SMACS: Stress Management AI Chat System

Daiki Mori, Kazuyuki Matsumoto, Xin Kang, Manabu Sasayama, Keita Kiuchi

2024

Abstract

The purpose of this study is to develop a stress management AI chat system that can connect users who want mental health care with counselors. By means of this chat system, a conversational AI based on a large language model (LLM) will collect data on the user's stressors through text chats with the user. The system is personalized to the user based on the collected data. This paper describes the nature of the data collected in the preliminary experiment conducted in March 2024 and the results of its analysis, and discusses considerations for the main experiment to be conducted after July 2024. The preliminary experiment was conducted with 11 students over a 3-week period. Discuss the distribution of the data collected and the issues involved in building a model for predicting stress levels.

Paper Citation

in Harvard Style

Mori D., Matsumoto K., Kang X., Sasayama M. and Kiuchi K. (2024). SMACS: Stress Management AI Chat System. In Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD; ISBN 978-989-758-716-0, SciTePress, pages 167-174. DOI: 10.5220/0012940300003838

in Bibtex Style

@conference{keod24,
author={Daiki Mori and Kazuyuki Matsumoto and Xin Kang and Manabu Sasayama and Keita Kiuchi},
title={SMACS: Stress Management AI Chat System},
booktitle={Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD},
year={2024},
pages={167-174},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012940300003838},
isbn={978-989-758-716-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD
TI - SMACS: Stress Management AI Chat System
SN - 978-989-758-716-0
AU - Mori D.
AU - Matsumoto K.
AU - Kang X.
AU - Sasayama M.
AU - Kiuchi K.
PY - 2024
SP - 167
EP - 174
DO - 10.5220/0012940300003838
PB - SciTePress