Early Detection of Learning Difficulties: Predictive Analytics through Artificial Intelligence in Basic Education
DOI:
https://doi.org/10.5281/zenodo.19871379Keywords:
Artificial Intelligence, Learning Difficulties, Predictive Analytics, Educational Data Mining, School Prevention, Educational Innovation.Abstract
This research article analyzes the viability and ethical impact of integrating Artificial Intelligence (AI) systems based on predictive analytics for the early detection of Specific Learning Difficulties (SLD) at the Basic Education level. Historically, the educational system has operated under a reactive "wait-to-fail" model, where psycho-pedagogical
intervention occurs only after repeated academic setbacks. The objective of this study is to propose a methodological framework for the use of Educational Data Mining (EDM) and Machine Learning algorithms capable of identifying anomalous learning patterns before the lag is consolidated. Through a descriptive-projective design and a systematic literature review, matrices of predictive variables were structured (reading fluency, interactive response times, attendance, and grade variance). Theoretical findings suggest that predictive models can alert about risks of dyslexia, dyscalculia, or ADHD up to 18 months earlier compared to traditional teacher referral. It is concluded that AI should not replace the clinical diagnosis of the Student Counseling Department (DECE), but rather act as a systematic screening tool that transforms passive school data into actionable early warnings, guaranteeing the right to timely pedagogical intervention.
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