Welcome: Your AI Research Lab

Here’s a number that should bother you: researchers spend an average of 4-6 hours per week just reading and organizing papers — and that’s before they analyze a single data point or write a single sentence. A typical manuscript takes 3-6 months from first draft to submission. The publication pipeline is where good science goes to wait.

AI changes the timeline, not the science. The tools available today can cut literature review time by 30%, suggest statistical tests in seconds instead of hours, and polish your prose to journal standards — all while you remain the scientist making every critical decision.

What You’ll Learn

By the end of this course, you’ll be able to:

• Search and synthesize literature across 200M+ papers using AI-powered discovery tools
• Design experiments with AI-assisted power analysis, protocol optimization, and variable selection
• Analyze data with AI-guided statistical test selection and publication-quality visualizations
• Write manuscripts faster using AI editing tools while maintaining your scientific voice
• Create figures and supplementary materials that meet journal publication standards
• Navigate ethics — disclosure requirements, authorship rules, and citation verification protocols

How This Course Works

Each lesson covers one stage of the research pipeline, from literature review through publication. You’ll learn specific AI tools and workflows with hands-on exercises you can apply to your current research.

Course structure:

• Lessons 1-3: Input phase — finding literature, designing experiments
• Lessons 4-5: Analysis and writing — processing data, drafting manuscripts
• Lessons 6-7: Output phase — figures, supplementary materials, peer review, publishing
• Lesson 8: Capstone — building your complete research AI toolkit

One principle governs everything: AI generates, the scientist verifies. Every AI output in this course gets checked against your expertise before it enters your research.

The Research AI Stack

✅ Quick Check: Look at the “What You Do” column. Notice a pattern? The scientist’s role is always about judgment — evaluating, verifying, interpreting, deciding. AI handles volume and speed; you handle meaning and quality. What stage of this pipeline currently takes you the most time?

The Citation Verification Rule

Before we go further, one rule applies to every lesson in this course:

Every AI-generated citation must be independently verified.

General AI tools (ChatGPT, Claude, Gemini) hallucinate references. They generate plausible-sounding author names, realistic journal titles, and fake DOIs. Specialized tools (Semantic Scholar, Elicit, Scite) pull from real databases — but even these can occasionally misattribute or mislink papers.

Your verification protocol:

1. Check the paper exists in the actual database (PubMed, Google Scholar, Web of Science)
2. Confirm the authors, title, journal, and year match
3. Verify the DOI resolves to the correct paper
4. Read at least the abstract to confirm the paper says what AI claims it says

This takes 30-60 seconds per citation. It’s non-negotiable.

Key Takeaways

• AI accelerates the mechanical parts of research (searching, analyzing, editing) while you retain all scientific judgment
• The research AI stack covers six stages: literature review, experimental design, data analysis, writing, figures, and publishing
• Citation verification is mandatory — general AI models fabricate references, and even specialized tools need human confirmation
• Major journals require AI disclosure in your methods section — this course teaches you exactly what and how to disclose
• The principle “AI generates, scientist verifies” governs every workflow in this course

Up Next

In the next lesson, you’ll master AI-powered literature review — searching 200M+ papers, mapping citation networks, and synthesizing evidence across studies in a fraction of the time it takes manually.