Course Overview

AI can support quantitative evaluation work, especially when you clean messy datasets, draft analysis plans, check calculations, and turn results into clear tables and visuals. This focused deep dive gives evaluators practical ways to use AI for common quantitative tasks while keeping rigor, traceability, and human judgment at the center. Participants use AI to build efficient workflows and quality checks. As a result, they move faster without losing control of their data or decisions.

What You Will Learn

Participants learn how to use AI to support quantitative workflows in ways that stay transparent and defensible. First, they draft and refine a simple analysis plan that aligns with evaluation questions. Next, they use AI to support data preparation by flagging common issues, including missing data, outliers, and inconsistent formats or labels. They also learn how AI can suggest cleaning steps and how to verify each change with clear accuracy checks.

Participants then use AI to assist with descriptive and comparative analysis tasks. For example, they summarize distributions and interpret basic group differences. Finally, participants turn quantitative outputs into decision-ready tables and simple visuals. They also practice writing clear, cautious interpretations that avoid confusing association with causality.

Course Format

This course runs as a 3-hour live, virtual, instructor-led session (or a half day in person). It combines short demonstrations with guided practice and peer exchange. Participants work with realistic evaluation datasets and leave with reusable prompts, a quantitative QA checklist, and a simple documentation log for AI-supported quantitative work.

Module Breakdown

The session begins by clarifying where AI adds the most value in quantitative evaluation and where it introduces risk. Participants then work through a practical workflow that mirrors real evaluation tasks. First, they translate evaluation questions into an analysis plan. The plan identifies indicators, comparison groups when relevant, and the outputs stakeholders need for decisions.

Next, the session moves into data preparation. Participants use AI to flag common data issues and propose cleaning steps. They also document key decisions and changes as they go.

Then the session shifts to analysis support. Participants generate and check descriptive results and simple comparisons. Along the way, they watch for common interpretation pitfalls. They apply verification routines throughout, including spot checks to confirm accuracy. The session closes with guidance on communicating quantitative results clearly. Participants practice writing limitations, documenting AI use, and preparing tables and visuals that stakeholders can understand quickly.

Who Should Attend

This course is designed for evaluators, researchers, and analysts who work with quantitative data and want practical ways to use AI without sacrificing rigor. It also supports evaluation managers who review quantitative outputs and want a consistent approach to quality checks and documentation.

Prerequisites

No prior AI experience is required. Participants should have basic familiarity with quantitative evaluation work products such as indicators, datasets, descriptive statistics, and simple cross-tab outputs. Participants may use tools they already work with, including Excel, Google Sheets, SPSS, Stata, or R. Advanced software skills are not required.