Generative AI is transforming how scientists and engineers work. From writing code to drafting papers, it's now embedded in daily research tasks — but what happens when everyone uses the same model?
This short course explores a critical risk: the potential for intellectual homogenization. When every team turns to the same model for ideas or solutions, we risk converging on similar answers — diminishing the diversity, originality, and serendipity that science depends on.
What You'll Learn
- How AI-generated code and hypotheses may reinforce dominant assumptions
- What “convergence risk” means in computational science
- Best practices for using AI as an augmentation tool — not a replacement
- Ways to maintain creativity and epistemic diversity in research workflows
Who Should Attend?
This session is designed for CERN scientists, engineers, software developers, and anyone using AI tools in technical workflows.
Apply Critical Thinking to Generative AI
Don't just use the tools — understand them. In a time when productivity is accelerating, this course invites you to pause, reflect, and ask: Are we all thinking alike because the model told us to?