Date: Tuesday, May 12, 2026
Hello. I’m Arthur Hernandez, Vice Chair of the Joint Committee on Standards for Educational Evaluation. During my 40+ years of evaluation research and practice, I’ve watched our field cycle through hand-tabulated data, mainframe printouts, desktop publishing, infographics, and interactive dashboards. Each arrival was heralded as transformative. And to a degree, each one also introduced new ways to get things wrong. Now artificial intelligence is here, and I confess I find the pattern familiar.
I remember when desktop charting software first became widely available in the late 1980s. Suddenly everyone could produce colorful graphs, and suddenly evaluation reports were full of misleading visuals, such as truncated axes, three-dimensional pie charts that distorted proportions, decorative elements that obscured findings. The tool made production easy. It did nothing to ensure quality. AI presents the same bargain at a much larger scale. I recently watched a demonstration where an AI tool generated a complete data dashboard from a spreadsheet in under a minute. It was impressive. It was also wrong in ways that would have violated several of the Program Evaluation Standards, particularly around accuracy and transparency. The speed was real. The rigor was absent.
Here is my practical suggestion, and it is a simple one. Before you share any AI-assisted visualization or report, run it through the lens of the Program Evaluation Standards. Ask yourself: Is this accurate? Is it transparent about its methods and limitations? Does it serve the information needs of its intended users? Is it fair in how it represents the people and programs being evaluated? These questions predate AI by decades, and they remain the right questions. Because good tools alone do not automatically produce good evaluations.
Something that warrants particular attention: AI tools carry embedded assumptions about how data should look, who the audience is, and what counts as clear communication. These assumptions tend to reflect dominant cultural norms. I would encourage any evaluator using AI for visualization to ground their work in culturally responsive evaluation frameworks. Stafford Hood, Rodney Hopson, and Henry Frierson have done essential work reminding our field that how we represent data is never culturally neutral. The Urban Institute’s Do No Harm Guide offers practical guidance for applying that awareness to visualization specifically. No AI tool I have encountered asks whose perspective is being centered in a chart. That question remains ours to ask.
I have been at this long enough to know that the tools will keep changing. What endures is our obligation to get it right, not just to get it done.
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