Design and Evaluation of a Rule-Based Expert System for Early Screening of Learning Disabilities in Educational Settings

ANDRADE-ARENAS L.; VILLAR-MAYUNTUPA G.; ALVARADO-PAUCAR A.; RAMOS-COSI S.; HUAMANI-URIARTE E.; ALVA-MANTARI A. · Ingenierie des Systemes d'Information (2026) · DOI: 10.18280/isi.310706

Resumen

Learning disabilities, including dyslexia, dyscalculia, and dysgraphia, can negatively affect students’ academic development when they are not identified at an early stage. This study proposes a rule-based expert system to assist the early screening of learning disabilities in educational environments. The system was developed through expert knowledge representation based on the Buchanan methodology and implemented using the Experta inference engine, Flask framework, and PostgreSQL database. The proposed system integrates pedagogical rules to evaluate learning-related indicators and generate classification outcomes for potential learning difficulties. Its performance was assessed using 90 test cases, consisting of 60 positive and 30 negative cases, through a confusion matrix and corresponding evaluation metrics. The system achieved an accuracy of 91%, precision of 100%, sensitivity of 87%, specificity of 100%, and an F1-score of 93%. The results demonstrate that the proposed approach can effectively identify potential learning difficulties while avoiding false-positive classifications. By combining artificial intelligence techniques with educational expertise, the developed system provides a practical decision-support approach for teachers and educational practitioners involved in early learning disability screening.

Instituciones: Universidad de Ciencias y Humanidades