Systematic Review Simulator
Practice every stage of an AI-assisted systematic review — from PICO formulation to evidence gap mapping — with guided exercises and instant AI feedback.
Formulating the Review Question with PICO
A well-structured question is the foundation of any credible systematic review. Use the PICO framework to break a broad topic into searchable, evaluable components — then let AI suggest synonyms and alternative phrasings.
📚 Theory: The PICO Framework
PICO (Population, Intervention, Comparison, Outcome) transforms vague research questions into precise, systematic search terms. For qualitative reviews, PICo or SPIDER frameworks are often more appropriate. The choice of framework shapes your entire review design.
- Population: Who is the study about? (demographics, setting, characteristics)
- Intervention: What is being studied or evaluated?
- Comparison: What is the alternative or control?
- Outcome: What outcomes are measured? (primary and secondary)
Start with the question your donor or stakeholders asked. AI will unpack it into a structured PICO framework with alternative search terms for each component.
Include the topic, population, and setting if known. The more specific, the better the PICO structure.
Building a Comprehensive Search Strategy
A systematic review is only as complete as its search. AI helps generate Boolean search strings, identify synonyms, and recommend databases — reducing the risk of missing relevant evidence.
📚 Theory: Boolean Logic & Database Searching
Systematic search strings combine controlled vocabulary (MeSH, Emtree) with free-text keywords using Boolean operators. AND narrows results; OR broadens them; NOT excludes. Each database has different syntax and thesaurus terms.
Select the databases relevant to your review. AI will generate ready-to-use Boolean search strings adapted to each database's syntax and controlled vocabulary.
Screening Titles and Abstracts at Scale
Screening thousands of records is the most time-consuming part of a systematic review. Practice AI-assisted triage: compare your Include/Exclude/Maybe decisions against AI classifications and refine your inclusion criteria.
📚 Theory: AI as a Screening Partner
AI can reduce screening workload by 70–80% by performing an initial triage. However, AI should never make final decisions without human review — it is a prioritisation and consistency tool, not a replacement for expert judgment. Always double-check a random sample of AI decisions.
Review your criteria from Stage 1. Edit if needed before generating practice abstracts.
Data Extraction & Quality Assessment
Extract structured data from included studies and assess risk of bias. AI can pre-populate extraction forms and apply quality checklists — but all outputs must be verified against the original text.
📚 Theory: Systematic Extraction & Risk of Bias
Data extraction captures study characteristics in a standardised format to enable comparison across studies. Risk of bias assessment evaluates whether study design or conduct may have led to systematically incorrect results. The Cochrane RoB 2 tool is the gold standard for randomised trials; ROBINS-I for non-randomised studies; CASP for qualitative evidence.
Paste the abstract or key sections of a study. AI will extract structured data into a standard form. Always verify each field against the original text.
| Field | Extracted Value | Verify ✓ |
|---|
Rate each domain of the Cochrane Risk of Bias 2 tool. Click "AI Assess" to get AI-generated verdicts with justifications based on the text you pasted above.
Synthesis, Evidence Gap Mapping & Conclusions
Pull findings together, identify patterns and inconsistencies, and map where evidence is strong, thin, or absent entirely. The final stage of your systematic review.
📚 Theory: From Data to Evidence-Based Conclusions
Evidence synthesis involves comparing findings across studies, assessing consistency, and drawing conclusions about what works, for whom, and under what conditions. An evidence gap map visualises where research is abundant and where investment is needed — an essential tool for research agenda-setting and donor prioritisation.
Paste key findings from your included studies. AI will identify overarching themes and generate a structured narrative synthesis — the foundation of your review conclusions.
Click any cell to cycle through evidence levels: Strong → Some → Weak → None. AI will generate an initial gap map based on your review topic, which you can then edit.
| Intervention Type ↓ · Population → | Urban Youth | Rural Youth | Young Women | Displaced Youth |
|---|
Generate a draft conclusion and transparency statement for your systematic review report.
Print or save your review materials as a PDF to share with your team or include in your review protocol.