Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
IJSREM64573
Volume:
10 |
6 |
Jun 2026
Pages:
1-2
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

MS.c computer Science
Modern college of arts, science and commerce shivajinagar

MS.c computer Science
Modern college of arts, science and commerce shivajinagar

MS.c computer Science
Modern college of arts, science and commerce shivajinagar
MS.c computer Science
Modern college of arts, science and commerce shivajinagar
Dyslexia is a learning disability where there are impairments in reading, writing, and spelling skills in children. This paper presents the development of a web application that performs dyslexia diagnosis using artificial intelligence techniques. In the developed system, behavioral measurements including reading speed, spelling accuracy, phonemic ability, attention, and reaction time are measured to determine dyslexia risk using supervised machine learning models. This web application is intended to be cost-effective and applicable in educational environments without any expensive infrastructure.
KEYWORDS
This paper outlines the development of a dyslexia early detection framework using machine learning, aimed at educational screening by way of behavioral study. The proposed web-based application offers a cost-effective tool that helps identify potential cases of dyslexia among students requiring extra educational support. The results emphasize the increasing use of artificial intelligence in educational technology.
[1] M. J. Snowling, Dyslexia, 2nd ed. Oxford, U.K.: Blackwell, 2000. [2] S. E. Shaywitz, Overcoming Dyslexia. New York, USA: Alfred A. Knopf, 2003. [3] C. M. Bishop, Pattern Recognition and Machine Learning. Springer, 2006. [4] T. M. Mitchell, Machine Learning. McGraw-Hill, 1997. [5] L. Breiman, “Random Forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001. [6] L. Rello et al., “Detecting Dyslexia Using Eye-Tracking Measures,” Proc. ACM SIGACCESS, 2015. [7] L. Zhou and C. Yu, “Early Prediction of Developmental Dyslexia Using Machine Learning,” Frontiers in Psychology, vol. 12, 2021.
Publication Date
6/6/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
0
Total views since publication
PDF Downloads
3
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...