AI-ASSISTED AND TRADITIONAL ASSESSMENT OF THERAPEUTIC COMMUNICATION SKILLS: A QUASI-EXPERIMENTAL STUDY WITH STANDARDIZED PATIENT-BASED EDUCATION

Authors : Orkun Erkayıran

Pages : 107-117

Doi:10.52831/kjhs.1755519

View : 194 | Download : 363

Publication Date : 2025-12-30

Article Type : Research Paper

Abstract :Objective: This study aims to examine the effectiveness of standardized patient (SP) based therapeutic communication training and the accuracy, reliability, and feasibility of artificial intelligence (AI) supported assessment in improving nursing students\\\' communication competencies. The premise of the study is to determine to what extent AI can assess complex clinical skills, such as interpersonal communication, in harmony with human raters. Method: This quasi-experimental pre-test/post-test study, including a three-month follow-up measurement, was completed with 64 students (intervention group n=33, control group n=31) after eight of the initially randomized 72 students left the study during the process. A five-session SP-supported therapeutic communication training was applied to the intervention group, while the control group received standard education. Communication performances were scored at baseline, post-training, and follow-up by both human raters and an AI-based assessment system analyzing anonymized transcripts containing coded nonverbal behaviors. Data were analyzed using mixed-design ANOVA, independent samples t-tests, Pearson correlations, and intraclass correlation coefficients (ICC). Results: Students in the intervention group showed significant improvement in therapeutic communication skills compared to the control group at all measurement times (p<.001), and this improvement was maintained at the three-month follow-up. AI-supported assessments showed near-perfect agreement with human evaluations at all time points (ICC=0.97-0.99). This finding demonstrates that AI can offer consistent, objective, and high-reliability communication assessment. Conclusion: The integration of SP-based training and AI-supported assessment effectively improves nursing students\\\' interpersonal communication competencies and supports the retention of learning. AI makes a strong contribution to competency-based education models by providing scalable, unbiased, and detailed feedback. The findings emphasize the importance of integrating structured simulation experiences and AI-based assessment approaches into nursing education. Future research is recommended to focus on long-term follow-up, multi-center samples, and ethical dimensions regarding AI use in education.
Keywords : Yapay Zekâ, Hasta Simülasyonu, Hemşirelik Eğitimi, Hemşirelik Öğrencileri, Yarı Deneysel Çalışma

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