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In recent years, advancements in Natural Language Processing (NLP) techniqueshave revolutionized the field of accessibility and exclusivity of testing,particularly for visually impaired students (VIS). CBT has shown in years backits relevance in terms of administering exams electronically, making the testprocess easier, providing quicker and more accurate results, and offeringgreater flexibility and accessibility for candidates. Yet, its relevance wasnot felt by the visually impaired students as they cannot access printeddocuments. Hence, in this paper, we present an NLP-driven Computer-Based Testguide for visually impaired students. It employs a speech technologypre-trained methods to provide real-time assistance and support to visuallyimpaired students. The system utilizes NLP technologies to convert thetext-based questions and the associated options in a machine-readable format.Subsequently, the speech technology pre-trained model processes the convertedtext enabling the VIS to comprehend and analyze the content. Furthermore, wevalidated that this pre-trained model is not perverse by testing for accuracyusing sample audio datasets labels (A, B, C, D, E, F, G) to compare with thevoice recordings obtained from 20 VIS which is been predicted by the system toattain values for precision, recall, and F1-scores. These metrics are used toassess the performance of the pre-trained model and have indicated that it isproficient enough to give its better performance to the evaluated system. Themethodology adopted for this system is Object Oriented Analysis and DesignMethodology (OOADM) where Objects are discussed and built by modelingreal-world instances.

Development of an NLP-driven computer-based test guide for visually impaired students