Early Detection of Students at High Risk of Academic Failure using Artificial Intelligence

The academic performance of students in Mexico has a great impact on the social and economic development of the country. Early detection of students at academic risk is necessary to improve educational quality and reduce school dropouts. This work presents a proposal that uses a predictive model based on Logistic Regression to identify students at high risk of academic failure and its usefulness to provide proactive and personalized support to those who need it. In addition, an overview of the impact of Artificial Intelligence and Machine Learning in education is presented, especially in predicting student dropout and supporting academic performance, allowing us to take an important step towards a more promising and successful educational future for students. students.