AI-Based Personalized E-Learning Systems: Issues, Challenges, and Solutions

Abstract

A personalized e-learning system is effective in imparting enhanced learning to its users. As compared to a conventional e-learning system, which provides similar contents to each learner, a personalized learning system provides specific learning contents and assessments to the learners. Personalization is based on Artificial Intelligence (AI) based techniques in which appropriate contents for each learner are determined using the level of comprehension of the learner and the preferred modes of learning. This paper presents requirements and challenges for a personalized e-learning system. The paper is focused in elaborating four research questions, which are related to identifying key factors of personalized education, elaborating on state of the art research in the domain, utilizing benefits of AI in personalized education, and determining future research directions. The paper utilizes an extensive survey of current research papers in answering these questions. It provides a comprehensive review of existing solutions in offering personalized e-learning solutions. It also elaborates on different learning models and learning theories, which are significant in providing personalized education. It proposes an efficient framework, which can offer personalized elearning to each learner and identifies significant directions for future research. The paper is beneficial for academicians and researchers in understanding the requirements of such a system, comprehending its methodologies, and identifying challenges which are needed to be addressed.

Artificial intelligence in personalised learning: a bibliometric analysis

“…The findings of this study present an overview of the research fronts and intellectual bases, as well as the evolution of research hotspots and recent trends in the field of AI-supported personalised learning. They complement related reviews which addressed AI in education in general (Chen et al, 2020b;Murtaza et al, 2022) or specific areas such as relevant technologies (Chu et al, 2022) and subject disciplines (Hwang and Tu, 2021), by highlighting the evolution of research on personalisation in learning with the support of advances in AI along the years.…”

“…Chen et al (2022a) examined the research topics related to AI applications in education and identified the trends of the topics along the years and future directions. The review of Murtaza et al (2022) addressed the development of AI-based personalised e-learning by surveying relevant systems which feature the provision of specific learning contents and assessments to learners according to their knowledge level and preferred mode of learning and summarising the learning models and theories as well as the requirements and challenges involved in these systems.…”

“…Despite a lot of studies have been done into the use of AI to support personalised learning, related reviews have not yet adequately addressed this area. Related reviews only covered AI in education in general (Chen et al , 2020b; Murtaza et al , 2022), specific AI technologies for education, such as AI-based robots (Chu et al , 2022) and intelligent tutoring systems (ITSs) (Mousavinasab et al , 2021), AI for education in specific regions (Salas-Pilco and Yang, 2022) or subject disciplines (Hwang and Tu, 2021). There has been a lack of systematic analysis of studies on using AI for the personalisation of learning.…”

Artificial Intelligence Revolution: Shaping the Future of Millennials

“…AI-integrated personalization is a method in which suitable content for each learner is established by grasping the learner's understanding and his or her preferred mode of learning. It is quite different from the traditional e-learning system, which provides similar content (Murtaza, Ahmed, Shamsi, Sherwani, & Usman, 2022). Learning abilities vary from one student to another.…”

AI in Educational Technology

“…Reference source not found., AI algorithms are employed to collect and analyse data on how students interact with learning materials and assessments. This data is then used to create a profile of each learner's progress and comprehension [46]. Based on this profile, the system can recommend suitable learning resources, adjust the level of difficulty, and provide targeted feedback to help students overcome challenges and reinforce their strengths [47].…”