AI+ for Laboratory Research & Experimental Design
Design Rigorous Experiments and Get to Results Faster with AI
Design Better Experiments. Get to Results That Last.
Most experimental science is underpowered, underdocumented, and under-designed — not because researchers are careless, but because experimental design is rarely taught. AI+ for Laboratory Research & Experimental Design gives bench researchers the DESIGN Protocol: a six-stage framework — Define, Establish, Size, Implement, Generate, Narrate — that integrates AI across every stage of the experimental lifecycle, from hypothesis to manuscript. Built by an AI engineering firm for researchers who run the experiments that science depends on — and who deserve better than inherited conventions.
- The full DESIGN Protocol with a pre-experiment quality checklist and AI design audit prompt
- Power analysis in plain terms — how to calculate the sample size your study actually needs, before you collect a single observation
- 25+ copy-ready prompt templates for protocol drafting, SOP generation, data QC, statistical test selection, methods section writing, and discussion structuring
- A practical guide to FAIR data principles and Documentation Debt — turning lab knowledge from memory into reproducible records
- Five AI-specific integrity risks every experimental researcher must know, and the safeguards for each
- A 90-day integration plan with a DESIGN self-assessment and week-by-week actions
PhD students and postdoctoral researchers in bench-based disciplines — life sciences, chemistry, physics, biochemistry, materials science, and engineering — who are responsible for designing, running, and reporting experiments. Also junior principal investigators setting up lab programmes for the first time.
Also for:R&D scientists in pharmaceutical, biotech, agritech, and materials companies who design and run experiments under commercial pressures; research technicians who manage experimental protocols and data quality in a lab environment.
- Apply the DESIGN Protocol to structure any experiment from initial curiosity to documented hypothesis with defined success criteria
- Calculate appropriate sample size and statistical power before data collection, selecting the correct test design for the research question
- Develop reproducible experimental protocols and AI-assisted lab documentation that meet FAIR data principles
- Use AI tools for data quality control, pattern detection, and statistical analysis while maintaining scientific rigour and researcher oversight
- Write clear, publication-ready results and methods sections using AI as a drafting assistant, and articulate where AI was used in the research process
- Diagnostic
- How AI-ready is your experimental practice?
- Chapter 1
- AI in the Laboratory Right Now
- Chapter 2
- From Curiosity to Testable Hypothesis
- Chapter 3
- Experimental Design and the DESIGN Protocol
- Chapter 4
- Statistical Power, Sample Size, and Study Architecture
- Chapter 5
- Protocol Development and Lab Documentation
- Chapter 6
- Data Collection, Lab Notebooks, and Quality Control
- Chapter 7
- AI-Assisted Analysis and Interpretation
- Chapter 8
- Writing Up: From Results to Manuscript
- Chapter 9
- Reproducibility, Integrity, and Open Science
- Chapter 10
- Your 90-Day AI Research Lab Plan
- Back matter
- Skill Summary · Recommended Next Reads · Glossary · Tool Reference
Built by an AI engineering firm for researchers who run the experiments that science depends on — and who deserve better than inherited conventions.
Often packaged with this title.
T3-01 · Job SkillsAI+ for Academic Research
T3-83 · Job SkillsAI+ for Systematic Literature Reviews
T3-88 · Job SkillsAI+ for Clinical Research & Evidence-Based Practice
T3-29 · Job SkillsAI+ for Data Analysis
T3-82 · Job SkillsAI+ for Qualitative Research Methods
T3-84 · Job SkillsAI+ for Quantitative Research & Statistics
T3-86 · Job SkillsAI+ for Fieldwork & Ethnographic Research
T3-87 · Job SkillsAI+ for Textual & Discourse Analysis