Parabelem Tinno Dolf Rompas, Hartentus M. Lalompoh, Suci K. Wagei, Alfrits P. Gunde, Johan Reimon Batmetan

Innovation of Adaptive Learning Based on Artificial Intelligence

Introduction

Innovation of adaptive learning based on artificial intelligence. Explore AI-based adaptive learning as an educational innovation. Achieve personalized, dynamic learning, boosting student motivation, independence, and achievement in the digital era.

Abstract

The development of digital technology encourages transformation across various sectors, including education. Adaptive learning based on Artificial Intelligence (AI) is an innovation that enables the teaching and learning process to take place in a personalized, dynamic manner, and aligned with students’ characteristics. This article discusses the implementation of AI-based adaptive learning as a strategy to improve learning effectiveness through learning needs analysis, curriculum differentiation, and the use of data as the basis for learning recommendations. The research method employed is a literature study by reviewing recent journals and scientific publications. The findings show that AI-based adaptive learning can increase learning motivation, independence, and learning achievement through the delivery of materials aligned with students’ competency levels, learning styles, and learning pace. In conclusion, this innovation becomes a potential solution for improving the quality of education in the digital era, provided that technological infrastructure, teacher readiness, and students’ digital literacy are adequately supported.

Review

This article, "Innovation of Adaptive Learning Based on Artificial Intelligence," addresses a highly pertinent and timely topic in the evolving landscape of digital education. The paper effectively frames AI-based adaptive learning as a transformative innovation capable of delivering personalized, dynamic, and student-centric learning experiences. By discussing its implementation through learning needs analysis, curriculum differentiation, and data-driven recommendations, the authors delineate a clear strategy for enhancing learning effectiveness. The premise that such an approach can align education with individual student characteristics is compelling and central to modern pedagogical discourse. The review highlights significant potential benefits, indicating that AI-based adaptive learning can markedly improve student motivation, independence, and overall learning achievement. This is attributed to its capacity for delivering educational content tailored to individual competency levels, learning styles, and paces. While these findings, drawn from a literature study of recent journals and scientific publications, effectively synthesize existing knowledge, it is important to note the methodological approach. As a literature review, the article excels at consolidating current understanding and identifying trends, offering a valuable overview of the field's progress rather than presenting new empirical data from primary research. In conclusion, the article robustly positions AI-based adaptive learning as a promising solution for elevating educational quality in the digital era. Its strength lies in synthesizing the benefits and outlining key implementation strategies. However, the authors astutely acknowledge critical dependencies for its successful integration, namely the need for adequate technological infrastructure, comprehensive teacher readiness, and robust student digital literacy. These prerequisites are crucial for translating the theoretical potential of this innovation into practical, widespread impact, suggesting avenues for future research and policy focus on overcoming these implementation hurdles.

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