A Systematic Literature Review on Large Language Models Applications in Computer Programming Teaching Evaluation Process

Tools based on the use of Large Language Models (LLMs) have improved the computer programming teaching process, automated feedback processes, facilitated program repair, and enabled personalized learning experiences. This research examines which and how LLM-based opportunities are applied in the computer programming teaching assessment process and how LLMs are applied to improve evaluation accuracy, their impact on student learning outcomes, and the challenges in scaling these technologies. Key opportunities arise from prompt engineering, which optimizes precision and LLM-generated feedback relevance, and feedback propagation techniques, which offer scalable solutions for large-scale programming courses. LLMs are also applied effectively in debugging assistance to detect and repair syntactic and semantic errors in student code. This review identifies several research directions, including prompt engineering refinement, improved feedback system scalability, and deeper exploration of the long-term educational impacts of LLM. The study concludes that LLMs are effective in enhancing the assessment process, but a balanced approach combining human oversight with automated feedback is crucial to fostering critical thinking and ensuring long-term learning success in programming education.