AI-Powered Interview Simulation to Improve Employability and Reduce Anxiety in Nursing and Midwifery Students

Summary

This randomized controlled trial aims to evaluate the effectiveness of a ChatGPT-based job interview simulation on the employment perceptions and interview-related anxiety of senior nursing and midwifery students. Transitioning from education to professional practice in healthcare is a critical phase that directly influences employability and career readiness. Particularly for nursing and midwifery students, the ability to navigate job interviews with confidence plays a pivotal role in shaping their future career paths. As such, innovative and digital interventions are needed to better prepare students for this process.

Grounded in Bandura's Social Cognitive Theory and the Technology Acceptance Model (TAM), the study explores how AI-driven simulations affect students' self-efficacy, perceived utility, and usability, and ultimately their career-related outlook. The intervention involves a structured, text-based job interview simulation powered by ChatGPT-4o, during which students respond to a series of nine professionally tailored questions. These questions are aligned with international competency frameworks such as those from ICN (2008) and ICM (2024), focusing on themes like professionalism, teamwork, evidence-based care, communication, and leadership. At the end of the simulation, the chatbot provides brief, constructive feedback to the participant.

A total of 102 final-year students from Koç University and Istanbul University-Cerrahpaşa will be recruited using stratified randomization. Participants will be assigned to either an intervention group, which will complete the ChatGPT simulation, or a control group, which will not receive any interview intervention but will complete the same pre- and post-test questionnaires. Key outcome measures include the Perceived Future Employability Scale (PFE), the Interview Anxiety Scale (MASI-T), and a simulation experience form for the intervention group. Quantitative data will be analyzed using SPSS with appropriate parametric and non-parametric tests based on data distribution, and an intention-to-treat (ITT) approach will be adopted.

To ensure the integrity of the experiment, blinding procedures, strict confidentiality, and group separation protocols will be applied. The simulation will be conducted individually on research-owned devices in private rooms, and no personal or textual data will be saved from the AI interactions. Ethical approval has been obtained from Koç University Social and Behavioral Ethics Committee. Participation is voluntary, informed consent will be collected, and all processes will comply with the Helsinki Declaration and Turkish Personal Data Protection Law.

Ultimately, this study seeks to offer evidence on the pedagogical utility of AI-based simulation tools in preparing healthcare students for employment, while also contributing to the broader field of digital transformation in health education.

Conditions

• Employment Anxiety
• Perceived Employability
• Educational Technology
• Artificial Intelligence (AI)
• Nursing Education Research
• Midwifery Education
• Simulation Training

Interventions

OTHER

AI-Based Interview Simulation

The intervention group participated in a structured, text-based job interview simulation supported by ChatGPT-4o, using nine standardized prompts based on ICN (2008) and ICM (2024) core competencies. Developed by experts in simulation and nursing education, the intervention aimed to enhance self-efficacy and reduce interview-related anxiety. Each session lasted 15-20 minutes and concluded with structured feedback provided by ChatGPT. Participants were also offered an optional preparation guide with 40 reflective questions across themes such as communication, anxiety, and self-awareness. This preparation was not mandatory and not included in the simulation time.

Sponsors & Collaborators

• Koç University

  lead OTHER

Principal Investigators

• SEDA SARIKOSE, Asst. Prof. · Koc University School of Nursing

Study Design

Allocation

RANDOMIZED

Purpose

OTHER

Masking

DOUBLE

Model

PARALLEL

Eligibility

Min Age

18 Years

Sex

ALL

Healthy Volunteers

Yes

Timeline & Regulatory

Start

2025-05-08

Primary Completion

2025-07-02

Completion

2025-08-30

Countries

• Turkey (Türkiye)

Study Locations