Intelligent Integrated Knowledge Discovery Platform: Advancements in Question Generation and Adaptive Learning

Abstract

In the evolving paradigm of digital learning, personalization has become a vital solution to cater to the diverse needs of learners. This paper presents an Intelligent Integrated Platform for Personalized Learning using Natural Language Processing (NLP), Bidirectional Encoder Representations from Transformers (BERT), and Large Language Models (LLM) to automatically generate questions and study material analysis for optimal results. The proposed system allows the uploading of the syllabus, notes, or presentation and returns the dynamically extracted key concepts and the most suitable questions. Our model achieves high accuracy in question selection and generation, which enables an adaptive learning experience catering to individual needs. The results indicate exceptional improvement in content comprehension and knowledge retention, making the approach highly effective in personalized learning. Comparative study with traditional approaches confirms the effectiveness, efficiency, and ease of the system. The study is a stepping stone towards future developments in AI-based learning, with the potential to extend to automated tests, intelligent tutoring systems, and self-learning education.