Shaping the Future of Radiography Education: Lessons From <scp>ChatGPT</scp> and Generative <scp>AI</scp>

Abstract

Generative artificial intelligence (AI), particularly large language models such as ChatGPT, is increasingly influencing learning and continuing professional development (CPD) across health professions. The radiography discipline is well positioned to explore the benefits and limitations of these technologies. This article presents a narrative review and conceptual synthesis of emerging evidence on the use of generative artificial intelligence in radiography education. Drawing on emerging radiography-specific studies, the paper synthesises evidence relating to image critique, professional communication training, simulation-based learning, CPD planning and reflective practice. Evidence suggests that ChatGPT can provide structured guidance, support self-assessment and scaffold learning processes that bridge classroom knowledge and clinical expectations. In image evaluation tasks, generative AI may identify broad positioning and exposure issues and prompt metacognitive reasoning when learners are encouraged to critically appraise its outputs. In communication tasks, AI systems can offer structural support but consistently lack the relational nuance essential for safe clinical interactions. In reflective tasks, AI-generated reflections typically lack emotional depth, professional and ethical insight and accountability. Furthermore, custom AI-driven simulations show promise as tools for safe experimentation; however, their outputs commonly default to technically correct responses that provide limited contextual or professional insight. These findings suggest that the value of generative AI lies in how it is pedagogically framed. Rather than advancing algorithmic or performance-based claims, this paper interprets generative AI as a pedagogical artefact whose educational value is contingent on framing, educator oversight and learner critical engagement. The review integrates fragmented evidence with educational theory and professional regulation and proposes a conceptual framework for the responsible integration of generative AI in radiography education. By foregrounding AI literacy, ethical governance and professional accountability, this paper offers an integrative, design-level contribution to support educators and professional bodies navigating the adoption of generative AI across the radiography education continuum.

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