Course Description
An introduction to artificial intelligence (AI) literacy for upper-division undergraduate students and graduate students at all levels preparing for careers in public health and related health professions. The course develops conceptual understanding of machine learning, deep learning, and large language models; examines current AI applications and evidence in health.
Additional Requirements for Graduate Students:
Graduate students will be required to complete additional homework and submit a final project of greater scope and complexity than the undergraduate project.
Athena Title
AI Literacy for Health
Prerequisite
BIOS 2010 or BIOS 2010E or STAT 2000 or STAT 2000E
Grading System
A - F (Traditional)
Student learning Outcomes
- Students will describe the evolution of AI from early symbolic AI to modern AI tools.
- Students will describe AI applications in public health, medicine, nursing, biomedical research, health communication, health administration, health informatics, and related fields.
- Students will use common AI tools for learning, writing, summarization, brainstorming, literature exploration, data interpretation, coding assistance, project development, and career preparation.
- Students will evaluate appropriate and inappropriate uses of AI in health contexts, including issues related to accuracy, hallucination, privacy, bias, equity, transparency, and accountability.
- Students will identify and summarize leading developers, research groups, conferences, companies, and organizations shaping AI in health and health sciences.
Topical Outline
- 1. Foundations of AI literacy and the history of AI, from early symbolic AI to today.
- 2. Concepts in machine learning, deep learning, large language models, and modern AI.
- 3. AI applications across public health, medicine, nursing, and related fields.
- 4. Practical AI tools for learning, writing, literature exploration, data interpretation, coding assistance, project development, and career preparation.
- 5. Responsible and applied AI in health: accuracy, hallucination, privacy, bias, equity, transparency, and accountability.
