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@article{175320,
        author = {Aditya Patil and Harsh Chavan and Samrudhi Dube and Shaurya Patil and Prof. Poonam Lad},
        title = {LLM-Based AI Tutor for Personalized Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {3202-3207},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175320},
        abstract = {The mission provides the development of an AI tutor specially tailored for engineering college students using a large language model (LLM), including generative models and masked language models, to provide personalized learning experiences. The AI tutor harnesses the power of advance natural language processing algorithms, such as GPT (Generative Pre-trained Transformer) to understand the unique needs of each student. Engineering students often face challenges in mastering complex topics such as mathematics, physics, and programming, where a one-size-fits-all approach to education is inadequate. The AI tutor addresses this issue by analyzing individual student interactions, problem solving strategies, such as root cause analysis and brainstorming, and comprehension levels to provide customized learning paths. Through continuous adaptation to the student’s performance, the AI tutor offers personalized quizzes and open-ended exercises, which help to breakdown complex concepts into manageable steps. Additionally, the system integrates adaptive learning and real-time feedback to the enhance student engagement and academic performance.},
        keywords = {AI Tutor, Personalized Learning, Generative Models, Adaptive Learning, Real-Time Feedback, Interactive Problem-Solving, Root Cause Analysis, Brainstorming.},
        month = {April},
        }