NWACHUKWU, MARY IFECHUKWU (2026) AI-BASED INTELLIGENT STUDY MATERIAL ORGANIZER AND RECOMMENDATION SYSTEM (using Natural language processing techniques). Other thesis, Godfrey Okoye University, Enugu.
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Abstract
In Nigeria, there has been a tremendous growth in the area of digitizing education and thereby making it easier for the people to have access to academic information. However, this development has created a major problem amongst the students as they face difficulties in organizing their files. These include the problems of cluttered storage, inappropriate file naming and update issues frequently leading to wastage of time, duplication of documents and under-preparedness for examinations. Though there is no dearth of educational applications, most of these address just one issue out of sorting, summarizing or tutoring. The objective of this project is to develop an AI-based intelligent study material organizer and recommendation system named StudyMate. In this project, we aim to design an efficient knowledge management system for the students which will help them in organizing their files easily and in an appropriate manner and also recommend them the relevant material. The design involved the use of the SDLC Waterfall model alongside machine learning. It is a client-server-based application designed for user-friendly purposes. Next.js and TypeScript are the technologies employed for the development of the frontend of the software, the backend relies on Laravel (PHP), while the student data will be stored in a MySQL database. To reduce the high costs associated with the cloud computing resources, it makes use of Vector Space Models to facilitate contextual searches. The AI responsible for conducting tasks such as text extraction and summary generation benefits from the Gemini AI API. In addition, it provides suggestions for topics based on the student's history of reading and keywords using the content-based filtering algorithm. The extensive testing of the StudyMate that has been carried out involving unit testing, integration testing, and usability testing has proven that it is quite efficient. It can easily handle text-based PDF files such as lecture slides and handouts without any complications. It automatically extracts metadata and generates summaries, flashcards, and quizzes in an effortless way. Most importantly, it delivers contextual search results instead of keyword-based results. Finally, it creates an easy-to-use student dashboard that facilitates information retrieval through NLP. In conclusion, the StudyMate prototype shows that AI can seriously cut down on the stress university students feel from juggling academic work. Instead of focusing just on theories or big business software, this project created a practical helper for everyday use. It connects raw data with actual knowledge we can use. For future updates, adding multi-modal features to handle things like audio and video lectures would be great. They could also include Optical Character Recognition (OCR) to read handwritten notes. This way, the system becomes even more helpful and versatile.
| Item Type: | Thesis (Other) |
|---|---|
| Subjects: | Q Science > Q Science (General) |
| Divisions: | Faculty of Arts > Faculty of Law > Faculty of Management and Social Sciences > Faculty of Education > Faculty of Natural and Applied Sciences |
| Depositing User: | MICHAEL MADUBUKO |
| Date Deposited: | 27 Jul 2026 10:26 |
| Last Modified: | 27 Jul 2026 10:26 |
| URI: | http://eprints.gouni.edu.ng/id/eprint/6027 |
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