G-4556

The Role of AI-Supported Case-Based Learning in Nursing Students’ Achievement Motivation: A quasi-experimental study

 Mohammad Souri ¹ ℗, Parastoo Amiri ², Elnaz Bornasi ³, Rasool Mohammadi ⁴, Azadeh Kordestani Moghaddam ⁵, Parastou Kordestani Moghadam ⁶ ©   

 
Abstract

1. Introduction: Motivation for achievement is crucial for professional success and quality academic performance in nursing students, but traditional teaching methods often face limitations in motivating and actively engaging students, which do not optimize this feature. In recent years, the use of a case-based learning approach and advances in artificial intelligence technology, especially in the field of chatbots as a new tool in medical education, can strengthen motivation through immediate feedback and personalized learning. Given the lack of studies in this field in Iran, this study aimed to determine the effect of case-based learning using chatbots on the motivation for achievement of nursing students. 2. Methods and Materials: This single-group quasi-experimental study was conducted in 1404 (2025) on 36 third-year nursing students of the Khorramabad School of Nursing and Midwifery, including 20 males (55.6%) and 16 females (44.4%). Initially, the students were assessed for their Gardner Multiple Intelligence scores and GPA, and then the educational intervention, which consisted of 12 sessions, was delivered in three phases: pre-test (T1), the first six weeks with case-based learning (T2), and the second six weeks with a combination of case-based learning and the use of ChatGPT (T3). Clinical cases were selected from the *New England Journal of Medicine Case Challenges* website, and two 45-minute workshops were held after the sixth week to train students on the use of AI chatbots. Data were collected using the standard Hermans Achievement Motivation Questionnaire. Analyses were performed using SPSS version 25 with repeated measures ANOVA and Bonferroni post hoc tests. 3. Results: The mean achievement motivation score increased from 83.83 (SD=9.2) at pre-test to 88.81 (SD=7.6) at six weeks, and 91.50 (SD=7.0) at twelve weeks. Repeated measures ANOVA indicated a significant effect of time (F=13.66, p0.001). Pairwise comparisons showed a significant increase in motivation between pre-test and six weeks (p=0.005) and between pre-test and twelve weeks (p0.001), whereas no significant difference was observed between six and twelve weeks (p=0.120). 4. Conclusion and Discussion: The findings of this study showed that case-based learning combined with AI support through ChatGPT can significantly increase the motivation to progress of nursing students. The greatest change occurred in the first six weeks and remained stable thereafter. These results highlight the importance of employing innovative and technological educational methods to increase student motivation. Limitations such as small sample size and conducting the study in a single center limit the generalizability of the findings. Therefore, future studies with larger samples and in diverse educational settings are recommended. Overall, integrating case-based learning with AI tools such as ChatGPT can be an effective approach to increase motivation to progress in nursing education.

Keywords: Achievement motivation, nursing education, case-based learning, artificial intelligence, ChatGPT