University of Twente Student Theses


Artificial Intelligence Acceptance, Perception in the Future Workfield and Adaptability of Healthcare Students

Miravete Benito, Pablo (2024) Artificial Intelligence Acceptance, Perception in the Future Workfield and Adaptability of Healthcare Students.

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Abstract:The integration of artificial intelligence (AI) in the healthcare field implies an adaptation process by the professionals. Numerous research was conducted about the impact of AI among healthcare professionals, but there exists a research gap in understanding healthcare students ́ acceptance intention of AI in their current studies and perception of the role of AI in their future workfield. Hence, this study focused on investigating the factors influencing AI acceptance intention in healthcare students and their Perception of AI in the future workfield among healthcare students, framed within the Unified Theory of Acceptance and Use of Technology (UTAUT) model. Methods The participants were 60 international healthcare students, mainly from the Univeristy of Twente and Universitat de València. A few prerequisites were determined such as currently studying a healthcare related program or fluently speaking English and/or Spanish. An online questionnaire was distributed to measure different UTAUT components, namely Effort expectancy, Expected performance, Facilitating conditions and Social influence, an extra numerical variable namely Adaptability and two different variables namely AI acceptance intention and Perception in the future workfield. Since the questionnaire was divided into different variables, Cronbach's alpha was calculated to determine the validity of the collected data. The first regression model was conducted to examine the significance between the selected UTAUT variables plus Adaptability, and AI acceptance intention. Secondly, another regression model was designed to analyse the relationship between the same dependent variables Perception in the future workfield. In addition, the moderation effect of Experience was determined in both cases. Results None of the UTAUT components approached significance with AI acceptance intention. In the second model, the relationship between Perception in the future workfield and Adaptability moderated by Experience (β = -.06, SE = .02, p = .03*), was determined as significant. However, the rest of the variables did not show a significant relationship with Perception in the future workfield. Conclusion The lack of significant relationships (excluding the relationship between Adaptability and Perception in the future workfield, including the moderation effect of Experience) suggested that the chosen variables in the UTAUT framework and Adaptability may not be individually strong factors of AI acceptance intention and Perception in the future workfield in the context of healthcare students. In conclusion, educators and researchers need to consider these nuanced insights when designing AI education interventions for healthcare students. The evolving landscape of AI in healthcare demands a continuous reevaluation of our understanding and the incorporation of diverse perspectives to ensure the effective integration of AI technologies into the future healthcare workfield.
Item Type:Essay (Bachelor)
Faculty:BMS: Behavioural, Management and Social Sciences
Subject:77 psychology
Programme:Psychology BSc (56604)
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