AI-BASED LEARNING TOOLS AND PLATFORMS: RECENT LIMITATIONS AND MITIGATION METHODS

Abstract

The integration of artificial intelligence (AI) in educational environments has significantly transformed how students access, interact with, and engage in learning. AI-based learning tools and platforms offer personalized, adaptive, and interactive learning experiences that can improve motivation, engagement, and learning outcomes. However, despite these advantages, the widespread adoption of AI in education faces several limitations, including technical challenges, algorithmic biases, data privacy concerns, and issues related to teacher and student adoption. This paper aims to explore the recent limitations of AI educational tools and to examine mitigation methods that can address these challenges effectively. By reviewing current literature, case studies, and practical implementations, this study identifies key challenges affecting both students and educators, while also highlighting strategies such as algorithmic transparency, targeted user training, privacy safeguards, and pedagogical integration. The findings provide insights for developers, educators, and policymakers to optimize AI-based platforms, ensuring that they contribute positively to students’ learning experiences and educational outcomes. Keywords: AI-based learning tools, Educational platforms, Limitations and mitigation methods, Student learning experiences, Adaptive learning.

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