Comprehensive Survey on Voice-Enabled Semantic Grading System for Visually Impaired Students

DOI

10.2991/978-94-6239-616-6_25How to use a DOI?

Keywords

Accessibility; Independence; Privacy; Secure exam; Voice-based grading; React Native Application; Visually impaired Learners

Abstract

Students who are visually impaired often encounter serious obstacles during examinations, mainly because most existing platforms do not offer truly accessible and independent exam-taking experiences. Typically, blind students must rely on scribes or assistants who read questions and write answers for them a practice that can introduce bias, risk privacy, and undermine the integrity of the process. For example, reliance on scribes means that students may not be graded entirely on their own knowledge, and privacy issues arise when sensitive information is shared during exams. These limitations affect students’ ability to participate confidently and independently. To address these problems, a cross-platform mobile application was designed using React Native. This app enables visually impaired students to take exams through voice commands or text input, removing the need for human assistance during the exam. By providing both audio and text response options, the system improves accessibility and fosters greater independence. Beyond facilitating user input, the application promptly evaluates answers using straightforward natural language techniques and provides immediate feedback, supporting fairness and confidentiality. The main goal is to replace manual, scribe based approaches with a secure, private, and user-friendly grading environment.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - R. Raju
AU  - R. Aiswarya
AU  - S. Samson Sambavi
AU  - Rukku Kanakadas
PY  - 2026
DA  - 2026/03/31
TI  - Comprehensive Survey on Voice-Enabled Semantic Grading System for Visually Impaired Students
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 299
EP  - 306
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_25
DO  - 10.2991/978-94-6239-616-6_25
ID  - Raju2026
ER  -