Harnessing Deep Learning for Automated Mathematical Problem Generation and Personalized Learning Pathways

Abstract

The integration of deep learning into educational technologies has opened new avenues for personalized learning and automated content generation. This paper presents a comprehensive approach to leveraging deep learning for the automated generation of mathematical problems and the creation of personalized learning pathways. We propose a novel framework that utilizes neural networks to generate diverse and contextually relevant mathematical problems, tailored to individual learner profiles. The framework also incorporates adaptive learning algorithms to dynamically adjust the difficulty and type of problems based on real-time performance data. Our methodology includes data collection, preprocessing, model training, and evaluation, with a focus on ensuring the quality and educational value of the generated content. The proposed model is evaluated on a dataset of mathematical problems, and the results demonstrate its effectiveness in generating high-quality problems and improving learning outcomes. We discuss the implications of our findings, potential applications, and future directions for research in this area.