Exploring ChatGPT-4o-generated reflections: Alignment with professional standards in diagnostic radiography: A pilot...

Area of Science:

• Medical Education
• Artificial Intelligence in Healthcare
• Diagnostic Radiography

Background:

• AI tools like ChatGPT-4o are emerging in education.
• This study investigates ChatGPT-4o's utility for reflective practice in medical radiation science (MRS).
• Focus is on AI-generated reflection quality against professional standards.

Purpose of the Study:

• To evaluate ChatGPT-4o's effectiveness in supporting reflective practice for medical radiation science students.
• To assess the quality of AI-generated reflections based on professional standards, depth, clarity, and relevance.
• To understand the limitations of AI in fostering authentic reflective learning.

Main Methods:

• Four clinical scenarios for third-year diagnostic radiography students were used as prompts.
• ChatGPT-4o generated reflective responses, reviewed by three experts.
• Evaluations used professional capability domains, safety standards, and criteria like clarity, depth, and authenticity.

Main Results:

• Reflections were generally well-structured and clear but lacked emotional depth and person-centered insights.
• Inter-rater reliability varied by scenario complexity, with Scenario 3 showing moderate to excellent agreement and Scenario 2 showing poor to fair agreement.
• Qualitative feedback highlighted limitations in AI's empathetic reflection and critical self-awareness.

Conclusions:

• ChatGPT-4o can generate structured reflections aligned with professional frameworks in MRS education.
• AI's lack of emotional and contextual depth restricts its ability to substitute genuine reflective practice.
• Hybrid models integrating AI with educator guidance offer a promising approach to enhance reflective learning efficiency and authenticity.