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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM64573

Machine Learning-Based Dyslexia Early Detection System

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM64573

Volume:

10 |

6 |

Jun 2026

Pages:

1-2

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

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.

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Authors

Komal Shivaji Nichit

Komal Shivaji Nichit

MS.c computer Science

Modern college of arts, science and commerce shivajinagar

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Sakshi Shankar shirsath

Sakshi Shankar shirsath

MS.c computer Science

Modern college of arts, science and commerce shivajinagar

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Snehal gokul karle

Snehal gokul karle

MS.c computer Science

Modern college of arts, science and commerce shivajinagar

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Mentors

Py

Prof.Manjushree yewale

MS.c computer Science

Modern college of arts, science and commerce shivajinagar

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Abstract

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

Dyslexia Machine Learning Educational Technology Diagnosis Artificial Intelligence Learning Disabilities.

Cite This Article

Komal Shivaji Nichit, Sakshi Shankar shirsath, Snehal gokul karle, Prof.Manjushree yewale, "Machine Learning-Based Dyslexia Early Detection System," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 6, pp. 1-2, Jun 2026.

Full Article/Conclusion

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.

References

[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.

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Publication Date

6/6/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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