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@article{177910,
        author = {Shantanu Rudakar Mandawkar and Krishna Shivraj Khaple and Netraja Mulay and Prakash Kene},
        title = {Enhancing Adaptive Learning Using Reinforcement Learning for Real-Time Code Debugging & Feedback},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {2908-2911},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177910},
        abstract = {This paper proposes a novel adaptive learning framework integrating reinforcement learning (RL) and transformer-based natural language processing (NLP) to deliver real- time, personalized, pedagogically rich feedback. Moving beyond static feedback models, the system uses Q-learning and policy optimization to identify student error patterns and adapt responses dynamically. The NLP module, leveraging spaCy and transformer architectures, generates context-specific responses tailored to learners. A four-week controlled experiment with 200 middle school students demonstrated significant improvements in performance, retention, and engagement. The system’s scalability, ethical transparency, and user-centered design offer promising directions for intelligent tutoring systems.},
        keywords = {},
        month = {May},
        }