AI-driven adaptive training for utility employees: a three-wave cluster-randomized field experiment on cognitive-load and personalization-fit pathways.

Evidence that AI-driven adaptive learning outperforms uniform instruction comes almost entirely from student samples in educational settings; whether, why, and for whom it works in real-world workplace training remains largely untested. We conducted a three-wave, matched-pair, cluster-randomized field experiment across six county-level power supply companies of a Chinese provincial electric power utility. Employees (N = 3,000 invited; analysis sample N = 2,574) received either knowledge-graph-driven adaptive training or traditional uniform training covering the same syllabus. Training batches were matched on batch-level characteristics and allocated by random draw before enrollment. Because the two arms differ in delivery medium as well as in adaptive routing, the contrast estimates the effect of a bundled adaptive delivery system rather than adaptivity in isolation. Training self-efficacy was measured at T1 and T2, whereas transfer intention and knowledge were measured at T1, T2, and at the 6-week follow-up (T3). Cognitive load and perceived personalization fit were measured at T2, and measurement invariance across conditions was confirmed by confirmatory factor analysis. Linear mixed models showed small but robust advantages in training self-efficacy (d ≈ 0.26), transfer intention (d ≈ 0.25), and knowledge (d ≈ 0.29 at T2), with all effects surviving three clustering levels, a company-level wild cluster bootstrap, and a stratum-level permutation test. No reduction in the knowledge advantage was observed at follow-up, and equivalence testing bounded any change to less than 0.10 SD (T3 d ≈ 0.26). Parallel mediation showed statistical indirect effects of reduced extraneous load and higher perceived personalization fit on self-efficacy and knowledge, whereas the pathway to transfer intention ran through fit alone. Cognitive-load subscales diverged: adaptive training reduced extraneous load (d = -0.34) but increased intrinsic load (d = 0.28), partially supporting the load hypothesis and cautioning against treating cognitive load as a unitary construct. Exploratory moderation indicated that more experienced employees derived greater benefits, contrary to the compensatory pattern reported for novice workers. Findings extend cognitive load theory and personalization research to a high-reliability industrial workforce and suggest that the motivational returns of adaptive training are tied to employees' perceptions that the training fits them.