Utilization of Github Copilot And Perceived Improvement in Debugging Skills among College Students at Quezon City University

This study examined the utilization of GitHub Copilot and its relationship with the perceived improvement in debugging skills among Bachelor of Science in Information Technology students at Quezon City University. Using a quantitative descriptive-correlational design, the study involved 150 BSIT students selected through purposive sampling based on their prior experience using GitHub Copilot. Data were gathered through a structured online questionnaire administered via Google Forms and analyzed using weighted mean and Pearson Product-Moment Correlation Coefficient (Pearson r). Findings revealed that respondents often utilized GitHub Copilot in programming-related tasks, particularly when encountering coding errors and debugging code, with an overall weighted mean of 3.94 interpreted as “Often.” The respondents also demonstrated a proficient level of perceived improvement in debugging skills, with a grand mean of 3.00 interpreted as “Proficient.” Correlation analysis further revealed a strong positive and statistically significant relationship between GitHub Copilot utilization and perceived improvement in debugging skills (r = 0.6094, p < .001), indicating that increased utilization of the tool is associated with higher perceived debugging proficiency. The study concludes that GitHub Copilot can serve as an effective AI-assisted programming tool that supports debugging and problem-solving activities while highlighting the importance of maintaining critical thinking and independent coding skills in programming education.

AI-assisted Programming, Debugging Proficiency, GitHub Copilot, Programming Education, Artificial Intelligence

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