AI-Powered Tutoring System for Automatic Notes Summarization and Quiz Generation

Authors

Irfan Syafie Nor Afian

Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka (UTeM), Melaka (Malaysia)

Lizawati Salahuddin

Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka (UTeM), Melaka (Malaysia)

Ariff Idris

Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka (UTeM), Melaka (Malaysia)

Fiza Abdul Rahim

Faculty of Artificial Intelligence, Universiti Teknologi Malaysia (UTM), Kuala Lumpur (Malaysia)

Article Information

DOI: 10.47772/IJRISS.2025.910000097

Subject Category: Education

Volume/Issue: 9/10 | Page No: 1156-1164

Publication Timeline

Submitted: 2025-10-02

Accepted: 2025-10-10

Published: 2025-11-05

Abstract

The AI-Powered Tutoring System is developed to address the persistent challenges of limited personalized learning support and cognitive overload that students experience when managing and revising extensive academic materials. Despite the growing use of digital learning tools, few systems effectively integrate automated summarization and quiz generation to promote active, self-directed learning. This study bridges the gap by designing an intelligent tutoring platform that enhances comprehension and engagement through AI-driven automation. The system allows students to upload academic materials such as PDFs, text files, and PowerPoint presentations, which are then processed using DeepSeek’s natural language processing API to generate concise summaries and structured quizzes. The system was developed using a full-stack web architecture comprising React.js for the frontend, Node.js and Express for the backend, and PostgreSQL as the database. Guided by the Agile methodology, the development process was structured into iterative sprints encompassing key phases such as planning, design, development, and testing. The system integrates the DeepSeek API for natural language processing, enabling the platform to provide summarized lecture notes and structured quizzes tailored to each uploaded document. It also includes user authentication, file handling, progress tracking, and a personalized library, allowing users to manage their learning resources effectively. A user acceptance test (UAT) with 31 undergraduate students was conducted using a five-point Likert scale questionnaire. The UAT results show that perceived usefulness, perceived ease of use, capability, trustworthiness, attitude toward the system, and behavioral intention to use had a high acceptance rate. Theoretically, this work contributes to the advancement of AI-driven educational technology by integrating principles from self-directed learning and technology acceptance models. Pedagogically, it offers an innovative approach to improving accessibility, comprehension, and learner autonomy through adaptive AI tools for content summarization and assessment.

Keywords

Artificial intelligence, educational technology, DeepSeek, automated notes summarization

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