Abstract
Over the past few decades, English-medium instruction (EMI) has been widely adopted in higher education to strengthen students’ global competence. In Taiwan’s nursing education, EMI courses aim to enhance professional communication in international healthcare settings; however, they also pose challenges in balancing language proficiency and clinical knowledge. Traditional lectures often fail to promote engagement or improve oral communication. Integrating artificial intelligence (AI) with multimodal teaching design offers a promising pathway for creating interactive, adaptive, and student-centered learning experiences. This study developed an EMI medical record reading curriculum integrating a multimodal design with AI technology and examined its effects on nursing students’ learning engagement and English oral expression. A quasi-experimental design was implemented with third-year nursing students enrolled in an “EMI Medical Record Reading” course. The experimental group (n = 51) received AI-based multimodal instruction combining text, video, audio, and real-time AI feedback, while the control group (n = 48) received traditional lectures. Instruments included the College Student Learning Engagement Scale, EMI Learning Perception Scale, English Oral Expression Assessment, and semi-structured focus group interviews. Quantitative data were analyzed using descriptive statistics and ANCOVA in IBM SPSS Statistics 30, and qualitative data were analyzed using the constant comparative method. Results showed that the experimental group achieved significantly higher learning engagement (F = 21.38, p < .001) and oral expression performance (F = 12.36, p < .05) than the control group. The EMI Learning Perception Scale also indicated higher satisfaction and perceived usefulness of English-mediated learning (F = 13.35, p < .05). Complementary qualitative findings revealed that students felt AI-assisted multimodal learning improved their comprehension, peer interaction, and comfort in English communication, while personalized AI feedback reduced anxiety and fostered learning confidence. These results suggest that AI-based multimodal teaching is an effective pedagogical approach for EMI nursing courses. It enhances engagement, builds confidence, and improves oral communication competence, thereby transforming nursing education into a more interactive and student-centered environment.
Notes
Reference list included in the attached file.
This presentation was accepted to the event as a poster. The presenter chose a slide format to convey the information.
Sigma Membership
Non-member
Lead Author Affiliation
Chang Gung University of Science and Technology, Guishan District, Taoyuan City, Taiwan
Type
Poster
Format Type
Text-based Document
Study Design/Type
Quasi-Experimental Study, Other
Research Approach
Quantitative Research
Keywords:
Curriculum Development, Teaching and Learning Strategies, Nursing Education, English as a Second Language, Artificial Intelligence, Problem-Based Learning, Teaching Methods
Recommended Citation
Hsieh, Pei-Lin, "AI-Based Multimodal Teaching: Effects on Nursing Students’ Learning Engagement and Oral Expression" (2026). International Nursing Research Congress (INRC). 25.
https://www.sigmarepository.org/inrc/2026/posters_2026/25
Conference Name
37th International Nursing Research Congress
Conference Host
Sigma Theta Tau International
Conference Location
Toronto, Ontario, Canada
Conference Year
2026
Rights Holder
All rights reserved by the author(s) and/or publisher(s) listed in this item record unless relinquished in whole or part by a rights notation or a Creative Commons License present in this item record. All permission requests should be directed accordingly and not to the Sigma Repository. All submitting authors or publishers have affirmed that when using material in their work where they do not own copyright, they have obtained permission of the copyright holder prior to submission and the rights holder has been acknowledged as necessary.
Review Type
Abstract Review Only: Reviewed by Event Host
Acquisition
Proxy-submission
Date of Issue
2026-07-24
AI-Based Multimodal Teaching: Effects on Nursing Students’ Learning Engagement and Oral Expression
Toronto, Ontario, Canada
Over the past few decades, English-medium instruction (EMI) has been widely adopted in higher education to strengthen students’ global competence. In Taiwan’s nursing education, EMI courses aim to enhance professional communication in international healthcare settings; however, they also pose challenges in balancing language proficiency and clinical knowledge. Traditional lectures often fail to promote engagement or improve oral communication. Integrating artificial intelligence (AI) with multimodal teaching design offers a promising pathway for creating interactive, adaptive, and student-centered learning experiences. This study developed an EMI medical record reading curriculum integrating a multimodal design with AI technology and examined its effects on nursing students’ learning engagement and English oral expression. A quasi-experimental design was implemented with third-year nursing students enrolled in an “EMI Medical Record Reading” course. The experimental group (n = 51) received AI-based multimodal instruction combining text, video, audio, and real-time AI feedback, while the control group (n = 48) received traditional lectures. Instruments included the College Student Learning Engagement Scale, EMI Learning Perception Scale, English Oral Expression Assessment, and semi-structured focus group interviews. Quantitative data were analyzed using descriptive statistics and ANCOVA in IBM SPSS Statistics 30, and qualitative data were analyzed using the constant comparative method. Results showed that the experimental group achieved significantly higher learning engagement (F = 21.38, p < .001) and oral expression performance (F = 12.36, p < .05) than the control group. The EMI Learning Perception Scale also indicated higher satisfaction and perceived usefulness of English-mediated learning (F = 13.35, p < .05). Complementary qualitative findings revealed that students felt AI-assisted multimodal learning improved their comprehension, peer interaction, and comfort in English communication, while personalized AI feedback reduced anxiety and fostered learning confidence. These results suggest that AI-based multimodal teaching is an effective pedagogical approach for EMI nursing courses. It enhances engagement, builds confidence, and improves oral communication competence, thereby transforming nursing education into a more interactive and student-centered environment.
Description
This session presents an AI-based multimodal English-Medium Instruction (EMI) teaching design implemented in a nursing course. Participants will explore how integrating AI feedback and multimodal strategies enhances nursing students’ learning engagement, confidence, and oral communication skills in clinical English contexts, offering evidence-based insights for innovative EMI pedagogy in nursing education.