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Resumen diario

Sesión

23E: Artificial Intelligence
Ubicación virtual: VIRTUAL: Agora Meetings

Presentación 3

12:56 - 13:04

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

Joseph Benites-Rodriguez¹, Luis Benites-Angulo², Carlos Benites-Angulo², Jesus Tabacchi-Murillo¹, Jose Rodriguez-Terrones³, Carlos Amaro-Guzman¹, Almintor Torres-Quiroz¹

1: Universidad Nacional del Callao - (PE), Perú; 2: Universidad Nacional de Trujillo - (PE); 3: Universidad Privada Antenor Orrego - (PE)

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.