Treffer: Adaptive Personalized Learning System with Generative AI.

This paper introduces AdaptEd: Personalized Learning for All (PLFA), a cutting-edge educational platform designed to enhance individual learning experiences through the integration of generative artificial intelligence (AI). AdaptEd leverages advanced AI models, including Llama 3.1 for general functionalities, CodeLlama for specialized programming courses, and Mistral AI for multilingual support, enabling the delivery of tailored educational content that aligns with each learner's unique abilities and preferences. The platform is developed using the Flask framework in Python, incorporating essential plugins such as Flask-Cors, Flask-Login, and Flask-SQLAlchemy to ensure robust functionality and scalability. Key features of AdaptEd include dynamic curriculum generation based on customizable parameters, file upload capabilities for enhanced content tuning via a Qdrant vector database, and a comprehensive reporting dashboard that allows students to manage and track their progress effectively. Additionally, AdaptEd employs a speech-to-speech interaction system utilizing Whisper for audio transcription and Llama models for prompt processing, facilitating an interactive and engaging learning environment. The system architecture adopts a microservices approach, containerized with Docker to optimize performance and scalability. Historical development involved iterative testing of various vector databases and language models to achieve optimal compatibility and efficiency. Preliminary evaluations indicate significant improvements in student engagement and knowledge retention, demonstrating the platform's potential to transform personalized education. Future enhancements will focus on integrating virtual code interpreters, live virtual classrooms, and gamification elements to further enrich the learning experience [ABSTRACT FROM AUTHOR]

Copyright of Frontiers in Health Informatics is the property of Iranian Journal of Medical Informatics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)