AI-Driven Precision Learning in Neurosurgical Residency: Integrating Predictive Analytics Into the RCKaS Framework

Area of Science:

• Neurosurgical education
• Medical training
• Artificial intelligence in medicine

Background:

• Competency-based neurosurgical education requires adaptive learning systems.
• The recurrence checkpoint: knowledge and skills (RCKaS) model offers formative evaluations but lacks precision due to manual feedback.
• Scalability and precision are limitations in current neurosurgical training assessments.

Purpose of the Study:

• To integrate artificial intelligence (AI) predictive analytics into the RCKaS framework.
• To evaluate the impact of AI-enhanced RCKaS on learning efficiency and competency progression.
• To assess the effect of AI integration on faculty workload in neurosurgical training.

Main Methods:

• A prospective controlled study involving 60 neurosurgical residents randomized into standard RCKaS and AI-enhanced RCKaS (RCKaS-AI) groups.
• AI engine analyzed multidimensional data (simulation metrics, timing, narrative feedback) using gradient boosting and recurrent neural networks.
• Primary outcome: time to reach entrustment level 4 (independent practice); secondary outcomes: entrustment variability, remediation, faculty time, resident satisfaction.

Main Results:

• The RCKaS-AI group reached entrustment level 4 faster (36 weeks vs. 48 weeks, p=0.004).
• Higher mean entrustment scores in RCKaS-AI (91.7 vs. 82.4, p=0.002) with a strong correlation (r=0.82) between predicted and actual scores.
• Reduced remediation frequency (44%) and faculty time (36%) were observed in the RCKaS-AI group.

Conclusions:

• AI integration transforms RCKaS into data-driven feedback cycles for neurosurgical education.
• The RCKaS-AI model accelerates competency acquisition and enhances feedback accuracy.
• AI-driven adaptive learning promotes consistent longitudinal skill development in neurosurgery residents.