Investigating the usefulness of artificial intelligence-driven robots in developing empathy for English for medical purposes communication: The role-play of Asian and African students

Studies have shown that integrating Artificial Intelligence into robot-assisted language learning ushers in an immersive situation to establish empathy for communication competence and enjoyment. To investigate the usefulness of Artificial Intelligence-driven robots in learning English for Medical Purposes oral skills, this quasi-experimental study was conducted through an embedded mixed methods design in the 2024 academic year. One thousand and forty male (n = 398) and female (n = 642) students of different academic disciplines from the Isfahan University of Medical Sciences were included in the study and grouped under three categories. They were randomly assigned to control (n = 520) and experimental (n = 520) groups. In the qualitative phase, students' communication enjoyment was debriefed through semi-structured interviews. Simultaneously, in the quantitative phase, once the participants watched simulcast lectures and joined in conversation with Artificial Intelligence-driven (non)-robots, their academic and professional oral skills were assessed. The collected data were the participants' responses to the interviews and formative assessment of their progress and performance through Objective Structured Video Examinations and Mini-Clinical Evaluation Exercises. The interview results suggested that the participants had empathy in joining conversations with robots. The statistical analysis indicated that in performing role-play to teach oral skills to the Artificial Intelligence-driven robot, the participants achieved significantly greater communication competence than those who did role-play for virtual agents. Students of Medicine were great academic and professional achievers as they were significantly successful in establishing empathy and communication. The findings could open up further prospects for using Artificial Intelligence-driven robots in discipline-specific language learning contexts.