Harnessing Large Language Models in Informatics Education: Opportunities and Challenges

Presentation Time: 08:00 AM - 09:15 AM

Abstract Keywords: Education and Training, Workforce Development, Large Language Models (LLMs)
Primary Track: Applications
Programmatic Theme: Academic Informatics / LIEAF

The integration of large language models (LLMs) into informatics education presents both opportunities and challenges. These AI-powered tools can be used to enhance learning by assisting with data analysis, coding, and knowledge retrieval, yet they also raise concerns about academic integrity, over-reliance, and skill development. While LLMs can act as interactive tutors and aid in real-world informatics applications, improper use may lead to learners bypassing critical thinking and analytical understanding. This panel will explore strategies for leveraging LLMs effectively in informatics curricula, ensuring they support—rather than replace—participant learning. Key discussions will include best practices for ethical LLM use, methods for assessing learning in an LLM-driven environment, and policy considerations to safeguard educational outcomes. By striking a balance between innovation and responsible use, educators can harness LLMs to enhance informatics education while maintaining academic rigor and integrity.

Authors:
Mark Weiner, MD - Weill Cornell Medicine / Population Health Sciences; William Hersh, MD, FACMI, FAMIA - Oregon Health & Science University; Jessica Ancker, MPH, PhD, FACMI - Vanderbilt University Medical Center; Neil Sarkar, PhD, MLIS - Rhode Island Quality Institute & Brown University;

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