Performance-Understanding Gap (Benner Framework)

The performance-understanding gap describes the condition of practitioners who execute tasks at advanced levels without possessing the underlying perceptual, judgmental, and embodied foundations that would allow them to perform at those levels independently. They produce expert-level outputs because they follow AI recommendations accurately—but the expertise belongs to the algorithm, not to them. Benner's framework predicts this gap as the structural consequence of removing developmental friction: when AI handles the struggle through which understanding is built—applying protocols to messy situations, feeling the weight of committed judgment, accumulating paradigm cases through embodied presence—the practitioner advances in performance metrics without advancing in actual expertise. The gap is invisible to output-based evaluation: the work gets done, patients receive adequate care, efficiency improves. What remains unbuilt is the practitioner's independent capacity to perceive what the data does not show, to recognize when the algorithm's recommendation is wrong, to exercise the caring, situated judgment that expert practice requires.