Artificial Intelligence and the Evolution of Learning

This chapter traces the evolution of Artificial Intelligence (AI), with emphasis on its applications in health sciences education. Originating from Alan Turing’s seminal inquiry – “Can machines think?” – AI has progressed through symbolic reasoning, neural networks, statistical models, and most recently, large language models (LLMs). Machine learning (ML) has transformed health education by enabling personalized instruction, predictive analytics, virtual simulations, and intelligent tutoring systems that support clinical reasoning and problem-solving skills. LLMs such as GPT, BERT, and Med-PaLM now automate content generation, exam design, feedback, and summarization – enhancing both teaching efficiency and learner support, especially in resource-limited settings. Personalized, self-paced learning – vital in clinical education – is further enabled by these digital tools, though it relies on robust infrastructure, educator readiness, and data protection. The chapter also examines ethical and legal concerns surrounding AI use, including privacy, bias, transparency, and accountability. It aligns with the World Health Organization’s six ethical principles: autonomy, human well-being, transparency, accountability, inclusion, and sustainability. Ultimately, AI is presented not merely as a tool but as a transformative force in pedagogy and professional development. Its effective deployment requires human-centered, evidence-based, and ethically grounded strategies. Health education must integrate digital ethics, data security, and inclusive design to prepare future professionals for both technical proficiency and responsible AI engagement.