Unveiling the Attitudes of Medical Faculty Towards the Integration of Artificial Intelligence in Medical Education
Year: 2025
Type: article
Abstract: OBJECTIVE: To explore the attitude of faculty members towards integration of artificial intelligence (AI) and to accelerate the appropriate adaptation of AI tools in medical education. STUDY DESIGN: A qualitative case study. Place and Duration of the Study: Department of Medical Education, Bahria University Health Sciences Campus, Karachi, Pakistan, from June to October 2023. METHODOLOGY: A qualitative case study design was employed using a social cognitive theory framework. Participants were selected using a purposive sampling technique. Inclusion criteria were faculty members with five years of teaching experience and who gave consent. Twenty-one participants were included in the study using purposive sampling technique. Data were collected using individual projective interviews and two focus group discussions using the photovoice methodology. Participants were given a presention on tools of AI which can be integrated in teaching and assessment. Pattern matching technique was used for data analysis. RESULTS: Most of the participants had limited awareness of tools of AI that can be integrated in medical education to enhance teaching and assessment strategies. However, few participants had a good understanding of AI and its utility. However, there was great variation in the attitude of faculty members. Participants voiced on fostering partnerships between healthcare organisations, educational institutes, and technology companies to pool resources and expertise. Faculty members suggested that there should be more faculty development programmes on AI applications in medical education. CONCLUSION: Medical faculty needs to be trained as they have limited knowledge and awareness of the integration of AI in medical education. KEY WORDS: Artificial intelligence, Medical education, Medical faculty.
Author: Khadija Farrukh Warraich
Institution: Bahria University
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4408221571