MSc in Information Technology

Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/2484

Students enrolled in the MSc in Information Technology programme are required to submit a thesis as a compulsory component of their degree requirements. This collection features merit-based theses submitted by postgraduate students specialising in Information Technology. Abstracts are available for public viewing, while the full texts can be accessed on-site within the library.
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Theses and Dissertations of the Sri Lanka Institute of Information Technology (SLIIT) are licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License .

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Author: search.filters.author.Nishamali, M.K.C.P.Subject: search.filters.subject.Integration

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    Development and Integration of an AI-Driven PHP Adapter for Automated Mathematical Question Classification and Assessment: Enhancing Student Profiling and Feedback Mechanisms
    (SLIIT, 2024-12) Nishamali, M.K.C.P.
    The transformative growth of AI can be seen in almost every sector. AI can be a useful application for the educational domain as well. This research aims to combine IT to develop mathematics subjects by leveraging AI in practice mainly introducing Capabilities of Open AI. The primary objective is to create OpenAI API through a specially created PHP adapter to classify mathematical questions into six main themes Sets and Probability, Algebra, Numbers, Geometry, Measurements, and Statistics. This automated AI-driven classification system helps to create online assessments within the blink of an eye. The Integration of Open AI API with a PHP-based framework makes a bridge between AI capabilities and education needs. This framework is the ideal solution for manual and traditional school assessments. This plugin can be implemented in other university-level courses as well. The sample of the adapter plugin is only created and tested for secondary school mathematics classes for grade 10. This AI-driven mathematics classification system is designed to optimize the assessment process by providing additional objectives such as leveraging automated student grading feedback so teachers and students can see the result instantly. Additionally, answers are automatically generated after the assessment, displaying the solving steps that help students identify their mistakes. Meanwhile, this system also predicts the student’s mathematics pass mark based on the results of the tests taken from this system.