AI-Assisted Emotion Recognition: Impacts on Mental Health Education and Learning Motivation

Li, Zhiqiang

doi:10.3991/ijet.v18i24.45645

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…Along with this, an improved probabilistic matrix factorization (PMF) model has been created to provide personalized content suggestions for learners. Their goal is to offer a more precise and successful method of health teaching [28]. Like pupil diameter, eye gaze can provide sufficient information about the stress level in humans.…”

Section: Researchers Have Successfully Detected the Affective States ...mentioning

confidence: 99%

Application of Computer Vision Techniques to Study the Relationship between Mental Stress and Pupil Diameter among Student Population

Moharana,

Das,

Routray

2024

Int. J. Onl. Eng.

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Stress is a state of mental tension, which helps us to cope with challenges in our life. It makes us progressive when it is positive, but excessive negative stress that perseveres for a long time leads to a state of depressiveness. Longer stressed stage of a human being changes the size, functionality and frequency of response of many internal and external body parameters. By applying computer vision techniques, these changes of body parameters can be tracked to get useful information about the mental stress for a stress affected person. Many studies show the pupil diameter varies significantly with the effect of stress. Our work is based on the study of variation of pupil diameters of stress affected and not affected university students. With the application of different supervised machine learning algorithms, we have observed that the pupil dilates more in case of stress affected students than non-stressed students. We have also found that the pupils of the students dilates more when they were in positive emotional states than their negative emotional states. This work will be helpful for researchers who are working in the field of emotion detection and recognition and affective disorder analysis.

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Section: Researchers Have Successfully Detected the Affective States ...mentioning

confidence: 99%

Application of Computer Vision Techniques to Study the Relationship between Mental Stress and Pupil Diameter among Student Population

Moharana,

Das,

Routray

2024

Int. J. Onl. Eng.

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Interactivity, humanness, and trust: a psychological approach to AI chatbot adoption in e-commerce

Ding,

Najaf

2024

BMC Psychol

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This study aims to investigate the impact of interactivity and perceived humanness on trust toward AI chatbots in the e-commerce setting. Moreover, this study also aims to examine the mediation effect of trust toward AI chatbots in the relationship between interactivity and intention to adopt AI chatbots for e-commerce as well as in the relationship between perceived humanness and intention to adopt chatbots for e-commerce. This study used a time lag approach to collect the data from 343 customers from the southern region of China. The data were collected online through a questionnaire designed in Chinese language using a survey firm. The findings of this study indicated that there is a significant impact of interactivity and humanness on the trust toward chatbots. Moreover, the findings of this study indicated that there is a significant mediating effect of trust toward chatbots in the relationships of interactivity and perceived humanness to adopt chatbots for e-commerce. In addition, this study found a significant moderating influence on the perceived enjoyment of using chatbots in e-commerce settings. This study provides a unique perspective of expectation-confirmation theory for adopting emerging technologies for online shopping and also provides insights for designers and business firms to develop businesses to facilitate the AI chatbot feature for e-commerce.

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Mediating effects of artificial intelligence on the relationship between academic engagement and mental health among Chinese college students

Wang,

Wang

2024

Front. Psychol.

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IntroductionAcademic engagement of Chinese college students has received increasing research attention due to its impact on Students’ Mental health and wellbeing. The emergence of artificial intelligence (AI) technologies marked the beginning of a new era in education, offering innovative tools and approaches to enhance learning. Still, it can be viewed from positive and negative perspectives. This study utilizes the Theory of Planned Behavior (TPB) as a theoretical framework to analyze the mediating role of students’ attitudes toward AI, perceived social norms, perceived behavioral control, and their intention to use AI technologies in the relationships between Students’ academic engagement and Mental health.MethodsThe study involved a total of 2,423 Chinese college students with a mean age of approximately 20.53 ± 1.51 years. The survey was conducted through Questionnaire Star, using a secure website designed specifically for the study. The Hayes’ PROCESS Macro (Version 4.2) Model 80 with SPSS 29.0, a multivariate regression analysis with a chain mediation model that allows for multiple mediators to be tested sequentially, has been used. The statistical test explored the direct and indirect effects of students’ engagement (X) on mental health (Y) through a series of mediators: attitude toward AI (M1), subjective norm (M2), perceived behavioral control over AI use (M3), and AI use behavioral intention (M4).ResultsThe direct positive relationship between engagement and mental health (β = 0.0575; p < 0.05), as well as identifying key mediating factors such as perceived behavioral control (β = 0.1039; p < 0.05) and AI use of behavioral intention (β = 0.0672; p < 0.05), highlights the potential of AI tools in enhancing students’ well-being. However, the non-significant mediating effects of attitude toward AI (β = 0.0135), and subjective norms (β = –0.0005), suggest that more research is needed to understand the nuances of these relationships fully.DiscussionOverall, the study contributes to the growing body of literature on the role of AI in education and offers practical implications for improving mental health support in academic settings.

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AI-Assisted Emotion Recognition: Impacts on Mental Health Education and Learning Motivation

Cited by 3 publications

References 24 publications

Application of Computer Vision Techniques to Study the Relationship between Mental Stress and Pupil Diameter among Student Population

Application of Computer Vision Techniques to Study the Relationship between Mental Stress and Pupil Diameter among Student Population

Interactivity, humanness, and trust: a psychological approach to AI chatbot adoption in e-commerce

Mediating effects of artificial intelligence on the relationship between academic engagement and mental health among Chinese college students

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