EvalCommunity Professional Resources
Comprehensive frameworks, standards, and practical instruments for Monitoring & Evaluation and International Development professionals, including AI survey practice, evaluation methods, data analytics, AI governance, and responsible automation.
Category 01
A structured reference for M&E practitioners covering 28 evaluation approaches across 9 conceptual families. Navigate by purpose, compare approaches side by side, and select the right framework for your evaluation context.
Assess whether a programme caused measurable changes in outcomes beyond what would have happened anyway. Covers RCTs, quasi-experimental designs, contribution analysis, and counterfactual methods.
A structured library of evaluation methods, instruments, and software platforms for M&E professionals, from surveys and interviews to participatory and mixed-methods approaches.
Design, structure, and generate professional evaluation reports aligned with international M&E standards so findings are clear, credible, and useful for decisions.
Build, validate, and refine a Theory of Change with a quality rubric, workshop facilitation guidance, AI prompts, and curated global resources.
Eight interactive tools for drafting, assessing, and improving Evaluation Terms of Reference aligned with major international evaluation standards.
Category 02
Design, strengthen, and operationalise M&E systems that support learning, accountability, adaptive management, and evidence-informed decisions.
Design high-quality logframes, build clear results chains, write SMART indicators, and align project logic with donor and development standards.
Use maturity self-assessment, templates, implementation roadmaps, and curated resources to apply the USAID CLA framework in practice.
Assess, audit, and strengthen M&E data quality using DQA and RDQA methods, scorecards, verification templates, and improvement action plans.
Category 03
A structured system for evaluators to design programmes, analyse data, generate insights, and improve decisions while working with AI across the evaluation cycle.
Hand off indicator tracking, data quality checks, donor report compilation, file organisation, and results dashboards using structured AI workflows.
A practical introduction to how generative AI works and where it can support evaluation, research, and development work without hype or jargon.
A practitioner guide to system prompts and retrieval-augmented generation for MEL professionals moving from prompting AI to configuring useful tools.
Apply NLP and AI to transform evaluation workflows, including text classification, sentiment analysis, entity extraction, and thematic coding.
Build a no-code AI system that automatically themes open-text survey responses directly in Google Sheets using a generated custom script.
A step-by-step guide to preparing, coding, reviewing, and validating open-text survey responses with AI-assisted qualitative analysis.
Define routines that run automatically on a schedule, trigger, or event so M&E workflows can produce outputs while you focus on decisions.
Use Claude Code routines to draft situation reports, compile donor deliverables, review logframes, flag budget variance, and coordinate partner updates.
Use Claude inside Excel to analyse MEAL data, build indicator dashboards, debug formulas, and run results framework analyses without leaving the workbook.
Use Claude inside Excel to manage project budgets, analyse field data, track partner performance, run funding pipeline analysis, and produce donor-ready outputs.
Use Claude inside Word to draft, review, revise, and refine documents across the evaluation cycle, from Terms of Reference to final reports.
Use Claude inside Word to draft, review, revise, and refine project and programme documents, from proposals to final reports.
Category 04
Six operational components to apply, audit, and communicate the JEDAI framework in survey and evaluation practice.
Seven components to operationalise AI-assisted survey scoping while keeping measurement decisions firmly in your hands.
Fluent wording is not valid measurement. Apply the SCAI framework to every AI-generated survey question before it reaches respondents.
Use FLOW and VALUE to manage the trade-off between analytical precision and respondent experience when AI generates complex survey logic.
AI flags symptoms; you diagnose causes. Use the DIAGNOSE framework to move from pattern to explanation and avoid redesigning the wrong thing.
Higher response rates are not automatically better data. Apply the ETHICS framework before AI-optimised deployment tactics reach respondents.
AI suggests patterns; you validate findings. Use this toolkit to check AI-assisted survey analysis before it appears in a report.
Category 05
A 12-step learning path that adapts Power BI to M&E practice: indicators, targets versus actuals, outcomes over time, dashboards, and impact storytelling.
Category 06
A workspace for assessing AI risks, governance, fairness, and trustworthiness in M&E and development programmes, with a directory of global AI governance institutions.
Map M&E skills into AI governance roles, identify capability gaps, and position yourself in an emerging field shaped by evaluation expertise.
Identify where AI misuse creates weak evidence, misleading conclusions, and indefensible findings across design, data collection, analysis, reporting, and use.
Use practical frameworks, disclosure standards, and case-based scenarios to distinguish responsible AI support from unethical AI substitution in research.
Category 07
Practical tools for feminist evaluation, gender-transformative M&E, and participatory impact assessment across humanitarian, development, and policy contexts.
Measure exclusion, barriers, and rights-based outcomes with precision, moving beyond counting inclusion toward understanding structural exclusion.
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