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All Journal International Journal of Technology and Modeling
Amoako, Kwame
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Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics

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AI-Powered Tools for Personalized Learning in Educational Technology Amoako, Kwame; Asante, Akosua; Owusu, Kwabena
International Journal of Technology and Modeling Vol. 3 No. 1 (2024)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v3i1.115

Abstract

In the digital era, the integration of Artificial Intelligence (AI) in educational technology has opened new avenues for optimizing the learning process through personalized approaches. This article proposes an innovative AI-based framework that combines predictive analytics, dynamic modelling of student learning profiles, and adaptive algorithms to craft learning experiences tailored to individual needs. The research methodology encompasses a systematic literature review, empirical case studies, and controlled experiments to evaluate the effectiveness of AI-powered educational tools. Findings indicate that this personalized approach significantly enhances student engagement, knowledge retention, and academic performance compared to traditional methods. The primary contribution of this study lies in the development of a flexible and scalable personalization model, alongside strategic AI integration practices applicable across diverse educational settings. These insights not only underscore the transformative potential of AI in education but also lay the groundwork for developing technology-driven solutions that address individual learning requirements and mitigate disparities in access to quality education.
Co-Authors Asante, Akosua Owusu, Kwabena