EXPLORING STUDENTS' PERCEPTION OF IMPLEMENTATION OF CHATBOX IN DIAGNOSTIC IMAGING COURSES

OBJECTIVES Despite the significant expansion of available resources for students, the utilization of chatbots in diagnostic imaging courses is limited. This study aims to address this gap by designing an artificial intelligence chatbot called Dibot to assist students by providing answers in the field and determine the impact of Dibot as a useful resource of knowledge for students in diagnostic imaging courses. MATERIALS & METHODS Dibot was introduced via two platforms, Telegram and Facebook Messenger. The perceptive implementation of Dibot was evaluated based on three main parts; Part A: Student's interaction, Part B: Perceived ease of use, and Part C: The perceived of usefulness. Survey was done via questionnaire on answering the perceptive implementation of Dibot while quizzes were done to determine either Dibot has an impact on students’ knowledge in this field. Data analysis involved the calculation of mean scores, standard deviation, and ANCOVA. RESULTS Experts' evaluations of Dibot yielded a high overall mean score of 4.33 ± 0.94, indicating their positive reception of the chatbot. Result for part A, with mean 4.59 ± 0.54 shows students agree that chatbot has good interaction with user. Regarding students' perception of usefulness, the study found a high mean score of 4.74, indicating that Dibot is a valuable tool in the students’ learning process. This positive perception suggests that Dibot effectively assists students in reviewing diagnostic imaging modalities, such as general x-ray, computed tomography, and magnetic resonance imaging. CONCLUSION In conclusion, the implementation of Dibot chatbot in diagnostic imaging courses address the limited availability of artificial intelligence chatbot and Dibot proves as a useful resource for students in diagnostic imaging courses. Despite the significant expansion of available resources for students, the utilization of chatbots in diagnostic imaging courses is limited. This study aims to address this gap by designing an artificial intelligence chatbot called Dibot to assist students by providing answers in the field and determine the impact of Dibot as a useful resource of knowledge for students in diagnostic imaging courses. Dibot was introduced via two platforms, Telegram and Facebook Messenger. The perceptive implementation of Dibot was evaluated based on three main parts; Part A: Student's interaction, Part B: Perceived ease of use, and Part C: The perceived of usefulness. Survey was done via questionnaire on answering the perceptive implementation of Dibot while quizzes were done to determine either Dibot has an impact on students’ knowledge in this field. Data analysis involved the calculation of mean scores, standard deviation, and ANCOVA. Experts' evaluations of Dibot yielded a high overall mean score of 4.33 ± 0.94, indicating their positive reception of the chatbot. Result for part A, with mean 4.59 ± 0.54 shows students agree that chatbot has good interaction with user. Regarding students' perception of usefulness, the study found a high mean score of 4.74, indicating that Dibot is a valuable tool in the students’ learning process. This positive perception suggests that Dibot effectively assists students in reviewing diagnostic imaging modalities, such as general x-ray, computed tomography, and magnetic resonance imaging. In conclusion, the implementation of Dibot chatbot in diagnostic imaging courses address the limited availability of artificial intelligence chatbot and Dibot proves as a useful resource for students in diagnostic imaging courses.