Artificial Intelligence Enhanced Collaborative Learning Among Medical and Non-Medical Students – A Comparative Cross-Sectional Study

Abstract

Introduction: The integration of Artificial Intelligence into educational methodologies, particularly AI-enhanced collaborative learning (AI-ECL), has garnered significant attention in recent years. AI promises to transform educational experiences by enhancing critical thinking and problem-solving skills. Objective: This study aimed to evaluate and compare the experiences, perceptions, and outcomes of AI-ECL among undergraduate medical and non-medical students in various colleges in Lahore, Pakistan. Methods: A comparative cross-sectional study was conducted with 195 undergraduate students, including 101 medical and 94 non-medical students. Data were collected through a pre-tested self-administered, closed ended proforma and analyzed using descriptive statistics and chi-square tests to assess differences between the two groups. Results: The findings revealed that a majority of medical (85.1%) and non-medical (80.9%) students reported using AI tools for learning or assignments. Medical students were more likely to trust AI with personal data and believed in its significant role in the future of education (92.1% vs. 77.7%). Significant differences were noted in AI usage for medical studies (p < 0.001) and trust in AI handling personal data (p = 0.019). However, both groups expressed similar confidence in using AI for learning. Conclusion: AI-ECL was positively perceived by both groups, but medical students showed higher acceptance and trust. Addressing these trust issues is essential, along with ensuring AI tools meet the specific needs of different disciplines to enhance educational outcomes.

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