GenAI and Research
GenAI and Research
When GenAI can write papers, what should you do?
Quentin Gallea — The Causal Mindset
About
This website shares lecture content from a course currently taught at the University of Neuchatel on how generative AI tools — large language models, coding agents, retrieval-augmented generation — are transforming research workflows in economics and the social sciences. The material follows the complete research cycle, from reading papers and reviewing literature to brainstorming hypotheses, analyzing data, writing, and presenting — exploring both traditional methods and GenAI tools at every stage.
The goal is not just to use these tools, but to use them responsibly and rigorously, understanding their strengths, limitations, and the risks they introduce to scientific work.
What You’ll Find Here
- Critical comparisons of traditional research methods with AI-assisted approaches
- Practical guidance on using generative AI tools responsibly at each stage of the research process
- An honest evaluation of the strengths, biases, and risks of AI tools applied to research
Sessions
| # | Topic | Materials |
|---|---|---|
| 01 | The AI Revolution | Available |
| 02 | Deep Dive into LLMs | Available |
| 03 | Everyday Life with GenAI | Available |
| 04 | Reading Scientific Research | Available |
| 05 | Literature Review | Available |
| 06 | Skills: Building Reusable AI Tools | Available |
| 07 | Creativity & Brainstorming | Available |
| 08 | Coding & Data Analysis | Available |
| 09 | Deep Research mode | Available |
| 10 | Scientific Writing | Available |
| 11 | Paper Review / Referee | New — now available |
| 12 | Claude Code & Agentic AI | New — now available |
| 13 | What’s Next: Careers in the GenAI Era | Coming soon |
What’s Next: Careers in the GenAI Era
If GenAI can write papers, what does it mean to be a researcher — and what does a career in research look like from here?
The course closes with a session devoted to that question. The motivation is simple: every previous session showed how a piece of the research workflow can be done faster, or differently, or partly by an agent. Put those pieces together and the role of the human researcher has clearly shifted. The closing session asks where the human edge actually lies — taste, judgment, framing, causal reasoning, knowing what is worth doing — and how to deliberately build those skills while the tooling keeps moving.
We will also hear from an external guest speaker, a recent master’s graduate who wrote his thesis on rethinking careers in the age of GenAI. He will share a concrete playbook for staying relevant in the labour market as the technology evolves: which skills compound, which ones are about to be commoditised, and how to position your work so that GenAI is a multiplier rather than a substitute.
This is not a forecast — nobody knows what the job market for researchers looks like in five years. But the goal of the entire course has been to give you the lens, the vocabulary, and the practical instincts to keep adapting. The closing session is where we tie that back to your own next steps.