Identification of priority cases for attention to the academic performance of students by subject

Identifying priority cases to improve students' academic performance by subject has gained relevance in modern education, and artificial intelligence (AI) emerges as a fundamental tool to achieve this. Through cluster analysis, an unsupervised learning technique, it is possible to group students according to specific performance patterns and needs. This allows educational institutions to quickly recognize which cases require priority attention and focus their resources efficiently. Cluster analysis makes it possible to identify segments of students with similar characteristics in their academic performance, revealing aspects such as recurring difficulties in certain subjects, learning patterns, and factors that affect performance. With these defined groups, teachers and administrators can establish personalized and effective intervention strategies. For example, specific support programs can be developed for students facing problems in mathematics or science, rather than applying general solutions. AI applied in this context not only helps to categorize students, but also allows for a more accurate prediction of who may need additional support in the short and long term. Furthermore, by identifying common factors within each cluster, it is possible to improve teaching methods and design curricula that are more adapted to the real needs of students. This practice not only promotes inclusive and student-centered education, but also optimizes the use of academic resources by focusing efforts on the most urgent cases. The application of cluster analysis in the identification of priority cases for academic attention represents a key innovation in the educational sector, allowing for more personalized, inclusive and effective education, supported by artificial intelligence.