Exploring the influence of artificial intelligence integration on personalized learning: a cross-sectional study of undergraduate medical students in the United Kingdom - Kehinde Sunmboye; Hannah Strafford; Samina Noorestani; Malena Wilison-Pirie (BMC Medical Education)
Abstract Background With the integration of Artificial Intelligence (AI) into educational systems, its potential to revolutionize learning, particularly in content personalization and assessment support, is significant. Personalized learning, supported by AI tools, can adapt to individual learning styles and needs, thus transforming how medical students approach their studies. This study aims to explore the relationship between the use of AI for self-directed learning among undergraduate medical students in the UK and variables such as year of study, gender, and age. Methods This cross-sectional study involved a sample of 230 undergraduate medical students from UK universities, collected through an online survey. The survey assessed AI usage in self-directed learning, including students’ attitudes towards AI accuracy, perceived benefits, and willingness to mitigate misinformation. Data were analyzed using descriptive statistics and linear logistic regression to examine associations between AI usage and demographics. Results The analysis revealed that age significantly influenced students’ willingness to pay for AI tools (p = 0.012) and gender was linked to concerns about AI inaccuracies (p = 0.017). Female students were more likely to take steps to mitigate risks of misinformation (p = 0.045). The study also found variability in AI usage based on the year of study, with first-year students showing a higher reliance on AI tools. Conclusion AI has the potential to greatly enhance personalized learning for medical students. However, issues surrounding accuracy, misinformation, and equitable access need to be addressed to optimize AI integration in medical education. Further research is recommended to explore the longitudinal effects of AI usage on learning outcomes.