City Research Online

Abstract

Introduction: AI education is essential to facilitate seamless clinical integration. The HCPC in the UK requires all radiographers to have some level of digital skills to maintain safety of clinical practice. This study aimed to evaluate the impact of a dedicated AI seminar on radiography students.

Methods: A dedicated 1.5-h in-person seminar was delivered by an AI vendor to final year undergraduate diagnostic radiography students at a UK University. The course consisted of both theory and practice training. An online survey was built and piloted, consisting of both closed and open-ended questions, to explore their level of knowledge, skills and confidence in AI, before (pre-test) and after the delivery (post-test) of the seminar using a 10-point scale. Pre-test was distributed two weeks before the seminar and post-test was open two weeks after.

Results: A total of 68 students answered the pre-test and 31 the post-test survey. Students’ theoretical knowledge (Mean = 6.57 vs Mean = 3.85), skills (Mean = 5.39 vs Mean = 3.44) and confidence (Mean = 5.47 vs Mean = 3.43) on AI were all significantly improved after the seminar. Their responses became more focused and specific in the post-test survey. In both surveys students expressed concerns around reliability and accountability of AI, data management and security, patient confidentiality and overreliance on technology in the open-ended questions. They also requested more AI training with hands-on options in their undergraduate degree.

Conclusion: This study confirms the importance of even brief, but customised educational interventions relating to AI for radiographers. The learning needs to be customised to maximise knowledge retention and applicability and to include both theoretical and practical aspects for consolidation of skills.

Implications for practice: These findings will help radiography educators build more focused, tailored AI courses for future students.

Publication Type:	Article
Additional Information:	© 2025 The Author(s). Published by Elsevier Ltd on behalf of The College of Radiographers. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Publisher Keywords:	Artificial intelligence, AI, Radiography, Education, Evaluation, Knowledge
Subjects:	L Education > LB Theory and practice of education > LB2300 Higher Education Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RC Internal medicine
Departments:	School of Health & Medical Sciences School of Health & Medical Sciences > Midwifery & Radiography
SWORD Depositor:	Symplectic Administrator

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