Needs Analysis for the Development of an AI-Based Medical English Teaching Program in Higher Education

Özet

The primary objective of this study is to evaluate the effectiveness of an artificial intelligence-based Medical English for Specific Purposes (ESP) course within the context of higher education, and to conduct a comprehensive needs analysis by taking into account the perspectives of students, faculty members, and artificial intelligence-based applications. In the study, a convergent parallel design, one of the mixed methods designs that allows for the integrated interpretation of both qualitative and quantitative data, was adopted. The research was conducted within the Faculty of Medicine at a public university in Turkey, and data were collected through semi-structured interviews, a needs analysis survey, document analysis, and academic achievement records. The study group consisted of 204 medical students currently enrolled in the Faculty of Medicine and six faculty members with at least three years of professional teaching experience. The findings reveal that the existing Medical English for Specific Purposes curriculum does not sufficiently support students’ fundamental linguistic skills such as professional communication, academic writing, listening, and speaking. Students reported particular deficiencies in areas such as terminology knowledge, case-based speaking practices, academic article analysis, and doctor-patient communication content. Faculty members, on the other hand, emphasized the necessity of updating the program’s learning outcomes, strengthening the connection between course content and professional contexts, and moving beyond an exam-oriented approach by incorporating formative and process-based assessment strategies throughout the instructional period. Furthermore, the views of both students and faculty members underscored the need for effective integration of digital resources, simulation-based learning, and technologies such as augmented reality within the instructional process. Survey data indicate that students perceive themselves as having low proficiency particularly in clinical communication skills. Among the preferred instructional methods during the course were group work, role-playing, question-and-answer activities, and practice-based learning. Analyses conducted with the support of artificial intelligence also recommended that the program be supported by personalized learning pathways, verbal feedback systems, and speech recognition technologies. In conclusion, the findings highlight the necessity of designing a Medical English for Specific Purposes course that is compatible with the requirements of the contemporary era—interactive, personalized, and supported by educational technologies. In this context, the model proposed within the scope of the study offers original contributions at both theoretical and practical levels.