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@article{206045,
        author = {Anjali Rambhad and Anushree Gattani and Madhura Pawar and Dr. Girish Potdar},
        title = {AI-Powered Debate Platform with Counterpoints and Feedback System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {2},
        pages = {153-157},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206045},
        abstract = {Developing argumentative and critical thinking skills is essential for academic, professional, and civic engagement. However, traditional debate practice is limited by the availability of mentors, peer interaction, and timely feedback. To address this gap, we present Vox-Debate, an AI-powered platform that allows learners to practice debates independently while receiving real-time counterpoints and detailed feedback. The platform combines Natural Language Processing (NLP) techniques, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG) to analyze arguments, generate relevant rebuttals, and provide structured, actionable feedback. Vox-Debate leverages a modular architecture with React.js frontend, Node.js backend, MongoDB database, and integrations with Hugging Face and Google APIs. This paper discusses the system design, implementation, and evaluation framework, highlighting its potential to enhance debate skills and critical reasoning at scale.},
        keywords = {},
        month = {July},
        }