Han & Liu 2026 provide survey evidence that supervisory support "weakened the negative associations between AI dependence and research autonomy and innovative behaviour," a micro-level, empirical analogue of Park et al. 2026's model result that mandatory practice preserves capability—together suggesting structured human oversight is the practical lever against AI-induced deskilling.
Han & Liu's survey (N=420) states verbatim that supervisory support 'weakened the negative associations between AI dependence and research autonomy and innovative behaviour' and positions supervisory support as a foundation for epistemic supervision, while Park et al.'s model result that 20% mandatory practice preserves 92% more capability is also stated, so the flagged synthesis is grounded in both texts.
Written by Kimi K3 via Ollama Cloud · checked by GLM-5.3 via Ollama Cloud · 1 Oct, 04:46
Source chain
AEvery quote below was checked, without a model, to appear verbatim in its source.
- 01
“Supervisory support was positively associated with autonomy, self-efficacy and innovation, and weakened the negative associations between AI dependence and research autonomy and innovative behaviour”
Abstract · no page number · evidence E16
- 02
“20% mandatory practice preserves 92% more capability than the simulation baseline (which includes a 5% background AI-failure rate)”
Full-text passage · p. 1 · evidence E3
The enrichment paradox: critical capability thresholds and irreversible dependency in human–AI symbiosis Jeongju Park1, Musu Kim 1, Sekyung Han 1,* 1Department of Electrical Engineering, Kyungpook National University, Daegu, Republic of Korea *Corresponding author: skhan@knu.ac.kr Abstract As artificial intelligence assumes cognitive labor, no quantitative framework predicts when human capability loss becomes catastrophic. We present a two-variable dynamical systems model coupling capability (H) and delegation (D), grounded in three axioms: learning requires capability, practice, and disuse causes forgetting. Calibrated to four domains (education, medicine, navigation, aviation), the model identifies a critical thresholdK ∗∼0.85 (scopedependent; broader AI scope lowersK∗) beyond which capability collapses abruptly—the “enrichment paradox.” Validated against 15 countries’ PISA data (102 points,R 2 =0.946, 3 parameters, lowest BIC), the model predicts that periodic AI failures improve capability 2.7-fold and that 20% mandatory practice preserves 92% more capability than the simulation baseline (which includes a 5% background AI-failure rate).
Passage read from arxiv.org, which may be a preprint rather than the published version.