Artificial Intelligence in Program Evaluation: Insights and Applications
Bibliographic record
Abstract
The practice note outlines six approaches to integrating artificial intelligence (AI) and machine learning (ML) into program evaluation, enhancing traditional methods with data-driven insights and improved efficiency. These approaches aim to address the growing need for evaluators to analyze complex datasets accurately while reducing manual effort. They include identifying patterns in data to uncover trends and outliers, using predictive models for forecasting outcomes, pinpointing areas for improvement by analyzing performance, simplifying data interpretation through visualizations, automating data analysis for efficiency, and leveraging dashboards for real-time monitoring and decision-making. The note highlights that while it does not offer guidance for evaluators with limited technical expertise, it provides a framework for integrating AI into evaluation practices. Successful implementation requires understanding stakeholder needs, fostering client engagement, ensuring tool usability, and maintaining effective communication. While AI can enhance evaluation quality and innovation, ethical considerations and biases in AI algorithms must be carefully addressed. These AI-powered techniques can enable more robust, evidence-based decision-making, supporting positive social impact.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.051 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".