Expanded Longitudinal Validation of Adaptive Learning Stability in AI-Assisted Neurosurgical Residency Training.

OBJECTIVE

To evaluate the longitudinal stability, reproducibility, and robustness of Adaptive Learning Stability (ALS) within an expanded AI-assisted neurosurgical residency cohort during a 12-month observation cycle.

DESIGN

This was a prospective controlled educational study. Residents completed 4 consecutive quarterly checkpoints integrating cognitive, technical, and decision-making assessments over 12 months. The RCKaS-AI framework incorporated predictive analytics, individualized adaptive remediation, and mentor-validated feedback pathways. Primary analysis evaluated longitudinal entrustment trajectory stability across checkpoints. Secondary analyses included inter-resident variance dynamics, remediation frequency, faculty workload, and predictive agreement between forecasted and observed competency progression.

SETTING

Two accredited neurosurgical residency programs in Kazakhstan, from January 2025 to January 2026.

PARTICIPANTS

Sixty-four neurosurgical residents randomized into standard RCKaS (n = 32) and AI-enhanced RCKaS-AI (n = 32) cohorts.

RESULTS

Both cohorts demonstrated progressive improvement in entrustment scores across sequential checkpoints. In the RCKaS-AI cohort, mean entrustment scores increased from 59.1 ± 12.8 at CP1 to 94.3 ± 6.9 at CP4, compared with 58.4 ± 13.1 to 85.7 ± 10.4 under standard RCKaS conditions (p = 0.001). Inter-resident variance decreased by 41% in the AI-assisted cohort versus 18% in the standard cohort. Residents within the RCKaS-AI framework achieved entrustment level 4 significantly earlier than controls (38 ± 7 weeks vs 50 ± 9 weeks, p = 0.003). Mean remediation frequency decreased from 3.4 ± 1.1 to 1.9 ± 0.7 sessions per resident, while faculty workload declined by 34%. Predictive analytics demonstrated strong agreement between forecasted and observed competency trajectories (r = 0.84; 95% CI, 0.77-0.89).

CONCLUSIONS

Expanded longitudinal implementation of the RCKaS-AI framework demonstrated reproducible stabilization of competency trajectories during neurosurgical residency training. These findings support the robustness of adaptive AI-assisted feedback systems as a practical mechanism for longitudinal competency regulation within structured surgical education programs. Further multicenter validation is required to evaluate external reproducibility across broader residency environments.