Implementation Timeline
graph TB
P1["📋 PHASE 1: Research Planning<br/>15-20 min"]
S1["Step 1: Define Problem Scope"]
S2["Step 2: Set Research Objectives"]
S3["Step 3: Prepare Documentation"]
P2["🤖 PHASE 2: AI-Assisted Research<br/>30-40 min"]
S4["Step 4: Customize MCP Prompt"]
S5["Step 5: Execute AI Research"]
S6["Step 6: Quality Verification"]
P3["🌳 PHASE 3: Tree Construction<br/>25-35 min"]
S7["Step 7: Extract Core Problem"]
S8["Step 8: Map Root Causes"]
S9["Step 9: Identify Effects"]
P4["💬 PHASE 4: Stakeholder Prep<br/>15-20 min"]
S10["Step 10: Convert to Questions"]
S11["Step 11: Plan Validation Approach"]
P1 --> S1 --> S2 --> S3 --> P2
P2 --> S4 --> S5 --> S6 --> P3
P3 --> S7 --> S8 --> S9 --> P4
P4 --> S10 --> S11
style P1 fill:#F59E0B,stroke:#D97706,stroke-width:3px,color:#1F2937,font-weight:bold
style P2 fill:#F59E0B,stroke:#D97706,stroke-width:3px,color:#1F2937,font-weight:bold
style P3 fill:#F59E0B,stroke:#D97706,stroke-width:3px,color:#1F2937,font-weight:bold
style P4 fill:#F59E0B,stroke:#D97706,stroke-width:3px,color:#1F2937,font-weight:bold
style S1 fill:#D9F99D,stroke:#72B043,color:#2A2A2A
style S2 fill:#D9F99D,stroke:#72B043,color:#2A2A2A
style S3 fill:#D9F99D,stroke:#72B043,color:#2A2A2A
style S4 fill:#D9F99D,stroke:#72B043,color:#2A2A2A
style S5 fill:#D9F99D,stroke:#72B043,color:#2A2A2A
style S6 fill:#D9F99D,stroke:#72B043,color:#2A2A2A
style S7 fill:#D9F99D,stroke:#72B043,color:#2A2A2A
style S8 fill:#D9F99D,stroke:#72B043,color:#2A2A2A
style S9 fill:#D9F99D,stroke:#72B043,color:#2A2A2A
style S10 fill:#D9F99D,stroke:#72B043,color:#2A2A2A
style S11 fill:#007F4E,stroke:#00b369,stroke-width:2px,color:#fff,font-weight:bold
Total: ~90 minutes
Total Time Estimate
Phase 1: Research Planning (15-20 min)
Phase 2: AI-Assisted Research (30-40 min)
Phase 3: Problem Tree Construction (25-35 min)
Phase 4: Stakeholder Preparation (15-20 min)
Total: ~90 minutes
Phase 2: AI-Assisted Research (30-40 min)
Phase 3: Problem Tree Construction (25-35 min)
Phase 4: Stakeholder Preparation (15-20 min)
Total: ~90 minutes
Phase 1: Research Planning
Step 1: Define Your Problem Scope
- Write a clear 1-2 sentence problem statement
- Specify affected population and geographic area
- List what you already know
Problem: Young adults aged 18-25 in rural Nyanza region, Kenya, have limited access to stable employment.
Step 2: Set Research Objectives
- Causes and effects you need to understand
- Interventions the research should help you design
- Stakeholders you'll eventually need to engage
Step 3: Prepare Documentation System
- Create a folder for sources
- Start from the Problem Tree template
- Track evidence (E) versus assumptions (A)
Phase 2: AI-Assisted Research
Step 4: Customize Your MCP Prompt
- Copy the MCP template
- Replace placeholders with your context
Step 5: Execute and Review AI Research
- Run the prompt, save outputs immediately
- Scan for errors or red flags
Step 6: Quality Verification
- Check credibility of 3-5 sources
- Verify key statistics against original sources
- Flag anything that looks like assumption
Don't Skip Verification
AI can generate plausible but incorrect citations.
Phase 3: Problem Tree Construction
Step 7: Extract and Organize Core Problem
- Refine the problem statement
- Specify who, what, and where
- Remove any causes or solutions
Step 8: Map Root Causes by Levels
- Level 1: direct causes
- Level 2: underlying causes
- Level 3: structural causes
- Tag each cause (E) evidence-based or (A) assumption
• Skills-labor market mismatch (E)
- Educational curricula not aligned with market needs (E)
- Limited access to practical/vocational training (E)
- Rapid economic transition outpacing skill development (A)
- Educational curricula not aligned with market needs (E)
- Limited access to practical/vocational training (E)
- Rapid economic transition outpacing skill development (A)
Step 9: Identify Effects by Time Horizon
- Immediate (0-6 months), medium-term (6 months-2 years), long-term (2+ years)
- Impact levels: individual, family, community, and system
- Tag each effect (E) or (A)
Milestone: Your Problem Tree is Complete!
Causes, problem, and effects, all tagged and ready for validation.
Phase 4: Stakeholder Preparation
Step 10: Convert Assumptions to Questions
- Turn each (A) item into an open-ended question
- Avoid leading questions
- Aim for 8-10 questions
❌ Bad (Leading Question)
"Isn't training the real problem here?"
✅ Good (Open-Ended Question)
"What keeps young people from finding good work?"
Step 11: Plan Validation Approach
- Identify who can answer your questions
- Decide how you'll reach them: focus groups, interviews, meetings
- Update your tree based on what you learn
Next: Stakeholder Mapping (Lesson 1.2)
Your Problem Tree and questions are ready for Lesson 1.2.
Quick Reference Checklist
Common Questions
- Generic results? Add more geography and population detail
- Deep enough? Stop once you reach something actionable
- Skip Phase 2? MCP is optional but saves hours