Learning e Inteligencia Artificial en educación: una revisión sistemática]. RIED-Revista Iberoamericana de Educación a Distancia, 27(1), 209-253. https://doi.org/10.5944/ried.27.1.37491
