Build a MEL Plan with AI

You are a senior M&E specialist. I need to build a MEL plan, starting with the results framework. Based on the program information I provide, create a results framework table with columns: - Level (Goal, Outcome, Output) - Result statement - Indicator (1-2 per result) - Baseline value (if known, otherwise "TBD - collect at baseline") - Target value - Data source - Frequency Include 1 goal, 2-4 outcomes, and 3-6 outputs. Each output should logically contribute to at least one outcome. The results framework should be: - Realistic (targets achievable within the program timeframe) - Measurable (every result has at least one quantifiable indicator) - Aligned with the program's theory of change Here is my program information: [Describe your program, its goals, theory of change, target population, and timeline]

For each indicator in the results framework, create a detailed indicator reference sheet. Present as a table with columns: - Indicator name - Precise definition (unambiguous, a new team member could understand it) - Unit of measurement - Calculation method (formula if applicable) - Data source - Collection method (survey, administrative records, observation, etc.) - Collection frequency - Responsible person/team (leave as TBD for me to fill) - Disaggregation dimensions - Data quality considerations (known risks or limitations for this indicator) For each indicator, also note whether it is a standard donor indicator or a custom indicator, and whether a validated measurement tool exists for it.

Create a data collection plan for this MEL system. Produce: 1. **Data collection schedule**: A 12-month calendar (table format) showing which data collection activities happen in which months. Include baseline, routine monitoring, midline (if applicable), and endline. 2. **Methods summary**: For each data collection method used (survey, KII, FGD, observation, administrative data), specify: - Which indicators it covers - Sample size or scope - Tools needed (questionnaire, interview guide, checklist) - Estimated cost and time - Who conducts it (staff, consultants, enumerators) 3. **Data flow**: How does data move from collection to entry to cleaning to analysis to use? Describe each step and who is responsible. 4. **Data quality assurance**: What checks are in place to ensure data quality? Include spot checks, double entry, supervisor review, and any automated validation. 5. **Data management**: Where is data stored? Who has access? What is the backup protocol? What data protection measures are in place?

Create a reporting schedule for this MEL plan. Produce: 1. **Reporting calendar**: A table showing each report type, frequency, audience, deadline, and which indicators/data feed into it. Include: - Donor progress reports (per donor requirements) - Internal monitoring reports - Annual review or learning reports - Evaluation reports (midterm, final) - Ad hoc reports (board, government) 2. **Report templates**: For each recurring report type, outline the standard sections and what data each section requires. Keep templates practical (what a program officer can actually fill in). 3. **Data-to-report timeline**: For each report, work backward from the deadline. When must data collection be complete? When must data be cleaned? When must analysis be done? Present as a countdown timeline. 4. **Roles**: Who drafts, who reviews, who approves each report type.

Design a learning agenda for this program. Produce: 1. **Learning questions** (3-5): Questions the program wants to answer through implementation, beyond what the indicators measure. These should be questions about HOW and WHY, not just WHAT. Example: "What factors enable or prevent community health workers from sustaining behavior change practices after training ends?" 2. **Learning methods**: For each question, how will the program answer it? Options include after-action reviews, most significant change stories, case studies, process monitoring, outcome harvesting, or dedicated learning studies. 3. **Learning rhythm**: When does learning happen? Define regular touchpoints (monthly team reflections, quarterly learning reviews, annual pause-and-reflect workshops). 4. **Adaptation protocol**: How will learning lead to program changes? Define the decision-making process: who can authorize changes, what evidence is needed, and how changes are documented. 5. **Knowledge products**: What will the program produce to share its learning? (briefs, case studies, blog posts, conference presentations)