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@article{199205,
        author = {Nithish N and Nitharsan S G and Nirmal S and Sabarigiri S and Mohamed Fasith J and Dr. Baby Kalpana Y},
        title = {AlgoViz: An Interactive Algorithm Visualizer with AI Assistance},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {12140-12147},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=199205},
        abstract = {With the rapid growth of digital learning, students face increasing challenges in understanding complex algorithms efficiently. Traditional learning methods rely heavily on static representations such as textbooks and lectures, leading to confusion, lack of clarity, and poor conceptual understanding. To address these challenges, we propose AlgoViz, an interactive algorithm visualizer with AI that enhances learning through real-time visualization and automation.The system allows users to input data and visualize algorithms step by step through an interactive interface. It uses modern technologies such as Node.js for backend processing, Python for Artificial Intelligence, and MongoDB for data management. The system dynamically processes user inputs and generates visual representations of algorithm execution, enabling better understanding of internal operations. Features such as real-time execution, interactive controls, and structured output presentation improve user engagement and learning efficiency.This approach reduces manual effort, minimizes errors in algorithm tracing, and enhances conceptual clarity. The proposed system provides a scalable, efficient, and user-friendly solution for modern computer science education, improving learning outcomes and making algorithm understanding more accessible and effective.},
        keywords = {Visual Representation, Digital Learning, Algorithm Visualization, Interactive Controls, Algorithm Tracing,Internal Algorithm Operations},
        month = {April},
        }