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International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 13 Issue: 03 | Mar 2026

p-ISSN: 2395-0072

www.irjet.net

AI StudyMate and Mental Health Assistant Kausar Shaikh1, Maziya Khan2, Fatima Sayyed3, Zoya Ashrafi4 , Ms. Noorusabah Sayed5 I/C HOD AN, Lecturer IF M.H. Saboo Siddik Polytechnic, India ---------------------------------------------------------------------------***----------------------------------------------------------------------

Abstract— this technical paper presents StudyMate, an

B. Knowledge Grounding (RAG)

integrated digital ecosystem designed to mitigate academic burnout by bridging the gap between productivity management and psychological support. While traditional educational tools focus strictly on task completion, StudyMate incorporates a Mental Health Companion that utilizes real-time sentiment analysis to monitor student wellbeing. By analyzing user inputs and study patterns, the system provides personalized interventions and stress-relief recommendations. Experimental data suggests that students using the integrated framework maintained a 25% higher consistency in task completion while reporting lower anxiety levels compared to traditional methods. This paper details the system architecture, the algorithmic approach to sentiment detection, and the impact of wellness-integrated pedagogy.

To ensure academic accuracy, the system pulls textual data directly from a PDF/Knowledge Base. This ensures that the AI's academic responses are grounded in actual course material, maintaining a secure and monitored environment for learning. II. THE GENESIS AND ORIGIN OF STUDYMATE The origin of StudyMate lies in the identification of a critical gap in current educational technology: the lack of emotional intelligence in productivity tools. While modern students are equipped with numerous digital planners, these systems operate on the flawed assumption that human productivity is a constant, linear variable. StudyMate was conceived to replace this rigid model with a system that recognizes the psychological state of the learner as the primary driver of academic success.

Keywords— Retrieval-Augmented Generation (RAG), Large Language Models (LLM), Sentiment Analysis, Academic Optimization, Mental Health Support.

A. Problem Identification

I. Introduction

The project originated from observing the "hustle culture" prevalent in higher education, where students prioritize task completion at the expense of mental wellbeing. Research into student behavior indicated that relentless deadlines without emotional support lead to high burnout rates. This provided the foundation for a tool that could monitor stress in real-time while assisting with academic workloads.

A. Definition StudyMate is proposed as a solution to this imbalance, providing a unified platform where academic optimization and mental wellness coexist. The system is designed to act as both a rigorous academic scheduler and a compassionate mental health companion. By integrating real-time sentiment analysis with traditional task management, StudyMate identifies early signs of exhaustion and proactively suggests wellness interventions.

B. Technological Evolution The technical origin of the system started with the integration of Natural Language Processing (NLP) to analyze student chat queries. Initially designed as a simple scheduler, the system evolved through the development of the MentalHealthSuite. By incorporating RetrievalAugmented Generation (RAG), StudyMate was able to pull context from a specialized PDF Knowledge Base, ensuring that its support was not just empathetic, but academically accurate.

I. BASIC CONCEPTS OF STUDYMATE The core philosophy of the system is rooted in the "Feedback Loop" between a student’s output and their internal state. A. Sentiment Analysis and Intervention The system architecture revolves around a closed-loop feedback system. It captures both structured data (task lists) and unstructured data (journal entries or chat queries). A Natural Language Processing (NLP) engine then performs sentiment analysis, assigning a numerical "Sentiment Score" to determine if the academic workload is causing a detrimental emotional response.

Impact Factor value: 8.315

Aim and Objective

The ultimate goal of StudyMate’s creation was to prove that a student's mental health is not a secondary concern, but the foundation of sustained productivity. By assigns a numerical "Sentiment Score" to user inputs, the system can determine if a workload is becoming

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