Conceptual Framework for AI Emotional Recognition in Children with Learning Difficulties for Paediatric Screening
DOI:
https://doi.org/10.65150/EP-gjetr/V2E5/2026-05Keywords:
Achievement of Emotion, Affective Computing, CNN, Dyslexia, Facial Emotional Recognition, Machine LearningAbstract
Assessing the emotional states of children with learning difficulties (LD) is critical for effective pedagogical and clinical intervention. However, existing diagnostic methods rely on subjective observations which are prone to human bias and lack a localised Asian-centric perspective. This concept paper proposes an AI-based Facial Emotional Recognition (FER) initiative designed to provide objective, real-time assessment of emotional engagement in children with dyslexia during learning activities. The framework utilises a mixed-methods approach, combining a Convolutional Neural Network (CNN) pipeline for video-to-image emotion classification with clinical field validation at the SPARK Child Development Centre. The system leverages 3D depth-sensing technology via Intel RealSense to extract 468 facial landmarks. Preliminary validation of the initiative’s expert-rated dataset achieved a Cohen’s Kappa of 0.87, indicating excellent inter-rater reliability. By bridging the gap between traditional psychiatric screening and modern affective computing, this digitalisation initiative supports the SDG 4 goals, offering a scalable tool for more precise and timely support for children with learning disabilities.
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Copyright (c) 2026 Marcella Peter, Jackie Ting Tiew Wei, Khairunnisa Ibrahim (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
