Perceptions of nursing graduates regarding artificial intelligence interviews: A Q-methodology study

Abstract

Background & Aim: The integration of Artificial Intelligence (AI) in recruitment processes is increasingly prevalent, particularly within medical institutions. AI interviews are becoming a common practice, and their impact on the perceptions and experiences of candidates is a subject of growing interest. Nursing graduates, who are often at the forefront of medical practice, frequently encounter these AI-driven evaluations during their job searches. This study attempted to examine the perception of AI interviews among nursing graduates who have experienced AI interviews in medical institutions.

Methods & Materials: The Q-methodology was applied by selecting 34 Q samples from the 102 concourses extracted through a literature review and in-depth clinical interviews with nursing graduates. The P sample consisted of 35 nursing graduates who had experienced AI interviews. Data were collected using the PQ Method's Q-methodology program, measured on a 9-point scale frame, and Q samples were normally distributed.

Results: The study identified four factors of perception: Proactive AI Interview Preparation, Negative Perception of AI Interviews, Positive Perception of AI Interviews, and Critical Acceptance of AI Interviews.

Conclusion: In conclusion, perceptions of AI interviews can be categorized into four main types, highlighting both the positive and negative aspects of this technology. The positive aspects include efficiency, fairness, and convenience, while the negative aspects involve concerns about privacy, bias, and the lack of human elements. To design AI interview programs tailored to specific job roles, it is crucial to balance these pros and cons. Additionally, reducing the burden of AI interviews through informative resources and pre-training programs is essential. For successful implementation, ongoing improvements, transparency, and a balanced integration of human judgment are necessary.