Evaluation of artificial intelligence fall school program at Smart University of Medical Sciences

Abstract

Background: Educational evaluation is one of the main pillars of educational systems, and course evaluation is a survey that students or course members complete at the end of a class or academic course. This study aims to evaluate the ‘Artificial Intelligence Fall School Program’ at Smart University of Medical Sciences. Methods: This study was conducted by collecting on various aspects of the course, including the course structure, teaching methods, instructors, scientific evaluations, and pre- and post-course tests. The course evaluation was conducted using an online questionnaire. In the initial phase of the study, the sample size was determined to be 96 participants, as calculated using Cochran’s formula. The research data were statistically analyzed at two levels: descriptive and inferential. Descriptive analysis was performed using statistical indicators such as frequency, percentage, and mean. The inferential analysis was conducted using the paired t test. Analyses were performed using SPSS 22. Results: From the viewpoint of the participants, all artificial intelligence (AI) schools in the field of medical sciences were deemed satisfactory. A paired t test was used to analyze and compare the pre-test and post-test scores of participants in the Fall AI schools. The results indicated an increase in the post-test scores of participants, following their involvement in the seven-week AI schools, compared to their pre-test scores. Conclusion: This evaluative study offers crucial insights into the effectiveness of the "Fall AI Schools" training program in fostering AI proficiency among medical professionals. The quantitative findings reveal a statistically significant positive response and learning outcomes among the participants across the seven specialized schools.