Measuring Mirages

The public conversation about AI’s impact on learning is shaped not only by opinion pieces and casual commentary, but also by a steady stream of research studies that claim to offer definitive evidence of harm. These studies often arrive with impressive institutional affiliations, sophisticated-sounding methodologies, and headlines designed to provoke alarm. Yet when examined closely, many of them share the same underlying weaknesses: narrow definitions of learning, untested assumptions about causation, and a tendency to interpret adaptation as decline. Before accepting their conclusions, it is worth looking closely at how these studies are designed, what they actually measure, and why their results may not mean what they seem to suggest.