Design and Evaluation of an Intelligent Adaptive Learning System Using Generative Artificial Intelligence
TL;DR
Based on the results of all evaluations performed, it has been found that AI-based adaptive learning systems provide greater motivation, more successfully comprehend course content, and a higher level of academic performance compared to the use of traditional mobile learning apps.
Abstract
The use of adaptive learning systems with artificial intelligence (AI) in education has changed how educators teach by providing a customized learner experience that matches the particular characteristics of each learner, thus increasing both learner engagement and learning outcomes. This article provides a description of the design, development, and evaluation of a generative AI-based interactive mobile learning system designed to increase learner engagement, increase the ability for the learner to acquire knowledge, and provide improved access to educational resources. By using a mobile-based learning platform, learners have access to real-time tutoring, generated, determined feedback, generated instructional materials, and interactive learning experiences, 24 hours a day, seven days a week. The system architecture consists of user profiling, a learning analytics engine, and a content generation process for creating dynamic course materials using GenAI and providing feedback and making recommendations. These four components of the system work together to create personalized learning paths for individual learners and to enhance the ongoing learning processes of all learners. A thorough assessment of the proposed approach was conducted by utilizing multiple methods with students from a wide variety of educational backgrounds in order to measure the effectiveness of the AI-based adaptive learning system. Students’ performance (effectiveness) was measured using both quantitative metrics and qualitative feedback, including measures of learning success (i.e., test scores), levels of engagement (i.e., how often students engage with the material), completion rate (number of tasks completed vs. total number of tasks assigned), and satisfaction (whether learners felt satisfied with their adaptive learning experience). Based on the results of all evaluations performed, it has been found that AI-based adaptive learning systems provide greater motivation, more successfully comprehend course content, and a higher level of academic performance compared to the use of traditional mobile learning apps.