Exploring the experiences of medical sciences faculty members and students in using artificial intelligence applications: a qualitative descriptive study

Abstract

Abstract Background The rapid emergence of generative artificial intelligence (AI) is reshaping medical education worldwide. However, existing studies are predominantly quantitative and Western-centric providing limited empirical evidence regarding how medical sciences faculty members and students experience and interpret the use of AI within constrained educational and socio-cultural contexts. This study aimed to explore the experiences of medical sciences faculty and students regarding AI use at Shiraz University of Medical Sciences, Iran. Methods A qualitative descriptive study was conducted from November 2024 to April 2025. A total of 16 participants (eight faculty members and eight students) from diverse medical disciplines with prior AI experience recruited using purposive and snowball sampling. Data were collected through semi‑structured, in‑depth interviews (50–60 min) supported by field notes and continued until data saturation was achieved. Interviews were transcribed verbatim and analyzed using Graneheim and Lundman’s qualitative content analysis with MAXQDA 24. Trustworthiness was ensured through qualitative rigor criteria, following COREQ guidelines. Results Five overarching themes with 16 subthemes emerged from the study: 1) functional and practical value of AI; 2) human oversight, ethics and authenticity; 3) perceived capacity building and institutional readiness; 4) cognitive, emotional, and behavioral influence; and 5) socio-cultural dynamics and future transformation. However, participants’ experiences varied based on their roles: faculty members used AI strategically for routine tasks preserving cognitive effort for more complex ones, whereas students exhibited a more relational reliance on AI, which appeared to increase their vulnerability to automation bias. Both groups expressed concerns about potential "intellectual deskilling" and infrastructure barriers, conceptualized as a geopolitical algorithmic divide that limits equitable access. Conclusions The experiences of AI use among medical sciences faculty and students are context-dependent and conditional rather than uniformly positive or negative. Its sustainable integration into medical education involves a complex negotiation between efficiency and significant ethical, cognitive and institutional challenges. these findings highlight the potential need for a pedagogical shift toward evaluative judgment and institutional support to bridge the geopolitical digital divide, alongside human-in-the-loop supervision to help mitigate the risk of educational deskilling. Future longitudinal research is recommended to explore how perceptions, ethical concerns, and usage patterns evolve as AI becomes more embedded in medical education.