Metricized Scaffolds: AI-Enabled Simulation Training and Transfer in Human Resource Development

AI-enabled simulation platforms are increasingly used for talent development, offering scalable, low-risk practice and algorithmic feedback for interpersonal HR capabilities such as difficult conversations, negotiation, and consultation. Yet HRD evaluation cautions that within-platform improvement may not transfer to authentic interactions once dashboards and metrics disappear. We conceptualize AI-enabled simulation training as metricized scaffolding and integrate scaffolding theory with exploration–exploitation learning dynamics to explain when algorithmic supports fade into self-regulation rather than produce metric adaptation (score chasing). We introduce Algorithmic Feedback Literacy (AFL)—learners’ capability to interpret algorithmic feedback, calibrate its authority, and translate cues into portable self-scaffolds—as a mechanism linking metricized practice to transfer. We test this model in an AI-enabled virtual internship and career development program that combines repeated simulation attempts with an authentic consultation assessment. Using platform traces (592 attempts nested within 90 learners), instructor-rated consultation performance as a transfer outcome, and 102 learner reflections, we examine how distributed practice span and practice dose related to simulation gains and transfer, and how consultation complexity changes, which interactional behaviors predicted success. Distributed practice span predicted transfer beyond within-platform gains, while practice dose operated primarily through measurable simulation improvement. Complexity shifted performance relevance toward governance and boundary-setting behaviors. Qualitative evidence identified two developmental pathways: productive internalization, where learners converted metric cues into routines and mode-switching strategies, and metric adaptation, where learners optimized scores without internalizing broader professional judgment. We discuss design implications for AI-enabled HRD and retention-oriented capability building: cultivate AFL through transparency and calibration supports, design for deliberate fading, and pair metricized refinement with authentic assessments to strengthen transfer.