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@article{200247,
        author = {HARIVIJAY V and Dr. Sangeetha Radhakrishnan},
        title = {StudyMind Habit analyzer for Multi-Role Educational Environments},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {408-411},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200247},
        abstract = {StudyMind AI is a full-stack, AI-powered web application designed to analyze and improve student study habits in multi-role educational environments. Leveraging a Flask-based RESTful backend and a responsive single-page frontend, the system supports three user roles Administrator, Teacher, and Student each equipped with purpose-built dashboards and functionality. The platform integrates the Groq Llama3-8B large language model for intelligent score prediction, with a deterministic rule-based fallback for offline or API-limited environments. Core features include real-time study session tracking with a built-in Pomodoro-style timer, teacher-assigned MCQ-based assessments with automated grading, daily study goal management, and AI-generated score forecasts with improvement recommendations. Experimental validation against manually computed ground truth confirms a prediction accuracy within 5 percentage points under normal study patterns. The system demonstrates how lightweight EdTech solutions can harness state-of-the-art AI capabilities without heavy infrastructure requirements},
        keywords = {Study Mind AI, Study Habit Analysis, Flask, Groq AI, Llama3, Score Prediction, Educational Technology, MCQ Assessment, Multi-Role Web Application, Python},
        month = {May},
        }