As artificial intelligence (AI) continues to transform research and practice across disciplines, public health education is at a critical juncture. Many educators at the Master’s and PhD levels remain hesitant or unfamiliar with integrating large language models (LLMs) like ChatGPT, Co-pilot, Claude, Grok, Gemini and may others into their teaching. Yet, these tools offer practical, low-barrier opportunities to enhance student learning, streamline faculty workflows, and build essential skills for the next generation of public health professionals.
AI as a Time-Saving Assistant
One of the most immediate benefits of LLMs for educators is the time they can save. Faculty often spend hours crafting sample code, reviewing student drafts, or creating feedback on research proposals. With a tool like ChatGPT, educators can quickly generate R or Python code snippets for common public health tasks, such as cleaning epidemiological data or plotting health outcomes by demographic group. These can be customized and reviewed for accuracy, reducing prep time without compromising rigor.
Consider an example from a biostatistics course. Instead of writing every script from scratch, an instructor can ask ChatGPT to draft code that performs logistic regression on a given dataset. This allows the educator to focus on refining the teaching material and contextualizing the analysis, rather than spending valuable time on routine programming.
Tutoring Complex Topics
LLMs can also serve as real-time teaching aides, particularly for students grappling with complex material. In courses covering causal inference or predictive modeling, students often struggle with the theoretical underpinnings. Educators can use LLMs to demonstrate how to simplify dense concepts.
For instance, an instructor might prompt ChatGPT to explain inverse probability weighting or the differences between supervised and unsupervised learning. These explanations can serve as a starting point for class discussions, assignments, or office hours, giving educators a scaffold to build deeper understanding.
Enhancing Research Training
At the graduate level, developing strong research skills is a central goal. LLMs can support this by helping students draft literature reviews, formulate research questions, and outline analysis plans. Rather than replacing critical thinking, AI tools can model it.
Imagine guiding a PhD student through the early stages of a dissertation. You might use an LLM to generate an initial summary of the literature on environmental exposures and birth outcomes. Then, the student and instructor can collaboratively refine the review, identify gaps, and develop a research hypothesis. This process accelerates the groundwork and teaches students how to evaluate, not just consume, AI-generated content.
Coding Support for Statistical Packages
Many public health students arrive with limited programming experience, making statistical software a major hurdle. Educators can use LLMs to bridge this gap by generating annotated examples of code in R, Stata, or Python. These examples can be shared in class or incorporated into labs and assignments.
For example, when introducing survival analysis, an instructor might ask ChatGPT to produce a commented R script that performs a Cox proportional hazards model using a standard dataset. Students can run the code, interpret the output, and then modify it for their own projects. This not only supports learning but also reduces the burden on faculty to troubleshoot each step individually.
Moving from Resistance to Integration
Educators need not overhaul their syllabi to incorporate AI tools. Start with one assignment where students use an LLM to review a topic, generate code, or critique AI-generated outputs. Discuss the ethical considerations, limitations, and best practices for using these tools in academic settings.
The integration of AI in public health education is not about embracing technology for its own sake. It’s about equipping students with the tools they will encounter in research and practice. For educators, it’s an opportunity to enrich teaching, save time, and stay at the forefront of a rapidly evolving field.
Call to Action
If you’re a public health educator new to AI, consider experimenting with LLMs in a single class session or assignment. Use them to support, not replace, your teaching goals. The first step is small but significant: begin exploring how these tools can enhance your work and your students’ learning experiences.
This document was human authored with Open AI Chat GPT used to improve clarity and grammar.