Advancing Nursing Education: Evaluating Artificial Intelligence (AI) and Escape Room (ER) Approaches for E‐Learning and Interpretation Skills

Introduction: The spread of new educational technologies has challenged the cold and inflexible framework of traditional education, aiming to replace centuries-old methods with short, low-cost methods to improve learning and educational productivity. Thus, this study aimed to compare the effects of artificial intelligence (AI) and escape rooms (ERs) on e-learning satisfaction and the skill of arterial blood gas (ABG) interpretation in nursing students. Materials and Methods: The current study was conducted as a quasi-experiment on 91 nursing students in the third to sixth academic semesters, with a convenient sampling approach to AI, ER, and control groups by balanced block randomization. In the AI group, participants used ChatGPT; the ER group used the Twine platform; and the control group used the traditional method once a week. The E-learning satisfaction scale (ELSS) and arterial blood gas interpretation scale (ABGIS) were filled before and two weeks after the end of interventions. Data were analyzed using descriptive and inferential statistical tests in SPSS V.25 at a 0.05 significance level. Results: < 0.001). The intervention made a big difference-explaining nearly 90% of the changes in ELSS and about 40% in ABGIS. Among all the factors, AI stood out the most, driving major improvements in both learning outcomes and student engagement. Conclusion: The use of AI compared to the educational ER and conventional methods significantly increases the learning of ABG interpretation and satisfaction with e-learning. It is suggested that the positive and effective capabilities of AI be used as an educational assistant to enhance learning and teaching experiences in medicine and nursing.