This course documents a minimal, rigor-first framework for using AI accurately in scientific and scholarly writing.
Rather than focusing on tools or prompts, it formalizes a small set of principles for framing research questions, verifying sources, ensuring accurate interpretations, and applying human judgment before claims are finalized.
The framework is designed for research-adjacent critical thinking, and is demonstrated using current high-capability language models (ChatGPT-4-class and above) as a practical constraint for accuracy.
A very simple but effective methodology is the base of everything.
When different studies seem to support different conclusions, and you’re unsure which claim is actually correct, or whether the evidence is truly contradictory at all, this is the framework you should be using.
Ri Xu, PhD provides evidence-based scientific writing and AI-assisted workflow support for science-led organizations and technical teams. Her work strengthens scientific rigor, reasoning accuracy, and clarity in fast-moving environments where small errors or poorly used AI tools can undermine good science.
Based in Montreal and working internationally, Ri partners with scientists, lab leaders, and technical professionals to translate complex work into clear, defensible communication and efficient, rigor-first workflows. Backed by an engineering PhD and over four years of experience, she helps teams use AI critically without compromising scientific standards.