Generative AI tools such as ChatGPT now perform core cognitive operations—reasoning, synthesis, evaluation, and creative generation—on users' behalf, raising urgent questions for educational psychology about how AI use relates to cognitive development. Yet research on cognitive offloading has largely treated AI use as a unidimensional phenomenon, obscuring a theoretically consequential distinction: whether AI substitutes for the user's own thinking or scaffolds it. Drawing on the autonomous–dependent help typology and self-determination theory, the present study introduces and examines a distinction between dependent cognitive offloading (delegating core thinking to AI) and autonomous cognitive offloading (using AI as a scaffold while retaining cognitive agency). In a three-wave time-lagged survey study ( N = 589 university students and early-career knowledge workers), we tested a dual-pathway model linking the two offloading modes to four perceived downstream cognitive outcomes—autonomous capability, creativity, deep processing, and independent judgment—through cognitive agency transfer and intrinsic motivation. Dependent offloading was positively associated with cognitive agency transfer and negatively associated with intrinsic motivation, which in turn were linked to poorer perceived outcomes. Autonomous offloading was positively associated with intrinsic motivation and more favorable perceived outcomes. Metacognitive monitoring attenuated the link between dependent offloading and cognitive agency transfer but did not buffer the negative motivational association. Notably, both offloading modes yielded comparable immediate benefits despite divergent downstream correlates, suggesting that potentially maladaptive AI engagement may be difficult for users to detect from immediate experience. These findings highlight the manner of AI engagement—not merely its frequency—as a key factor in understanding its associations with perceived cognitive functioning, and point to practical strategies for educators, learners, and AI tool designers seeking to harness AI without undermining cognitive autonomy.

Instructors can prescribe which part of a writing task a student may delegate to generative AI, but not how the student works. This four-arm randomised experiment separated them. At Site A, 744 undergraduates were randomised to one of four divisions of labour across four rounds of report writing—AI drafts and the student evaluates and revises; the student drafts and AI evaluates; the student drafts, AI revises, and the student adjudicates each change; no AI—with all AI withdrawn at Week 6 post-test, immediately after the final round (639 completed). A reduced three-arm replication at Site B randomised 126 students (109 completed). Both arrangements in which the student judged AI-produced text outperformed the arrangement in which AI judged student-produced text and the no-AI control (d = 0.35 to 0.36), whereas having AI evaluate was equivalent to no AI. The benefit was unevenly distributed (arm × tendency F(3, 629) = 7.25, p < 0.001), and logs showed students high in dependent tendency re-outsourcing their assigned evaluation (b = 0.072, p < 0.001). Satisfaction carried no information about error detection (r = 0.015), while self-rated quality was weakly associated with writing performance. Prescribing a division of labour governs the assignment, not the cognition.

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.