The Effect of AI-Generated Case Studies on the Knowledge and Satisfaction of Psychiatry Nursing Students

Rohman Hikmat, Popy Siti Aisyah, Endah Yuliany Rahmawati, Helmy Hazmi

= http://dx.doi.org/10.24990/injec.v10i2.1001
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Abstract


Background: Artificial intelligence (AI) is increasingly being utilized in higher education nursing to assist in the delivery of complex content, such as psychiatric nursing. AI-generated case studies are clinical case studies created by an OpenAI GPT-4-based AI system, designed to provide contextual, adaptive, and relevant learning scenarios for clinical practice. The case content was validated by three psychiatric nursing lecturers and selected based on its relevance to course learning outcomes. However, empirical evidence regarding the effectiveness of this approach in psychiatric nursing education remains limited.

Objective: To determine the effect of AI-generated case studies-based learning on improving psychiatric nursing knowledge and student learning satisfaction.

Method: This study used a quasi-experimental design with a non-equivalent control group and a pretest–Post-test approach. The sample consisted of 80 undergraduate nursing students (inclusion: having completed at least 75% of the psychiatric nursing theory material; exclusion: not attending ≥1 learning session), divided purposively into an intervention group (n = 40) and a control group (n = 40). The intervention consisted of three 90-minute AI-generated case study sessions, each with small group work, structured discussions facilitated by the lecturer, direct feedback, and a short quiz at the end of the session. Knowledge was measured with 10 multiple-choice questions, and learning satisfaction was measured using the Nursing Student Satisfaction Scale. Data were non-normally distributed (p < 0.05, Shapiro–Wilk test), therefore, they were analyzed using the Wilcoxon Signed-Rank and Mann–Whitney U tests.

Results: The intervention group experienced a significant increase in knowledge scores (median pretest = 6, Post-test = 9; Z = –5.612; p < 0.001), while the control group did not (median pretest = 6, Post-test = 7; Z = –1.923; p = 0.054). The difference in Post-test scores between groups was significant (U = 428.0; p < 0.001). Learning satisfaction scores were also higher in the intervention group (median = 82.5) than in the control group (median = 78.0), although approaching statistical significance (U = 642.5; p = 0.052).

Conclusion: AI-generated case studies-based learning significantly improves psychiatric nursing knowledge and tends to increase student learning satisfaction compared to conventional methods.

Keywords


artificial intelligence; case-based learning; nursing education; psychiatric nursing; student satisfaction

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References


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