ISSN :2582-9793

Feasibility Study on Assessing Emotional Health: Applications of Artificial Intelligence

Original Research (Published On: 28-Dec-2023 )
Feasibility Study on Assessing Emotional Health: Applications of Artificial Intelligence
DOI : https://dx.doi.org/10.54364/AAIML.2023.11101

Chung Te Ting, Wen-Fu Yang and Hsiu-Hao Liu

Adv. Artif. Intell. Mach. Learn., 3 (4):1758-1767

Chung Te Ting : The Ph.D. Program in Business and Operations Management

Wen-Fu Yang : The Ph.D. Program in Business and Operations Management

Hsiu-Hao Liu : College of Management Chang Jung Christian University Tainan 711301, Taiwan

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DOI: https://dx.doi.org/10.54364/AAIML.2023.11101

Article History: Received on: 20-Oct-23, Accepted on: 21-Dec-23, Published on: 28-Dec-23

Corresponding Author: Chung Te Ting

Email: ctting@mail.cjcu.edu.tw

Citation: Wen-Fu Yang, Hsiu-Hao Liu, Chung Te Ting (2023). Feasibility Study on Assessing Emotional Health: Applications of Artificial Intelligence. Adv. Artif. Intell. Mach. Learn., 3 (4 ):1758-1767

          

Abstract

    

Numerous studies indicate that educators often experience high levels of stress. Reducing stress and anxiety can prevent setbacks in their professional realization, thereby improving teaching quality and maintaining physical and mental health. Educators must adapt to the constant changes in today's society to ensure the comprehensive development of student populations. However, continuous interaction with students, parents, or legal guardians, as well as relationships with peers, can lead to the accumulation of stress and tension. Over time, this can result in symptoms of burnout syndrome. Therefore, considering students' right to education and the requisite quality of education, educators should pay special attention to and maintain emotional well-being. This study utilizes artificial intelligence technology to obtain signals of organ cell function from the human body. Through database matching, the current status of organ function is determined. Subsequently, a comparison is made with questionnaire data to confirm the psychophysical condition of each case. A total of 20 cases were collected for this study, and through comprehensive analysis of the results from artificial intelligence detection and emotional health self-assessment questionnaires, the feasibility of assessing emotional health through artificial intelligence detection was confirmed. The analysis revealed a high degree of correlation between the two models. Therefore, the results of this study can serve as a reference for relevant professionals in academia, industry, and government.

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