Getting Started with GenAI
online course for doctoral students and early career researchers
This course is designed for doctoral students and early-career researchers from any discipline. No coding or AI expertise is required—just your curiosity. Drawing directly from research papers, the course is best suited to learners who want to see past the headlines. It will help you get the most out of GenAI in your research while learning to recognize exaggerated claims.
Evidence-based
Based on research papers
Open to all disciplines
No coding or AI background required
Thoughtful
Clear explanations with conceptual depth
Critical
Helps you see beyond hype
What you will learn
Introduction to GenAI technologies
You will gain a conceptual understanding of the technologies underlying modern GenAI, explained in clear, non-technical language
Strengths and weaknesses of GenAI
You will learn to tell the difference between cases where GenAI can be extremely helpful in research and those where caution is needed
Use cases in research
You will discover how GenAI can support different stages of research from idea generation and data analysis to communicating results and navigating your PhD journey.
Ethical considerations
You will explore how to use GenAI tools responsibly, including questions of authorship and accountability, confidentiality and copyright, and bias and discrimination.
Trusted by 250+ PhD students and early-career researchers
The first three editions of the course were offered as non-compulsory, not-for-credit modules at University of Technology Sydney and attracted more than 250 participants.
Yuveena Gopalan
PhD Candidate (Learning Analytics)
"I found the course incredibly insightful, it not only presented examples grounded in relevant literature but also explored the inner workings of Generative AI in a way that made complex ideas easy to follow. What really stood out to me were the practical demonstrations. Seeing examples on utilising synthetic samples and digital clones gave me an understanding into potential applications and ways in which it could be applied in my own work."
Bernardo Mendonca Severiano
PhD Candidate (Institute for Sustainable Futures)
"Ivan brings a great blend of technical depth and accessible communication to the topic of Generative AI. His sessions sparked engaging discussions across disciplines and helped us reflect critically on both the opportunities and limitations of these tools in academic contexts. Ivan's insights were both intellectually rigorous and action-oriented. A valuable contribution to our institution's thinking on emerging technologies and sustainable futures."
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