AI tools can support evaluators and nonprofit teams throughout every phase of an evaluation, from early design and planning to implementation and data use. Below are practical examples of how AI can strengthen each stage of the evaluation process.
- DesignÂ
- Assist in learning about a topic
- Summarize RFPs or background materials
- Outline proposals or evaluation plans
- Score proposals for RFP alignment
- Draft needs assessments
- Support development of a logic model
- Draft evaluation questions
- Perform literature reviews (formal or informal): find existing published research, identify validated surveys or instruments
- Generate logic model visuals or diagrams
- Draft survey or focus group questions aligned with outcomes
- Scan and summarize community demographic or epidemiological data
- Identify relevant frameworks or evidence-based models (e.g., trauma-informed, CBPR, harm reduction)
- Draft plain-language summaries or one-pagers for stakeholders
- Develop evaluation capacity-building materials (e.g., training outlines, slide decks, tip sheets)
- Prepare agendas, activities, PowerPoints, and team bios
- Draft project work plans with timelines, staff responsibilities, and resources
- Implement
- Develop toolkits and user guides for data collection
- Create scripts for focus groups or interviews
- Plan outreach, identifying who, when, and how
- Suggest quality checks and review early data
- Assess progress, prepare agendas, and update work plans
- Score assessments or track deliverables
- Translate data collection tools for multilingual participants
- Draft and test survey logic (e.g., skip patterns, response flows)
- Simulate interview responses for pilot testing
- Summarize meeting notes or transcripts automatically
- Generate reminders, checklists, or task trackers for data collection milestones
- Use
- Clean and review data sets
- Recode or restructure data
- Provide assistance in Excel, R, or other analysis languages
- Develop tables, charts, graphs, and visualizations
- Identify themes in qualitative data (interviews, focus groups, open-ended responses)
- Suggest key insights for different audiences
- Summarize key findings for different audiences (funders, youth, coalition members)
- Draft or edit report sections, abstracts, or success stories
- Visualize theories of change, outcomes, or timelines
- Convert findings into social media summaries, infographics, or presentations
- Prepare executive summaries, table labels, or captions
- Check for readability, plain language, or cultural relevance
- Generate recommendations based on trends or evidence
- Rewrite for clarity or tailor materials for specific audiences, ages, or contexts
- Reflect & Improve
- Identify lessons learned or implementation barriers from reports or transcripts
- Synthesize feedback across sites or partners
- Draft post-project learning briefs
- Suggest process improvements or follow-up evaluation questions