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AUTHOR(S): Asriyan Elina, Sargsyan Siranush, Amirkhanyan Diana
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TITLE Application of Machine Learning (ML) Models for Determining the Components of Emotional Intelligence |
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ABSTRACT Emotional intelligence (EI) refers to the ability to recognize, understand, and manage one's own emotions, as well as to perceive, interpret, and influence the emotions of others. This skill is essential for thoughtful behavior in both personal and professional domains. A high level of EI is particularly significant in the field of education, as it influences students’ academic performance, social relationships, well-being, and career prospects. The present article introduces a machine learning (ML)-based software package designed to assess and, if necessary, enhance the emotional intelligence of students. Within this software, a student's emotional quotient (EQ) is evaluated using Nicholas Hall’s “Emotional Intelligence” questionnaire. Based on the resulting score, the system recommends individualized exercises aimed at improving specific components of EI, as defined by Daniel Goleman. The developed software thus provides a tool not only for identifying a user’s EQ level but also for facilitating its targeted development when needed. |
KEYWORDS Artificial Intelligence, Machine Learning, Emotional Intelligence, Emotional Quotient, Support Vector Machine, Neural Network, Python, education |
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Cite this paper Asriyan Elina, Sargsyan Siranush, Amirkhanyan Diana. (2025) Application of Machine Learning (ML) Models for Determining the Components of Emotional Intelligence. International Journal of Education and Learning Systems, 10, 37-41 |
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