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The 'Negotiator': Assessing artificial intelligence (AI) interview preparation for graduate radiographers
M Chau1, E Arruzza2, C L Singh1
1Faculty of Science and Health, Charles Sturt University, Level 5, 250 Boorooma St, Charles Sturt University, Wagga Wagga, NSW 2678, Australia.
Introduction:
The Negotiator is an AI-powered interview preparation tool utilizing OpenAI's ChatGPT to assist graduate radiographers in preparing for professional job interviews. The study aimed to assess the tool's relevance, clarity, alignment with competency standards, and overall ability to enhance interview readiness for candidates with distinct educational and professional backgrounds.
Methods:
Three academic evaluators independently assessed two AI-generated interview scenarios tailored to (1) a Bachelor's graduate with foundational radiography knowledge and clinical placement experience, and (2) a graduate-entry Master's student transitioning into radiography from another career. Evaluators rated six criteria-relevance, clarity, alignment with competency standards, practicality, engagement, and overall effectiveness-using a Likert scale. Quantitative analysis included Friedman tests and intraclass correlation coefficients (ICC) to assess inter-rater reliability, while manifest content analysis of qualitative feedback identified strengths and limitations of the tool.
Results:
The Friedman test revealed no significant differences in ratings for Scenario 1 (p=0.232), but Scenario 2 showed significant differences (p=0.047). ICC analysis indicated low inter-rater reliability across both scenarios (Scenario 1: ICC=0.182, Scenario 2: ICC=0.242). Thematic analysis highlighted the tool's strengths in providing relevant prompts, structured responses, and interview readiness while identifying limitations in aligning with competency standards and addressing specific clinical scenarios.
Conclusion:
The Negotiator demonstrates potential as a supplementary tool for radiography interview preparation by enhancing clarity and confidence. However, refinements are needed to improve alignment with professional standards and contextual specificity. Future research should explore personalization, broader applications, and its impact on real-world interview outcomes.
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