AI-Driven Learning Analytics: Enhancing Personalized Learning Pathways on Mobile Platforms

Humanities

This research investigates the application of Artificial Intelligence (AI) driven learning analytics to create personalized learning pathways for students using mobile platforms. It addresses the need for tailored educational experiences that cater to individual student needs and preferences. By leveraging AI technologies such as machine learning and natural language processing, this research aims to enhance mobile learning environments and improve student outcomes by offering significant potential to transform educational paradigms. The researchers explore the theoretical foundations of learning analytics and the role of AI in educational settings, examining the benefits of personalized learning, which increases students’ engagement, motivation, and academic success. Mobile platforms are identified as ideal tools for delivering personalized learning experiences due to their accessibility and convenience. Developing AI algorithms is central to this study to analyse student data, including performance metrics, behavioural data, and interaction patterns. By processing this data, the AI system identifies individual learning styles, strengths, and areas for improvement. In the implementation phase, the researchers address challenges associated with deploying AI-driven learning analytics on mobile platforms, including data integration, algorithm accuracy, and user-friendly interfaces. The researchers outline best practices for safeguarding student information and ensuring compliance with regulations, discussing the ethical implications of AI in education. The research includes case studies illustrating the practical applications of AI-driven learning analytics in different educational contexts, highlighting successes and challenges and offering valuable lessons for future initiatives. The researchers explore future directions and potential innovations in AI-driven personalized learning, considering trends in mobile technology and AI, such as augmented reality and adaptive learning systems. By addressing technical, ethical, and practical considerations, the study provides a comprehensive framework for implementing personalized learning in diverse educational settings and offers a roadmap for future research and development.

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