Machine learning as a teaching strategy education: A review

Abstract

In this article, we present a systematic review of the literature that explores the impact of Machine Learning as a teaching strategy in the educational field. Machine Learning, a branch of artificial intelligence, has gained relevance in teaching and learning due to its ability to personalize education and improve instructional effectiveness. The systematic review focuses on identifying studies investigating how Machine Learning has been used in educational settings. Through a thorough analysis, its impact on various areas related to teaching and learning, including student performance, knowledge retention, and curricular adaptability, is examined. The findings of this review indicate that Machine Learning has proven to be an effective strategy for tailoring instruction to individual student needs. As a result, engagement and academic performance are significantly improved. Furthermore, the review underscores the importance of future research. This future research will enable a deeper understanding of how Machine Learning can optimize education and address current challenges and emerging opportunities in this evolving field. This systematic review provides valuable information for educators, curriculum designers, and educational policymakers. It also emphasizes the continuing need to explore the potential of Machine Learning to enhance teaching and learning in the digital age of the 21st century.