An empirical study for the dynamic and personalised learning experience of the AI course generator - Faza Amal, Sophian; Abu Saiid, Ismail; Mansor, Hafizah (IIUM Press, 2024)
In a world that is quickly evolving, the demand for continuous learning and upskilling is critical for personal and professional growth. However, many learners struggle to create personalised, efficient learning paths tailored to their unique needs due to the limitations of traditional course creation methods, which require significant human input and expertise. This project aims to address this problem by developing "modulo," an innovative platform designed to automate the creation of personalised and structured learning paths. The objectives of “modulo” are to leverage artificial intelligence and external APIs to generate customised study plans for any chosen subject, integrate curated YouTube tutorials and supplemental materials, and enhance the learning experience with adaptive quizzes tailored to user progress. The methodology follows an Iterative-Waterfall approach, combining structured phases with iterative cycles to incorporate feedback and adapt to emerging challenges. The system architecture is built on a microservices framework, with a frontend developed using React and Next.js, and a backend supported by Supabase with Prisma for database management, NextAuth for user authentication, and Stripe for payment processing. The result is a scalable and maintainable platform that empowers diverse user groups by enhancing education accessibility. “modulo” provides a dynamic and personalised learning experience, making a meaningful impact on self-directed learning.