Effect of AI-Based Adaptive Learning Class: Fundamental of Nursing Medication Calculation

Objectives This study aimed to evaluate the effects of AI-based adaptive learning on medication calculation performance and competency in a fundamental nursing course, and to analyze the structural relationships among these variables. Methods The study was conducted with 160 second-year nursing students at a university. The AI-based adaptive learning program was implemented over two weeks and included a pre-diagnostic test, personalized content provided through an LMS, instructor feedback, and a post-test. Differences in medication calculation performance were analyzed using paired t-tests to compare pre- and post-test scores. Learning experiences were analyzed through a word cloud visualization. Results The participants' mean age was 22.3 years, with 125 female students(78.1%) and 35 male students(21.9%). The mean medication calculation performance was significantly improved from a pre-test score of 10.25 to a post-test score of 13.34(t=-17.189, p=.000). The overall satisfaction score was 4.75 with high scores in general satisfaction (4.74), engagement (4.81), interest and enjoyment (4.70), adaptive learning requirements (4.72), and skill improvement (4.80). Open-ended responses highlighted “repetition,” “videos,” and “support” as positive aspects, while “unit conversion,” “calculation methods,” and “problems” were cited as challenges. The importance of “repetition” and “learning” was emphasized in reflections. These results demonstrate that AI-based adaptive learning positively influenced the students' drug dosage calculation performance. Conclusions AI-based adaptive learning is an effective instructional strategy to enhance nursing students' medication dosage calculation performance. This study provides foundational data for the development and validation of AI-based adaptive learning models in nursing education. Further research is recommended to explore its broader applications and effectiveness in the field.