{"title":"“谈判者”:评估毕业放射技师的人工智能(AI)面试准备","authors":"M. Chau , E. Arruzza , C.L. Singh","doi":"10.1016/j.jmir.2025.101982","DOIUrl":null,"url":null,"abstract":"<div><h3>Introduction</h3><div><em>The Negotiator</em> 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.</div></div><div><h3>Methods</h3><div>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.</div></div><div><h3>Results</h3><div>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.</div></div><div><h3>Conclusion</h3><div><em>The Negotiator</em> 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.</div></div>","PeriodicalId":46420,"journal":{"name":"Journal of Medical Imaging and Radiation Sciences","volume":"56 5","pages":"Article 101982"},"PeriodicalIF":1.3000,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The ‘Negotiator’: Assessing artificial intelligence (AI) interview preparation for graduate radiographers\",\"authors\":\"M. Chau , E. Arruzza , C.L. Singh\",\"doi\":\"10.1016/j.jmir.2025.101982\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Introduction</h3><div><em>The Negotiator</em> 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.</div></div><div><h3>Methods</h3><div>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.</div></div><div><h3>Results</h3><div>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.</div></div><div><h3>Conclusion</h3><div><em>The Negotiator</em> 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.</div></div>\",\"PeriodicalId\":46420,\"journal\":{\"name\":\"Journal of Medical Imaging and Radiation Sciences\",\"volume\":\"56 5\",\"pages\":\"Article 101982\"},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2025-05-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Medical Imaging and Radiation Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1939865425001328\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Medical Imaging and Radiation Sciences","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1939865425001328","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
The ‘Negotiator’: Assessing artificial intelligence (AI) interview preparation for graduate radiographers
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.
期刊介绍:
Journal of Medical Imaging and Radiation Sciences is the official peer-reviewed journal of the Canadian Association of Medical Radiation Technologists. This journal is published four times a year and is circulated to approximately 11,000 medical radiation technologists, libraries and radiology departments throughout Canada, the United States and overseas. The Journal publishes articles on recent research, new technology and techniques, professional practices, technologists viewpoints as well as relevant book reviews.