Investigating the effectiveness of artificial intelligence-powered robots in learning medical English: A mixed-methods study on sociolinguistic competence and perceptions

Abstract

Recent advancements in Artificial Intelligence-powered robots have introduced a variety of educational technologies aimed at accommodating standard varieties of languages. However, the adaptiveness of the knowledge bases of these robots in establishing adaptive language learning at universities remains underexplored. This quasi-experimental study involved 1041 students from 13 academic disciplines with diverse first languages. Utilizing a parallel design, while students in the control group learned medical English skills through non-adaptive AI-powered robots, students in the experimental group learned the same skills through adaptive AI-powered robots. We assessed the effectiveness of learning medical English through Artificial Intelligence-powered robots with(out) adaptive knowledge bases, employing Objective Structured Video Exams for formative assessment. Additionally, the participants shared their perceptions of Artificial Intelligence-powered robots in medical English education through semi-structured interviews. Quantitative findings indicated that students using adaptive Artificial Intelligence-powered robots showed a greater sociolinguistic competence in medical English compared to those learning with non-adaptive robots. The qualitative findings suggested that the students in disciplines requiring direct interaction with patients and healthcare staff expressed a more positive perception of learning medical English through Artificial Intelligence-powered robots. While both groups acknowledged the value of the course, those engaging with adaptive systems demonstrated a great appreciation for the learning process and outcomes. Based on these results, we offer recommendations for researchers on effectively integrating Artificial Intelligence-powered robots into university language education, highlighting the potential for enhanced learning experiences through adaptive technologies.