Cognitive Dependency in AI-Assisted Programming: Correlation Between GitHub Copilot Usage and Syntactic Memory Degradation in Engineering Students (#2421)

Authors

Benites-Rodriguez, Joseph

Benites-Angulo, Luis

Benites-Angulo, Carlos

Tabacchi-Murillo, Jesus

Rodriguez-Terrones, Jose

Amaro-Guzman, Carlos

Torres-Quiroz, Almintor

Abstract

This longitudinal study examines the cognitive impact of GitHub Copilot on 1,460 engineering students. Using a mixed-methods approach combining cognitive load theory assessments, syntactic retention tests, and development speed metrics, we document a signifi cant inverse relationship between AI assistant dependency and long-term syntactic memory consolidation (r = -0.67, p < 0.001). While intensive Copilot users demonstrated 34.2% faster task completion times initially, they exhibited 41.8% lower syntactic recall in delayed assessments (Week 16) and 53.6% reduced performance in unassisted programming conditions. Analysis revealed that intensive AI use correlates with decreased Germanic cognitive load (r = -0.54, p < 0.001), suggesting reduced deep processing essential for skill acquisition. Domain-specifi c analysis showed a pronounced deterioration in advanced constructs: object-oriented programming (-42.1%), functional programming (-38.7%), and complex data structures (-37.3%). Structural equation modeling confi rmed mediation through Germanic cognitive load (indirect eff ect = -0.33, 95% CI [-0.39, -0.27]), explaining 49% of the total relationship. These fi ndings reveal a critical productivity-learning paradox in AI-assisted programming education, with implications for curriculum design and pedagogical practice in computer science education.