Funda Çam, Ayşe Dost, Mahmut Dağcl
Advancements in large language models have increased the use of AI-generated content in nursing education; however, concerns remain regarding the reliability and quality of such outputs. This study aimed to evaluate the reliability and quality of nursing care plans generated by ChatGPT-5 and to compare them with those previously produced using ChatGPT-3.5. This methodological follow-up study used a descriptive and comparative design. Forty nursing diagnoses previously analyzed in 2023 were re-evaluated using ChatGPT-5 to ensure methodological continuity. Generated care plans were assessed using a descriptive criteria form and the DISCERN instrument. Paired comparisons were conducted using the Wilcoxon signed-rank test. ChatGPT-5-generated care plans showed significantly higher numbers of total and accessible references and higher DISCERN reliability and total scores than those reported in the 2023 evaluation of ChatGPT-3.5. No significant differences were observed in care-related information quality or overall quality scores; however, category-based analyses showed a shift toward higher quality levels. ChatGPT-5-generated nursing care plans showed higher reference accessibility and reliability scores than those reported in the 2023 evaluation of ChatGPT-3.5 using the same nursing diagnoses, although human oversight remains essential, particularly regarding reference accuracy.