Intelligent Cognitive Mirroring Model to Enhance Learning Experience in Autistic Spectrum Disorder Children in Malaysia

In the face of technological developments that have ignited revolutionary waves of change across many industries, the education sector has until recently remained relatively loyal to traditional methods and practices. This stoicism is, however eroding, facilitated in part by the need to rapidly embrace technology during the global Covid-19 pandemic. As such, education is now undergoing a digital transformation. Trending digital teaching technologies are enabling a new variation of learning platforms, one which serves as an enabler to introduce greater equality in the education of children with learning difficulties, particularly those with Autism Spectrum Disorder (ASD). Past researchers have emphasized that the success of a teaching and learning tool for ASD children relies primarily on the use of visual learning as it has been proven that visual thinking can be one of their key strengths. Moreover, children with autism often have faster processing capabilities, thereby opening up new avenues for the acquisition of cognitive development through appropriately designed learning platforms and pedagogies. This research aims to study the requirements by capturing the perspective of stakeholders engaged with the ASD learners. Subsequently, studying the efficacy of the proposed Intelligent Cognitive Mirroring Model (ICMM), which is an extension of the CREST model to enhance the learning experience amongst the ASD Learners. Researchers used a hybrid learning application with integrated AI technology to determine the learners learning experience. The findings suggest that the growing body of evidence that hybrid interventions specifically designed for ASD children can significantly improve the learning process and their learning experience to overcome the inherent difficulties faced by such individuals.

Authors

Institutions

Publication Details

Published
2025-05-09
DOI
https://doi.org/10.1109/itet65804.2025.11100344
Primary Topic
Autism Spectrum Disorder Research
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Intelligent Cognitive Mirroring Model to Enhance Learning Experience in Autistic Spectrum Disorder Children in Malaysia

LI Bo-yue, Chandra Reka Ramachandiran, Imran Medi, Malissa Maria Mahmud et al.
Autism Spectrum Disorder Research
article

Intelligent Cognitive Mirroring Model to Enhance Learning Experience in Autistic Spectrum Disorder Children in Malaysia

LI Bo-yue, Chandra Reka Ramachandiran, Imran Medi, Malissa Maria Mahmud, Geetha Kanaparan, Preethi Subramaniam
article en

Abstract

In the face of technological developments that have ignited revolutionary waves of change across many industries, the education sector has until recently remained relatively loyal to traditional methods and practices. This stoicism is, however eroding, facilitated in part by the need to rapidly embrace technology during the global Covid-19 pandemic. As such, education is now undergoing a digital transformation. Trending digital teaching technologies are enabling a new variation of learning platforms, one which serves as an enabler to introduce greater equality in the education of children with learning difficulties, particularly those with Autism Spectrum Disorder (ASD). Past researchers have emphasized that the success of a teaching and learning tool for ASD children relies primarily on the use of visual learning as it has been proven that visual thinking can be one of their key strengths. Moreover, children with autism often have faster processing capabilities, thereby opening up new avenues for the acquisition of cognitive development through appropriately designed learning platforms and pedagogies. This research aims to study the requirements by capturing the perspective of stakeholders engaged with the ASD learners. Subsequently, studying the efficacy of the proposed Intelligent Cognitive Mirroring Model (ICMM), which is an extension of the CREST model to enhance the learning experience amongst the ASD Learners. Researchers used a hybrid learning application with integrated AI technology to determine the learners learning experience. The findings suggest that the growing body of evidence that hybrid interventions specifically designed for ASD children can significantly improve the learning process and their learning experience to overcome the inherent difficulties faced by such individuals.

Asia Pacific University of Technology & Innovation (MY), Xiamen University Malaysia (MY), Sunway University (MY)
Xiamen University
Quality Education
Openalex Percentile: Top 79%
Autism Spectrum Disorder Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.