Ignite Creation Date:
2025-12-24 @ 9:23 PM
Ignite Modification Date:
2025-12-30 @ 11:35 PM
Study NCT ID:
NCT07061132
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2025-07-11
First Post:
2025-07-02
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
AI-Powered Interview Simulation to Improve Employability and Reduce Anxiety in Nursing and Midwifery Students
Sponsor:
Koç University
Organization:
Study Overview
Official Title:
Leveraging AI-Powered Interview Simulations to Enhance Employability and Reduce Anxiety in Final-Year Nursing and Midwifery Students: A Randomized Controlled Trial
Status:
ACTIVE_NOT_RECRUITING
Status Verified Date:
2025-07
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
CARESIM
Brief 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.
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.
Detailed Description:
This study introduces an innovative AI-based intervention aimed at enhancing employability preparation among senior nursing and midwifery students through a simulated job interview experience. The intervention is structured around a generative artificial intelligence model (ChatGPT-4o), which delivers a dynamic, text-based interaction mimicking realistic interview settings. The chatbot prompts are informed by international professional competency frameworks and were developed with input from nursing educators, digital learning experts, and HR professionals.
The conceptual basis of the study combines Social Cognitive Theory and the Technology Acceptance Model, allowing exploration of how simulated, interactive environments contribute to professional readiness. In particular, the study investigates the influence of self-efficacy beliefs and user perception of AI technology on employment-related attitudes.
To ensure validity and usability, a pilot implementation was conducted prior to full deployment. This included iterative testing of the chatbot dialogue flow, question clarity, and interview timing, with input from students outside the main sample. Minor refinements were applied to optimize realism and reduce cognitive overload during simulation.
The AI interaction does not involve natural language learning or data retention; its sole function is to facilitate reflective engagement in a structured scenario. All responses are deleted after the session to comply with data protection standards. The chatbot-generated feedback is non-evaluative and designed to reinforce confidence.
Quantitative analysis will be supplemented with descriptive insights from a post-simulation feedback form that captures students' subjective experience, perceived preparedness, and usability impressions. This mixed-methods integration supports a comprehensive understanding of the intervention's practical and pedagogical value.
Ultimately, the project aims to contribute to the responsible implementation of AI tools in health professions education, particularly in the context of employability, professional identity formation, and digital literacy.
The conceptual basis of the study combines Social Cognitive Theory and the Technology Acceptance Model, allowing exploration of how simulated, interactive environments contribute to professional readiness. In particular, the study investigates the influence of self-efficacy beliefs and user perception of AI technology on employment-related attitudes.
To ensure validity and usability, a pilot implementation was conducted prior to full deployment. This included iterative testing of the chatbot dialogue flow, question clarity, and interview timing, with input from students outside the main sample. Minor refinements were applied to optimize realism and reduce cognitive overload during simulation.
The AI interaction does not involve natural language learning or data retention; its sole function is to facilitate reflective engagement in a structured scenario. All responses are deleted after the session to comply with data protection standards. The chatbot-generated feedback is non-evaluative and designed to reinforce confidence.
Quantitative analysis will be supplemented with descriptive insights from a post-simulation feedback form that captures students' subjective experience, perceived preparedness, and usability impressions. This mixed-methods integration supports a comprehensive understanding of the intervention's practical and pedagogical value.
Ultimately, the project aims to contribute to the responsible implementation of AI tools in health professions education, particularly in the context of employability, professional identity formation, and digital literacy.
Study Oversight
Has Oversight DMC:
False
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
Secondary ID Infos
| Secondary ID | Type | Domain | Link | View |
|---|---|---|---|---|
| 2025.203.IRB3.080 | OTHER | Koç University Ethics Committee | View |