MindEase- An AI Mental Health Chatbot

Abstract

Mental health challenges such as stress, anxiety, and emotional instability have become increasingly prevalent, particularly among students and young professionals. Limited access to timely support, social stigma, and high consultation costs often discourage individuals from seeking professional assistance. This paper presents MindEase, an AI-based mental health support Android application designed to provide emotional assistance, self-awareness, and access to professional resources. The application is developed using Java in Android Studio and operates on the Android platform. The chatbot functionality is powered by Google AI Studio through secure API integration, enabling context-aware and empathetic responses. Natural Language Processing (NLP) techniques are employed to analyze user text input and detect emotional states such as happiness, sadness, stress, anger, and neutrality. User interaction data and mood logs are securely managed using a MySQL database. Additionally, the system integrates the Google Maps SDK to help users locate nearby psychologists, enhancing practical usability. Experimental evaluation indicates that MindEase effectively performs emotion detection and response generation, serving as a reliable preliminary mental health support tool while complementing professional healthcare services.

Introduction

The paper presents MindEase, an AI-based mental health support Android application designed to provide accessible emotional assistance to users experiencing stress, anxiety, or mood-related issues. Due to increasing mental health concerns and barriers such as stigma, cost, and limited access to professionals, the system aims to offer preliminary support through a mobile platform.

???? Key Features of MindEase:

• AI-powered chatbot using a hybrid approach (AIML rule-based system + API-based pre-trained AI models via Google AI Studio).

• Natural Language Processing (NLP) for emotion detection and context-aware responses.

• Secure MySQL database for storing chat logs and user data.

• Google Maps API integration to locate nearby psychologists for professional support.

• Developed using Java in Android Studio.

???? System Architecture:

The system includes:

• User Interface (chat-based interaction)

• Input Processing Module (text normalization & keyword extraction)

• AI Processing Engine (emotion classification + intelligent response generation)

• Database Module (data storage & privacy management)

• Professional Support Module (location-based services)

• Output Response Module (delivering supportive messages)

???? Methodology:

• Collects and preprocesses user text input.

• Uses AIML for emotion classification (stress, anxiety, sadness, etc.).

• Generates empathetic responses through AI API integration.

• Stores interaction data securely.

• Tests system performance using Android Studio and Google Colab.

???? Results:

• Accurate emotion detection using rule-based classification.

• Improved response quality through AI integration.

• Smooth system performance with minimal delay.

• Successful location-based psychologist search.

• Demonstrates effective preliminary mental health support.

Conclusion

This paper presented MindEase, an AI-based mental health support Android application designed to provide accessible and cost-effective emotional assistance. The system integrates rule-based AIML classification with API-driven pre-trained AI models to deliver empathetic and context-aware responses. By combining natural language interaction, secure MySQL database management, and Google Maps-based professional support integration, the application offers a comprehensive preliminary mental health assistance platform. The experimental results demonstrate that the hybrid approach improves response relevance and conversational quality while maintaining system efficiency within the Android environment. Although the application does not replace professional psychological therapy, it serves as a reliable first-level support system that encourages emotional awareness and timely access to professional care.

References

[1] K. Fitzpatrick, A. Darcy, and M. Vierhile, “Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): A randomized controlled trial,” JMIR Mental Health, vol. 4, no. 2, pp. 1–11, 2017.. [2] T. Bickmore, D. Schulman, and C. Yin, “Maintaining engagement in long-term interventions with relational agents,” Applied Artificial Intelligence, vol. 24, no. 6, pp. 648–666, 2010.I. Boglaev, “A numerical method for solving nonlinear integro-differential equations of Fredholm type,” J. Comput. Math., vol. 34, no. 3, pp. 262–284, May 2016, doi: 10.4208/jcm.1512-m2015-0241. [3] B. Liu, Sentiment Analysis and Opinion Mining. San Rafael, CA, USA: Morgan & Claypool Publishers, 2012.

Copyright

Copyright © 2026 Mansi Shabadi, Aarya Abhyanlkar, Hritika Naral, Shravani Bhosale, Sejal Swami, Shrutika Pakave. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.