INTEGRATING AI-ASSISTED MEDICAL ENGLISH MODULES INTO CLINICAL SIMULATION: IMPACT ON NURSING STUDENTS' COMMUNICATION CONFIDENCE IN TELEHEALTH SETTINGS
Abstract
The use of Medical English in telehealth settings has become an essential requirement due to the rapid global expansion of remote healthcare counseling and consultations. However, nursing students often experience a lack of communication confidence when interacting in English within virtual clinical scenarios. This study aimed to evaluate the impact of integrating AI-assisted Medical English modules into clinical simulation on nursing students' communication confidence in telehealth settings. A quantitative method with a quasi-experimental pre-test and post-test design was employed. The study involved 57 final-year nursing students at STIKes Darussalam Lhokseumawe selected via total sampling. The research was conducted in July 2026. Data were collected using a telehealth communication confidence scale questionnaire administered before (pre-test) and after (post-test) the AI-assisted intervention. Data were analyzed univariately and bivariately using the Paired Sample t-test. The results demonstrated a significant increase in the mean communication confidence score from 52.4 (moderate/low category) at pre-test to 78.6 (good category) at post-test. Statistical analysis revealed a p-value of 0.001 (p<0.05), indicating a statistically significant effect of integrating AI-assisted Medical English modules into clinical simulations on enhancing nursing students' communication confidence in telehealth settings. It is concluded that incorporating AI tools into remote counseling simulations effectively improves nursing students' English communication readiness and confidence.
Keywords
Monthly Statistics (Last 12 Months)
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