Artificial Intelligence in Medical Education: Knowledge, Attitudes, and Practices of AI Adoption among Teaching Staff and Medical Students at the University of Zawia

The integration of artificial intelligence in medical education represents a transformative opportunity, yet a comprehensive understanding of academic stakeholders' knowledge, attitudes, and practices regarding AI remains limited. This study assessed the current state of AI awareness, perceptions, and utilization among medical educators and students to inform strategic implementation frameworks. A cross-sectional survey was administered to 121 participants (25.6% students, 34.7% teaching assistants, 39.7% faculty members; 80.2% female) at a medical institution, examining self-assessed AI knowledge, ethical considerations, usage patterns, and integration preferences. Participants demonstrated moderate AI knowledge (28.1% reporting high/very high understanding, 57% medium), with the highest familiarity in translation (55.4%) and academic writing tools (49.6%), but limited awareness of data analysis applications (17%). While 93.4% believed AI enhances creativity and 74.4% supported integrating smart classrooms into curricula, significant barriers persisted: 71.1% reported no prior AI use in teaching/study (despite 64.5% using chatbots), 73.6% had never attended AI training workshops, and privacy concerns dominated ethical considerations (74.4% identifying privacy as primary concern, 58.7% citing it as a major adoption barrier). Notably, 82.6% did not consider AI a necessary component of current educational programs, reflecting cautious optimism rather than enthusiastic adoption. The findings reveal a critical gap between theoretical interest in AI and practical implementation in medical education, characterized by moderate knowledge levels, strong ethical reservations, and insufficient training opportunities. Strategic integration requires addressing privacy concerns, developing structured faculty development programs, and implementing phased adoption strategies beginning with low-risk applications to build institutional confidence before advancing to more complex AI implementations. These insights provide a roadmap for medical education institutions navigating the evolving AI landscape.