AI for Scientists (Lab to Paper)
Accelerate every stage of research — literature review, experimental design, data analysis, writing, and publishing — with AI tools built for scientists.
What You'll Learn
- Use AI tools to conduct systematic literature reviews and map citation networks
- Design experiments with AI-assisted power analysis and protocol optimization
- Analyze research data using AI-guided statistical test selection and visualization
- Write publication-ready manuscripts with AI editing while maintaining your scientific voice
- Create journal-quality figures, captions, and supplementary materials with AI assistance
- Evaluate AI ethics in research — disclosure requirements, authorship rules, and citation verification
Course Syllabus
The Research Bottleneck
You became a scientist to make discoveries — not to spend 60% of your time on literature searches, formatting tables, and wrestling with statistical software. A typical researcher spends 4-6 hours per week just keeping up with new publications in their field. Writing a single paper takes 3-6 months from first draft to submission.
AI doesn’t replace your scientific thinking. It accelerates the mechanical parts — finding papers, running statistics, polishing prose — so you can spend more time on what actually matters: designing experiments, interpreting results, and advancing knowledge.
What You’ll Learn
This course covers the complete research pipeline from literature review to publication:
- Literature Review: Use AI to search 200M+ papers, map citation networks, and synthesize evidence across studies
- Experimental Design: Optimize protocols, run power analyses, and plan experiments with AI assistance
- Data Analysis: Select the right statistical tests, generate visualizations, and identify patterns in complex datasets
- Scientific Writing: Draft, edit, and polish manuscripts while maintaining your voice and meeting journal standards
- Figures & Materials: Create publication-quality figures, write captions, and organize supplementary materials
- Publishing Ethics: Navigate AI disclosure requirements, authorship rules, and citation verification
Every workflow follows one principle: AI generates, the scientist verifies. Your expertise drives every decision — AI handles the volume.
Who This Course Is For
- Graduate students learning to manage the research-to-publication pipeline
- Postdocs and early-career researchers looking to increase publication output
- Principal investigators wanting to integrate AI into their lab workflows
- Research staff managing literature databases, data analysis, or manuscript preparation
Prerequisites: Active research experience (you should know what a p-value is and have submitted at least one paper or thesis). No AI experience required.
Frequently Asked Questions
Do I need programming skills for this course?
No. While some AI tools have coding interfaces, this course focuses on tools with user-friendly interfaces. We cover point-and-click tools like Julius AI and JASP alongside prompt-based approaches.
Which scientific disciplines does this course cover?
The workflows apply across disciplines — biology, chemistry, physics, social sciences, engineering, and more. Examples span multiple fields so you can adapt them to your specific area.
Will AI write my papers for me?
No — and it shouldn't. AI assists with drafting, editing, and structuring, but every claim, citation, and conclusion must reflect your scientific judgment. This course teaches you to use AI as a research accelerator, not a replacement.
What about journal policies on AI use?
We cover current policies from Nature, Science, ICMJE, and major publishers. You'll learn exactly what to disclose and how to use AI while staying fully compliant with publication ethics.