An LLM-Based Guided Programming Assistance System for Code Quality Feedback and Formative Assessment

Recent advancements in Large Language Models (LLMs) hold significant promise for reshaping programming education. However, critical instructional challenges such as high failure and dropout rates, insufficient feedback, and inadequate support for students’ independent analytical thinking remain prevalent. Addressing these gaps, this study introduces the Guided Programming and Analysis System (GPAS), an innovative educational approach leveraging LLM-based technology to enhance programming instruction. GPAS is designed to support autonomous learning processes by integrating multi-turn interactive thought guidance, code polishing, semantic annotation generation, and structured scoring mechanisms across multiple programming languages. Experimental results demonstrate significant effects were observed in correlation analysis with expert evaluations (r=0.668, p=6.84×10−12) and paired-sample tests on code and report-level improvements (effect sizes Cohen’s d=0.92 and 1.56, respectively, p=3.56×10−6 and p=1.64×10−10). Additionally, findings revealed that the GPAS platform significantly improved code quality, particularly benefiting lower-achieving students, and effectively captured nuanced improvements in readability, structure, and boundary handling. Moreover, GPAS emphasizes the supportive role of educators, enabling them to focus more effectively on higher-order teaching tasks while the platform handles routine instructional feedback. Collectively, these results suggest that GPAS provides a promising framework for supporting learner-centered programming education and independent problem-solving processes.