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An empirical study for the dynamic and personalised learning experience of the AI course generator

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 crea...

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Main Authors: Faza Amal, Sophian, Abu Saiid, Ismail, Mansor, Hafizah
Format: Article
Language:English
Published: IIUM Press 2024
Subjects:
T Technology (General)
Online Access:http://irep.iium.edu.my/113514/2/113514_An%20empirical%20study%20for%20the%20dynamic.pdf
http://irep.iium.edu.my/113514/
https://journals.iium.edu.my/kict/index.php/IJPCC/issue/view/41
https://doi.org/10.31436/ijpcc.v10i2.483
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spelling my.iium.irep.1135142024-07-31T03:04:51Z http://irep.iium.edu.my/113514/ An empirical study for the dynamic and personalised learning experience of the AI course generator Faza Amal, Sophian Abu Saiid, Ismail Mansor, Hafizah T Technology (General) 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. IIUM Press 2024-07-30 Article PeerReviewed application/pdf en http://irep.iium.edu.my/113514/2/113514_An%20empirical%20study%20for%20the%20dynamic.pdf Faza Amal, Sophian and Abu Saiid, Ismail and Mansor, Hafizah (2024) An empirical study for the dynamic and personalised learning experience of the AI course generator. International Journal on Perceptive and Cognitive Computing (IJPCC), 10 (2). pp. 23-30. E-ISSN 2462-229X https://journals.iium.edu.my/kict/index.php/IJPCC/issue/view/41 https://doi.org/10.31436/ijpcc.v10i2.483
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Faza Amal, Sophian
Abu Saiid, Ismail
Mansor, Hafizah
An empirical study for the dynamic and personalised learning experience of the AI course generator
description 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.
format Article
author Faza Amal, Sophian
Abu Saiid, Ismail
Mansor, Hafizah
author_facet Faza Amal, Sophian
Abu Saiid, Ismail
Mansor, Hafizah
author_sort Faza Amal, Sophian
title An empirical study for the dynamic and personalised learning experience of the AI course generator
title_short An empirical study for the dynamic and personalised learning experience of the AI course generator
title_full An empirical study for the dynamic and personalised learning experience of the AI course generator
title_fullStr An empirical study for the dynamic and personalised learning experience of the AI course generator
title_full_unstemmed An empirical study for the dynamic and personalised learning experience of the AI course generator
title_sort empirical study for the dynamic and personalised learning experience of the ai course generator
publisher IIUM Press
publishDate 2024
url http://irep.iium.edu.my/113514/2/113514_An%20empirical%20study%20for%20the%20dynamic.pdf
http://irep.iium.edu.my/113514/
https://journals.iium.edu.my/kict/index.php/IJPCC/issue/view/41
https://doi.org/10.31436/ijpcc.v10i2.483
_version_ 1806442141460725760
score 13.211869

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