ADAPTIVE CLOUD LEARNING TUTOR USING LLM

Abstract: So these are the contents of my research paper. These are some PPT slides I want you to convert into a paper presentation which consists of six pages. I want you to give me abstract, keywords, introduction, proposed system architecture, methodology with some modules, and experiments and results, results and discussions, conclusions, and some references. Please don't add on your own. If you want any other information, you can ask me. Make it plagiarism- free, more humanized way. That's it. Make it in a structured manner. Learning Management Systems (LMS) have become an essential component of modern education, enabling institutions to deliver courses, manage assessments, and track learner progress through digital platforms. However, most traditional LMS platforms operate using static recommendation techniques and predefined rules, offering minimal personalization to learners. As a result, students often struggle to identify suitable courses aligned with their interests, skill levels, and academic goals. Additionally, mentor allocation is frequently manual or generic, reducing the effectiveness of guidance and academic support. This research proposes an Intelligent Learning Management System (iLMS) powered by Large Language Models (LLMs) and collaborative recommendation mechanisms. The system processes structured and unstructured data such as student interactions, course materials, feedback, and mentor profiles to generate semantic embeddings. These embeddings capture learner preferences, knowledge levels, engagement patterns, and mentor expertise. Based on this understanding, the system delivers personalized course recommendations, adaptive learning paths, and intelligent mentor-student matching. The proposed system also integrates real-time performance monitoring, predictive analytics, and dynamic assessment mechanisms to identify knowledge gaps and adjust content difficulty accordingly. Interactive dashboards provide continuous feedback to students, mentors, and administrators. By combining LLM-based semantic analysis with adaptive recommendation strategies, the iLMS enhances learner engagement, improves academic outcomes, and provides a scalable solution for personalized digital education.

Keywords: Learning Management System, Large Language Model (LLM), Personalized Learning, Course Recommendation, Adaptive Learning Path, Mentor-Student Matching, Semantic Embedding, NLP, Collaborative Recommendation, Educational Data Mining.