• Active Minds in the Age of AI: Student Engagement in Medical Education
  • Ali Madadi Mahani,1 AmirAli Moodi Ghalibaf,2,*
    1. Student Research Committee, School of Medicine, Kerman University of Medical Sciences, Kerman, Iran
    2. Student Research Committee, Mashhad University of Medical Sciences, Mashhad, Iran


  • Introduction: Student engagement is widely recognized as a cornerstone of effective medical education. Engaged learners not only acquire knowledge more deeply but also develop essential professional behaviors, reflective capacities, and collaborative skills that are vital for modern clinical practice. Traditionally, engagement has been promoted through active learning strategies such as problem-based learning, team-based activities, and simulation-based training. However, the rapid emergence of artificial intelligence (AI) is reshaping the educational landscape. AI-driven technologies, ranging from adaptive learning platforms to natural language processing chatbots, are altering how students interact with information, peers, and educators. Despite the growing implementation of AI tools, their influence on student engagement has not been systematically synthesized. This narrative review aims to explore how AI contributes to, challenges, and potentially redefines student engagement in medical education.
  • Methods: The present narrative review study conducted by searching electronic databases, including PubMed, Scopus, and Web of Science for English-language articles published between 2015 and 2025, August, using combinations of the keywords “artificial intelligence,” “student engagement,” and “medical education.” Peer-reviewed studies included. Sources focusing exclusively on technical aspects of AI without educational implications were excluded. Data were analyzed thematically, with particular attention to three dimensions of engagement: cognitive, emotional, and behavioral. The review emphasized integrative interpretation rather than systematic synthesis, in line with narrative review methodology.
  • Results: The review identified three dominant themes illustrating the role of AI in student engagement. As Cognitive Engagement: AI-powered adaptive learning systems and intelligent tutoring platforms have demonstrated the capacity to personalize instruction, adjust content difficulty, and provide immediate feedback. These tools encourage deeper cognitive processing by allowing students to progress at their own pace while challenging them to apply knowledge in contextually relevant scenarios. Additionally, AI-based clinical simulations and virtual patients enhance diagnostic reasoning and problem-solving, fostering higher-order cognitive engagement. As Emotional Engagement: AI applications such as conversational agents and virtual mentors were found to provide supportive learning environments that can reduce anxiety and increase confidence. Several studies reported that students felt more motivated and less intimidated when engaging with AI systems compared to traditional classroom assessments. However, concerns about depersonalization were also evident, with some learners expressing reluctance to rely too heavily on non-human feedback. As Behavioral Engagement: The introduction of AI in collaborative platforms has encouraged active participation and teamwork. By integrating data visualization, predictive analytics, and group-based problem-solving tools, AI has prompted students to engage more consistently with both peers and educators. Nonetheless, inequities in access to AI technologies and variability in digital literacy emerged as barriers that could potentially limit sustained behavioral engagement across diverse student populations. Across these dimensions, AI was consistently positioned not as a replacement for human educators but as an augmentation tool. Its impact on engagement was highly dependent on instructional design, institutional support, and the pedagogical intentionality of implementation.
  • Conclusion: This review shows that AI can play an important role in strengthening student engagement in medical education by supporting learning, motivation, and participation. However, its benefits will only be realized if it is integrated carefully into teaching approaches that value human interaction, fairness, and the development of professional identity. Rather than seeing AI only as a new technology, it should be understood as a chance to rethink students’ roles—from passive recipients of information to active and empowered learners. Future studies should examine the long-term effects of AI on engagement, address ethical challenges, and ensure equal access for all students. The ultimate success of AI in medical education will depend on using its potential while keeping the central aim of preparing compassionate, skilled, and engaged physicians.
  • Keywords: Artificial Intelligence, Student Engagement, Medical Education