Bridging the Gap: A systematic review of AI-powered smart learning Systems for addressing diverse student learning needs

Cheng Aihua () and Tsai Cheng-Chung ()

Edelweiss Applied Science and Technology, 2025, vol. 9, issue 4, 1423-1436

Abstract: The rapid evolution of artificial intelligence (AI) has transformed educational practices, with AI-powered smart learning systems (AI-SLS) emerging as powerful tools for addressing diverse student learning needs. This systematic review aims to assess the current state of research on AI-SLS, focusing on their key features, effectiveness, implementation challenges, and optimization strategies. Adhering to the PRISMA guidelines, the review analyzed peer-reviewed studies and grey literature from 2010 to 2023, employing a rigorous methodology to ensure transparency and reproducibility. The findings reveal that AI-SLS, through adaptive algorithms, natural language processing, and data-driven analytics, significantly enhance personalized and inclusive learning. However, their effectiveness is contingent on equitable access, teacher readiness, and alignment with pedagogical goals. Ethical concerns, technical limitations, and institutional resistance were identified as major barriers to implementation. To address these challenges, the review proposes strategies such as developing ethical guidelines, investing in infrastructure, and fostering stakeholder collaboration. The study contributes to the literature by highlighting the integration of advanced functionalities like emotion recognition and gamification, which represent a significant evolution in AI-SLS. Furthermore, it emphasizes the need for context-sensitive designs and scalable solutions to ensure inclusivity. By aligning technological advancements with ethical principles and practical considerations, this review provides actionable insights for educators, policymakers, and developers, ultimately advancing the goal of creating equitable and effective learning environments for all students.

Keywords: Adaptive algorithms; AI-powered smart learning systems; Ethical challenges; Implementation barriers; Inclusive education; Optimization strategies; Personalized learning. (search for similar items in EconPapers)
Date: 2025
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