Citation

Qazi, Sameer and Kadri, Muhammad Bilal and Naveed, Muhammad and Khawaja, Bilal A. and Khan, Sohaib Zia and Alam, Muhammad Mansoor and Mohd Su'ud, Mazliham (2024) AI-Driven Learning Management Systems: Modern Developments, Challenges and Future Trends during the Age of ChatGPT. Computers, Materials & Continua, 80 (2). pp. 3289-3314. ISSN 1546-2226

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Abstract

COVID-19 pandemic restrictions limited all social activities to curtail the spread of the virus. The foremost and most prime sector among those affected were schools, colleges, and universities. The education system of entire nations had shifted to online education during this time. Many shortcomings of Learning Management Systems (LMSs) were detected to support education in an online mode that spawned the research in Artificial Intelligence (AI) based tools that are being developed by the research community to improve the effectiveness of LMSs. This paper presents a detailed survey of the different enhancements to LMSs, which are led by key advances in the area of AI to enhance the real-time and non-real-time user experience. The AI-based enhancements proposed to the LMSs start from the Application layer and Presentation layer in the form of flipped classroom models for the efficient learning environment and appropriately designed UI/UX for efficient utilization of LMS utilities and resources, including AI-based chatbots. Session layer enhancements are also required, such as AI-based online proctoring and user authentication using Biometrics. These extend to the Transport layer to support real-time and rate adaptive encrypted video transmission for user security/privacy and satisfactory working of AI-algorithms. It also needs the support of the Networking layer for IP-based geolocation features, the Virtual Private Network (VPN) feature, and the support of SoftwareDefined Networks (SDN) for optimum Quality of Service (QoS). Finally, in addition to these, non-real-time user experience is enhanced by other AI-based enhancements such as Plagiarism detection algorithms and Data Analytics.

Item Type:	Article
Uncontrolled Keywords:	Internet of Things (IoT); artificial intelligence (AI)
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	02 Sep 2024 06:54
Last Modified:	02 Sep 2024 06:54
URII:	http://shdl.mmu.edu.my/id/eprint/12900

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