This study critically evaluates the efficacy of GitHub Copilot in low-level programming education, specifically within C programming tasks involving complex concepts like memory management and pointer manipulation. While AI tools have shown promise in supporting high-level programming, its impact on skill-intensive, low-level contexts remains underexplored. We conducted a within-subject experimental study with 34 graduate computer science students, assessing performance on AI -assisted and independent tasks. Statistical analyses revealed that Copilot, one of the AI programming tools, enhances productivity in routine coding activities; however, it is insufficient for tasks requiring deep problem-solving skills. Notably, a significant performance decline in AI-free tasks suggests a dependency on Copilot that may hinder the development of essential independent problem-solving abilities. Survey feedback underscores ethical concerns, with 40.6 % of students expressing uncertainty about responsible AI usage and potential over-reliance. These findings highlight the ne-cessity for structured instructional practices, including AI-free assessments and clear ethical guidelines, to promote balanced technology integration in programming education. This study contributes to educational theory by illuminating the limitations of generative AI within constructivist and self-regulated learning frameworks. Future research should explore the long-term effects of AI dependency on technical skill development and investigate AI advancements tailored for low-level programming to better support foundational skills.