Medical imaging and radiation science students' use of artificial intelligence for learning and assessment

S. Lewis, F. Bhyat, Y. Casmod, A. Gani, L. Gumede, A. Hajat, L. Hazell, C. Kammies, T. B. Mahlaola, L. Mokoena, L. Vermeulen

Research output: Contribution to journalArticlepeer-review

Abstract

Introduction: Artificial intelligence has permeated all aspects of our existence, and medical imaging has shown the burgeoning use of artificial intelligence in clinical environments. However, there are limited empirical studies on radiography students' use of artificial intelligence for learning and assessment. Therefore, this study aimed to gain an understanding of this phenomenon. Methods: The study used a qualitative explorative and descriptive research design. Data was obtained through five focus group interviews with purposively sampled undergraduate medical imaging and radiation science students at a single higher education institution in South Africa. Verbatim transcripts of the audio-recorded interviews were analysed thematically. Results: Three themes and related subthemes were developed: 1) understanding artificial intelligence, 2) experiences with the use of artificial intelligence with the subthemes of the use of artificial intelligence in theoretical and clinical learning and challenges of using artificial intelligence, and 3) incorporation of artificial intelligence in undergraduate medical imaging and radiation sciences education with the subthemes of student education, ethical considerations and responsible use and curriculum integration of artificial intelligence in relation to learning and assessment. Conclusion: Participants used artificial intelligence for learning and assessment by generating ideas to enhance academic writing, as a learning tool, finding literature, language translation and for enhanced efficiency. Simulation-based artificial intelligence supports students' clinical learning, and artificial intelligence within the clinical departments assists with improved patient outcomes. However, participants expressed concerns about the reliability and ethical implications of artificial intelligence-generated information. To address these concerns, participants suggested integrating artificial intelligence into medical imaging and radiation sciences education, where educators need to educate students on the responsible use of artificial intelligence in learning and consider artificial intelligence in assessments. Implications for practice: The study findings contribute to understanding medical imaging and radiation sciences students’ use of artificial intelligence and may be used to develop evidence-based strategies for integrating artificial intelligence into the curriculum to enhance medical imaging and radiation sciences education and support students.

Original languageEnglish
Pages (from-to)60-66
Number of pages7
JournalRadiography
Volume30
DOIs
Publication statusPublished - Dec 2024

Keywords

  • AI
  • Generative AI
  • Large language models
  • Radiography education
  • Radiography students
  • Teaching and learning

ASJC Scopus subject areas

  • Research and Theory
  • Radiological and Ultrasound Technology
  • Health Professions (miscellaneous)
  • Radiology, Nuclear Medicine and Imaging
  • Assessment and Diagnosis

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