An AI-Based Framework for Personalized Educational Content Generation and Adaptive Assessment

Abstract

The swift development of digital learning has required the replacement of the immobile teaching repositories with dynamic adaptive learning spaces. Although artificial intelligence has already started infiltrating the educational industry, the current systems often fail to be able to offer holistic personalization that considers not only the intellectual capacity but also the unique psychological profiles of specific learners. The paper suggests a holistic design of a personalized learning and assessment platform powered by AI that integrates Large Language Models (LLMs) with the elements of automated behavioral profiling and tools that enable accessibility to all. The proposed platform will also be capable of changing pedagogical strategies and content presentation on the fly to maximize student engagement by using natural language processing to understand student personality traits and learning styles. Moreover, the platform incorporates safe, decentralized data management mechanisms to safeguard confidential psychological and academic information. By carefully examining the modular structure of the system, the theoretical basis, and the hypothetical assessment plan, the paper will provide a strong base of the next generation educational technologies that can be effective, secure, and ethically responsible at the same time.