Development of an Interdisciplinary Smart Learning Ecosystem Using Artificial Intelligence, Embedded Electronics, Communication Engineering, and Educational Management for Special Needs Education

Abstract

The study focuses on developing a multidisciplinary smart learning ecosystem that encompasses artificial intelligence, embedded electronics, communication engineering, and educational management systems, aimed at enhancing adaptive instructional support in special needs education. However, a significant research gap still exists, as contemporary assistive education technologies are mainly confined to standalone computerized applications, which hamper the coordinated analysis of learner interactions, communication responses, and academic plans within the institution. The smart learning ecosystem model fills this gap through embedding electronics for assessing classroom participation, applying artificial intelligence for estimating learning response variability, and coordinating instruction with communication supported by educational management activities. The statistical verification involves conducting multivariate variance analysis, assessing predictive reliability, and performing significance tests on various educational performance indicators. The findings reveal enhanced consistency of interventions, more rapid instructional adaptations, and closer correlations between classroom assistance and educational decisions than fragmented assistive approaches. This multidisciplinary approach offers a practical smart learning ecosystem for special needs education without relying on ethical learner diagnostics.