Empowering future nurses: Transforming English speaking skills with VAK-AR digital learning

Effective communication is a fundamental skill for nursing professionals, especially in clinical settings where English proficiency is crucial for patient care and interdisciplinary collaboration. This study aims to examine the effectiveness of a Visual-Auditory-Kinesthetic (VAK) learning model integrated with Augmented Reality (AR) in enhancing the English-speaking skills of nursing students. Employing a pre-experimental design with a one-group pre-test and post-test, the research involved undergraduate nursing students from Universitas Muhammadiyah Magelang (Unimma) and Stikes Surya Global enrolled in an English for Specific Purposes (ESP) course. The material focused on two core topics: "Giving Admission" and "Symptoms and Physical Problems." Data were analyzed using a paired t-test to determine significant differences before and after the intervention. The findings indicate a substantial improvement in students' speaking proficiency, with average scores increasing from 58.1% (pre-test) to 89.6% (post-test), achieving a gain score of 76.14%, categorized as high. The study concludes that the VAK-AR learning model is effective in improving English-speaking skills within nursing contexts. Practically, this model offers an engaging, immersive digital learning experience that supports students’ linguistic development in healthcare settings. The results provide valuable insights into integrating educational technology with language instruction for nursing education.

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Publication Details

Journal
Edelweiss Applied Science and Technology
Published
2025-06-24
DOI
https://doi.org/10.55214/25768484.v9i6.8331
Primary Topic
AI in Service Interactions
Type
article
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article

Empowering future nurses: Transforming English speaking skills with VAK-AR digital learning

Athia Fidian, Sri Nur Hartiningsih, Arif Rohman, Nur Hidayanto Pancoro Setyo Putro et al.
Edelweiss Applied Science and Technology
AI in Service Interactions
article

Empowering future nurses: Transforming English speaking skills with VAK-AR digital learning

Athia Fidian, Sri Nur Hartiningsih, Arif Rohman, Nur Hidayanto Pancoro Setyo Putro, Maryani Maryani
article en

Abstract

Effective communication is a fundamental skill for nursing professionals, especially in clinical settings where English proficiency is crucial for patient care and interdisciplinary collaboration. This study aims to examine the effectiveness of a Visual-Auditory-Kinesthetic (VAK) learning model integrated with Augmented Reality (AR) in enhancing the English-speaking skills of nursing students. Employing a pre-experimental design with a one-group pre-test and post-test, the research involved undergraduate nursing students from Universitas Muhammadiyah Magelang (Unimma) and Stikes Surya Global enrolled in an English for Specific Purposes (ESP) course. The material focused on two core topics: "Giving Admission" and "Symptoms and Physical Problems." Data were analyzed using a paired t-test to determine significant differences before and after the intervention. The findings indicate a substantial improvement in students' speaking proficiency, with average scores increasing from 58.1% (pre-test) to 89.6% (post-test), achieving a gain score of 76.14%, categorized as high. The study concludes that the VAK-AR learning model is effective in improving English-speaking skills within nursing contexts. Practically, this model offers an engaging, immersive digital learning experience that supports students’ linguistic development in healthcare settings. The results provide valuable insights into integrating educational technology with language instruction for nursing education.

Edelweiss Applied Science and TechnologyVol. 9(6)
Yogyakarta State University (ID)
Openalex Percentile: Top 93%
AI in Service Interactions
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Empowering future nurses: Transforming English speaking skills with VAK-AR digital learning — Athia Fidian, Sri Nur Hartiningsih, et al. · Edelweiss Applied Science and Technology (2025) | TGRS Research Map | TGRS