What is it about?
Give professionals with similar backgrounds the identical AI tool and their outcomes diverge sharply. This paper asks why. It reviews the evidence on what drives the gap, including cognitive capacity, domain expertise, metacognition, epistemic calibration, personality, and culture, and argues that this variance is not noise around an average benefit but an unsolved empirical puzzle that human resource development research is well placed to take on.
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Why is it important?
Most claims about AI at work describe the average user, but organizations do not employ averages. If the benefit of the same tool depends heavily on who is using it, then training design, hiring, evaluation, and fairness all look different, and studies reporting only mean effects are hiding the most decision-relevant part of the result.
Perspectives
This piece is a companion to my article on the cognitive commons. That one asks what AI adoption does to the supply of professional expertise; this one asks why expertise and related capacities decide who gets value from AI in the first place. The two questions meet in the same place: what a person brings to the tool still matters, and we understand that far less well than we think.
Dr. Nolan Lovett
Old Dominion University
Read the Original
This page is a summary of: Individual Differences in Generative AI Effectiveness: An Empirical Puzzle for HRD Research, New Horizons in Adult Education and Human Resource Development, July 2026, SAGE Publications,
DOI: 10.1177/19394225261472792.
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