Beyond the One-Size-Fits-All: A Systematic Review of Personalized and Gamified e-Learning for Neurodivergent Learners

Abstract

Traditional education, characterized by rigid curricula and inflexible teaching methods, often fails to accommodate the diverse cognitive profiles of neurodivergent learners, including those with Autism Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorder (ADHD), and dyslexia. Although e-Learning has introduced greater flexibility and interactivity into education, many existing platforms continue to adopt a one-size-fits-all approach, primarily catering to neurotypical learners, often overlooking the diverse cognitive and behavioral needs of neurodivergent students. The neurodivergent students frequently encounter challenges related to attention regulation, sensory processing, and retention of information, and these factors are rarely addressed in the design of conventional digital learning environments. While gamification and intelligent technologies have shown promise in enhancing learner engagement and personalization, their application in neurodiverse contexts remains limited and insufficiently customized. This systematic literature review investigates the potential of gamified e-Learning platforms, enhanced by advanced intelligent technologies, to create personalized and inclusive educational experiences for neurodivergent students. Following the PRISMA protocol, this study analyzed 82 studies published between 2020 and 2024 from Scopus, Web of Science, and Google Scholar databases, focusing on gamification in e-Learning and its effectiveness for neurodivergent learners. The findings suggest that traditional e-Learning platforms lack the adaptability and personalization required to engage neurodivergent students effectively. However, emerging approaches—such as adaptive gamification, multisensory content delivery, personalized feedback, and AI-driven analytics—show promise in improving engagement and learning outcomes. Technologies like reinforcement learning and generative AI offer further potential for dynamic content customization. The study identified the pressing need for future research focusing on developing inclusive, personalized, adaptive e-Learning systems and pedagogical models; conducting longitudinal studies on their efficacy; exploring sensory overload and accessibility barriers; evaluating the effectiveness of generative AI and immersive technologies; addressing the digital divide; and ensuring ethical AI-driven personalization.