Analyzing Emotional Expressions in ASD Education Using Neutrosophic Set Theory for Effective Classroom Inclusion.

Abstract

The detection of emotions in children with Autism Spectrum Disorder (ASD) presents a key challenge: how to interpret emotional expressions to foster effective inclusion in mainstream educational settings. This topic is particularly relevant in the current context, given the growing emphasis on inclusive education and the need to adapt pedagogical methodologies to the specific characteristics of students with ASD. Although existing literature addresses emotion recognition through computer vision, it lacks approaches that integrate the inherent uncertainty of human emotions within educational contexts. To address this gap, the present study employs Neutrosophic Set Theory, a mathematical tool that models indeterminacy, along with computer vision techniques based on convolutional neural networks, to analyze facial expressions in real time. The results reveal that this approach enables accurate classification of emotions such as happiness, neutrality, and fear, providing valuable data to adjust pedagogical strategies. This research contributes to the field by proposing a novel theoretical framework that combines neutrosophy and technology to enhance educational inclusion. Additionally, it provides teachers with practical tools to personalize instruction, promoting a more equitable and emotionally supportive learning environment for students with ASD.