The Effect of Generative AI as a Coding Assistant in Deep Learning Practicum on Code Quality and Conceptual Understanding - Nurhidayah; Alimin; Ohfit Rijei; Owentianus Nouvic; Putra Langlang Buana; Putra Rajawijaya (Information Technology Education Journal, 2025)
The rapid adoption of Generative AI as a coding assistant in programming education raises critical pedagogical questions regarding its impact on learning quality. This study investigates whether the use of Generative AI in a deep learning practicum enhances students’ code quality and conceptual understanding or merely improves productivity without meaningful comprehension. A quasi-experimental pretest–posttest control group design was employed involving 60 undergraduate students enrolled in a Deep Learning course. The experimental group (n = 30) used Generative AI tools (ChatGPT/GitHub Copilot) during practicum sessions, while the control group (n = 30) relied on conventional resources. Instruments included a validated conceptual understanding test (α = 0.87) and an analytic code quality rubric based on ISO/IEC 25010 standards (κ = 0.82). Data were analyzed using independent samples t-tests and MANOVA at α = 0.05. Results show that the experimental group achieved significantly higher posttest conceptual scores (M = 78.63) than the control group (M = 72.10), t(58) = 3.34, p = 0.001, d = 0.86. Code quality scores were also significantly higher (20.77 vs. 18.12 out of 25), t(58) = 4.57, p < 0.001, d = 1.18. MANOVA confirmed a significant combined effect (Wilks’ Λ = 0.71, p < 0.001). The study was limited to a single institution and a six-week intervention period, which may restrict generalizability and long-term interpretation. This research provides controlled experimental evidence demonstrating that Generative AI can enhance both technical code quality and conceptual mastery in deep learning education, contributing empirical guidance for responsible AI integration in computing curricula