Technical Review: Architecting an AI-Driven Decision Support System for Enhanced Online Learning and Assessment

Saipunidzam Mahamad (), Yi Han Chin, Nur Izzah Nasuha Zulmuksah, Md Mominul Haque, Muhammad Shaheen and Kanwal Nisar
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Saipunidzam Mahamad: Department of Computing, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia
Yi Han Chin: Department of Computing, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia
Nur Izzah Nasuha Zulmuksah: Department of Computing, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia
Md Mominul Haque: Department of Computing, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia
Muhammad Shaheen: Faculty of Engineering & IT, Foundation University Islamabad, Rawalpindi 46000, Pakistan
Kanwal Nisar: Faculty of Engineering & IT, Foundation University Islamabad, Rawalpindi 46000, Pakistan

Future Internet, 2025, vol. 17, issue 9, 1-28

Abstract: The rapid expansion of online learning platforms has necessitated advanced systems to address scalability, personalization, and assessment challenges. This paper presents a comprehensive review of artificial intelligence (AI)-based decision support systems (DSSs) designed for online learning and assessment, synthesizing advancements from 2020 to 2025. By integrating machine learning, natural language processing, knowledge-based systems, and deep learning, AI-DSSs enhance educational outcomes through predictive analytics, automated grading, and personalized learning paths. This study examines system architecture, data requirements, model selection, and user-centric design, emphasizing their roles in achieving scalability and inclusivity. Through case studies of a MOOC platform using NLP and an adaptive learning system employing reinforcement learning, this paper highlights significant improvements in grading efficiency (up to 70%) and student performance (12–20% grade increases). Performance metrics, including accuracy, response time, and user satisfaction, are analyzed alongside evaluation frameworks combining quantitative and qualitative approaches. Technical challenges, such as model interpretability and bias, ethical concerns like data privacy, and implementation barriers, including cost and adoption resistance, are critically assessed, with proposed mitigation strategies. Future directions explore generative AI, multimodal integration, and cross-cultural studies to enhance global accessibility. This review offers a robust framework for researchers and practitioners, providing actionable insights for designing equitable, efficient, and scalable AI-DSSs to transform online education.

Keywords: artificial intelligence (AI); decision support systems (DSSs); online learning; automated assessment; personalized learning (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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