AdaptiveGPT: Towards Intelligent Adaptive Learning

Abstract

Adaptive learning is an educational methodology that allows the personalization of learning according to the student’s pedagogical path. In digital environments, the strategic use of technologies enhances adaptive learning initiatives, enabling a dynamic understanding of intricate contextual nuances and the ability to identify and recommend appropriate learning activities. Therefore, this work proposes developing and evaluating a prototype that uses a large language model to create adaptive educational activities in face-to-face and virtual environments automatically. The applied methodology involves the implementation of a large language model with advanced cognitive capabilities to generate learning activities that adapt to individual needs. A proof of concept was developed to evaluate the practicality and usability of this approach. The research results indicate that the approach is practical and adaptable to different educational contexts, reinforcing the synergy between adaptive learning, artificial intelligence, and learning environments. The proof of concept evaluation showed that the prototype is highly usable, validating the proposal as an innovative solution to the growing needs of modern education.