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
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
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.