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The application of artificial intelligence and machine learning techniques has grown significantly across all disciplines in recent years as a result of ever-increasing volume of data as well as changing demands of higher education, such as those related to digital education. In many countries around the world, learning disabilities (LD) are becoming a major problem that can even hinder performance progress. The endeavor of this work is to assist the specific program with systems administration in their assignment to accompany the norm. Nowadays, many different fields use machine learning to predict future outcomes. Predicting children's learning disabilities, as well as determining actual disability and how early it is detected, are among most useful applications of machine learning. This review article aims to comprehend work done on this field previously, up to this point and attempt to comprehend the holes in execution of the different AI models and the review depends on intelligent school system by artificial reasoning. Countless examinations validate that learning is worked with assuming showing systems are as per understudies learning styles, making the growing experience more compelling and further developing understudies exhibitions.

Computer Assisted Instruction (CAI) is considered as an effective teaching-learning strategy for the effective plan, written and programmed in the computer that can be run in a several computer units and allows every student computer terminal. The instruction are programmed with the implementation of a computer disc (CD) played in audio, video, gaming, drag & drop, and simulation activity of students in their own pace. In the contemporary world the CAI is not underestimated with the instructional computer value. In human body disability is observed as the difficulty of the human body function. In Learning Disability (LD) is heterogeneous disorder groups with significant difficulties in acquisition for listening, speaking, reading, writing and reasoning. This research examined the contribution of CAI on the LD students. The analysis is based on the data collected from the LD diagnosed school students. The data were collected from the Special school in Riyadh in Saudi Arabia with the data collected from 245 sample students. The experimental analysis is conducted for the experimental and control group. The analysis is conducted based on the consideration of three variable such as hand-eye co-ordination, attention and academic performance. The analysis of data expressed that through CAI model the LD student eye-hand co-ordination, attention and academic performance is improved.