Constructing knowledge: the role of AI in medical learning

Area of Science:

• Medical Education Technology
• Artificial Intelligence in Healthcare Education
• Digital Learning in Medicine

Background:

• Large language models (LLMs) present novel opportunities and challenges in medical education.
• Alignment with constructivist learning theories suggests potential for enhanced critical thinking and problem-solving skills.
• The rapid advancement of AI necessitates an examination of its role in medical training.

Purpose of the Study:

• To explore the potential benefits and challenges of integrating LLMs into medical education.
• To assess the impact of LLMs on educational outcomes and learning effectiveness.
• To address the need for revised curricula and assessment methods in response to AI integration.

Main Methods:

• Review of current literature on LLMs and constructivist learning theories in medical education.
• Analysis of potential applications of LLMs for inquiry-based learning.
• Identification of ethical considerations and potential biases associated with AI in education.

Main Results:

• LLMs show promise for enhancing critical thinking and problem-solving through inquiry-based learning.
• Empirical studies are needed to validate the effectiveness of LLMs on educational outcomes.
• Significant ethical concerns, including AI bias and equitable access, must be addressed.

Conclusions:

• The integration of LLMs in medical education requires careful consideration of pedagogical alignment and empirical validation.
• Curriculum redesign and new assessment strategies are essential to effectively leverage AI tools.
• Addressing AI biases and ensuring equitable access are critical for responsible deployment in training medical professionals.