Abstract:

Numerous gaps in present procedures of identifying dyslexia are the driving force behind the research desire to examine the function of AI in early identification and intervention for dyslexic children. Most of early intervention efforts are hampered by the fact that traditional techniques of diagnosing dyslexia sometimes depend on subjective assessments and may result in a delayed diagnosis. In order to detect dyslexia in its early stages and provide affected pupils with timely support, there is also a need for more effective and precise screening instruments. Furthermore, the efficacy and personalization of current intervention approaches may be lacking, emphasizing the need for creative solutions like artificial intelligence (AI) to customize treatments to meet the needs of specific learning needs. The overall goal of the research is to close these gaps by investigating how artificial intelligence (AI) technology can improve the process of early detection and intervention for dyslexic kids, hence enhancing their educational performance.

The study aims to analyse and clarify the varied functions of artificial intelligence (AI) in the early detection and intervention of students with learning disabilities (Dyslexia). In order to help create more successful and inclusive educational practises, this study intends to provide a thorough understanding of the ways in which AI technologies can improve the early identification and support systems for students who are dealing with learning difficulties (Dyslexia). Research question is: What is the role of AI in early identification and intervention for students with learning disabilities (Dyslexia)?

This research is based on the literature review of available research journals with use of integrated literature review method. The results identified AI tools used to detect dyslexia (such as VRAIlexia with smartphone application that integrates VR (Virtual Reality) and AI and second tool that employs AI algorithms to identify challenges, and DyslexiScan with a Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) models). The AI tools that support intervention use cutting-edge algorithms and machine learning approaches to personalise intervention, modify course materials that help education of these children, deliver immediate feedback and propose alternate strategies. The key benefits and challenges of AI tools used in detection and intervention are discussed in the article. The article concludes with limitations and the future work in this discipline.

Keywords:

AI technology, AI tools, early childhood education, dyslexia.