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Published

2024-08-15

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Vol. 9 No. 7 (2024): Published

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Research Articles

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How to Cite

Akkaya, B., Buçan Kırkbir, İlknur, & Üstgörül, S. (2024). Evaluation of artificial intelligence anxiety status of generation Z candidate nurses using machine learning in perspective of leadership. Environment and Social Psychology, 9(7). https://doi.org/10.59429/esp.v9i7.6136

Evaluation of artificial intelligence anxiety status of generation Z candidate nurses using machine learning in perspective of leadership

Bulent Akkaya

Manisa Celal Bayar University, Ahmetli Vocational High School

İlknur Buçan Kırkbir

Karadeniz Technical University

Sema Üstgörül

Manisa Celal Bayar University


DOI: https://doi.org/10.59429/esp.v9i7.6136


Keywords: artificial intelligence anxiety, leadership, generation Z, nurse, machine learning

Abstract

This study aims to determine the artificial intelligence (AI) anxiety levels of Z-generation candidate nurses and the variables affecting the anxiety levels of artificial intelligence by the machine learning (ML) method. Data were collected from 431 candidate nurses by questionnaire using the convenience sampling method. R open access programming language was used for the statistical analysis of the study and the evaluation of significant variables according to their importance levels. The Boruta algorithm, a machine learning method, was used in the determination of the variables affecting the level of artificial intelligence anxiety according to the degree of importance. The findings showed that the most important variable for students' artificial intelligence anxiety level is age. Moreover, there is a statistically significant relationship between students' class and their anxiety level, a significant relationship between artificial intelligence and machine learning in health and their anxiety level, and a significant relationship between gender and technological competence evaluation. Furthermore, nearly half of the participants (48.5%) had very low anxiety, 12.8% had low anxiety, 30.2% had medium anxiety, 6.5% had high-level anxiety and 2.1% of them had very high levels of anxiety. With this research, the artificial intelligence anxiety of generation Z was determined by determining the demographic characteristics that are effective in artificial intelligence. We concluded that more sensitive analysis and different results can be obtained when using a machine learning algorithm compared to classical statistical analysis in determining the complex relationships in the data.

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