Chatbot | Systems and Informatics Research Lab

This project developed EmoSApp, an offline smartphone-based conversational application designed to provide mental health and emotional support while preserving user privacy. The system leverages a fine-tuned and quantized large language model that runs entirely on-device, eliminating the need for internet connectivity or cloud-based processing. Through evaluations with students and mental health professionals, we demonstrated that EmoSApp can engage in empathetic conversations, provide relevant support, and deliver personalized guidance in resource-constrained environments. The project highlights the potential of privacy-preserving, on-device AI technologies for accessible and scalable mental health support.

Screenshots of the EmoSApp

Details

Lead Student:Vimaleswar A

Domain: Mental Health

Year: 2024 - 2025

Presentation

Dataset

Knowledge Dataset and code available on Hugging Face
Dataset creation details available in the paper

Papers

2025

Conference An Offline Mobile Conversational Agent for Mental Health Support: Learning from Emotional Dialogues and Psychological Texts with Student-Centered Evaluation

Vimaleswar A, Prabhu Nandan Sahu, Nilesh Kumar Sahu, and 1 more author

In Proceedings of the International Joint Conference on Natural Language Processing and the Asia-Pacific Chapter of the Association for Computational Linguistics (IJCNLP-AACL), 2025

Abs DOI

Mental health plays a crucial role in the overall well-being of an individual. In recent years, digital platforms have increasingly been used to expand mental health and emotional support. However, there are persistent challenges related to limited user accessibility, internet connectivity, and data privacy, which highlight the need for an offline, smartphone-based solutions. To address these challenges, we propose EmoSApp (Emotional Support App): an entirely offline, smartphone-based conversational app designed to provide mental health and emotional support. EmoSApp leverages a language model, specifically the LLaMA-3.2-1B-Instruct, which is fine-tuned and quantized on a custom-curated “Knowledge Dataset” comprising 14,582 mental health QA pairs along with multi-turn conversational data, enabling robust domain expertise and fully on-device inference on resource-constrained smartphones. Through qualitative evaluation with students and mental health professionals, we demonstrate that EmoSApp has the ability to respond coherently and empathetically, provide relevant suggestions to user’s mental health problems, and maintain interactive dialogue. Additionally, quantitative evaluations on nine commonsense and reasoning benchmarks, along with two mental health specific datasets, demonstrate EmoSApp’s effectiveness in low-resource settings. By prioritizing on-device deployment and specialized domain-specific adaptation, EmoSApp serves as a blueprint for future innovations in portable, secure, and highly tailored AI-driven mental health support.