## 2 Research Background  
The increasing adoption of LLMs in education raises questions about their role in learning processes, particularly in technical domains like computer science. Tools like LLMs offer several benefits, including instant code generation, problem-solving advice, and explanations of abstract programming concepts. However, evaluating whether such tools enhance learning or inadvertently enable dependency on AI for problem solving without proper understanding is essential. In the context of software engineering, the use of structured prompting across different phases of the Software Development Life Cycle (SDLC) has shown that Generative AItechniques like ChatGPT can reduce skill barriers and accelerate development by improving the quality of responses, depending on the quality of the initial prompts [5]. Moreover, several design techniques, particularly prompt patterns, are being explored to automate common software engineering tasks [6]. These patterns address issues like ensuring code decoupling from third-party libraries, creating API specifications from requirements lists, and improving requirements elicitation, rapid prototyping, code quality, deployment, and testing. LLMs have rapidly transformed software development, especially in code generation. However, their inconsistent performance, propensity to hallucinations, and quality issues complicate program comprehension and hinder maintainability. Research [7] indicates that prompt engineering, the practice of designing inputs to direct LLMs towards generating relevant outputs, may help address these challenges. In this regard, researchers have introduced prompt patterns and structured templates to guide users in formulating their requests. However, the influence of prompt patterns on code quality has yet to be thoroughly investigated. An improved understanding of this relationship would be essential to advancing our collective knowledge on how to effectively use LLMs for code generation, thereby enhancing their understandability in contemporary software development. This paper empirically investigates the impact of prompt patterns on code quality, specifically maintainability, security, and reliability, using the Dev-GPT dataset. Results show that Zero-Shot prompting is most common, followed by Zero-Shot with Chain-of-Thought and Few-Shot. Analysis of 7583 code files across quality metrics revealed minimal issues. Kruskal-Wallis tests indicated no significant differences among patterns, suggesting that prompt structure may not substantially impact these quality metrics in ChatGPT-assisted code generation. Recent work in prompt engineering has identified several standard interaction modes for eliciting effective responses from LLMs. Notably, [PERSON] et al. [8] introduced zero-shot and few-shot prompting strategies, where task instructions are given with or without examples, respectively. Later studies extended this taxonomy to include methods such as instruction-based prompting [9] and chain-of-thought prompting for eliciting reasoning steps [10]. These frameworks provide useful guidance for understanding how prompt design impacts LLM output quality and reliability. However, most prior work has focused on performance in benchmark tasks (e.g., reasoning, translation, math), while relatively few studies have investigated how novice users, such as students, naturally formulate prompts during open-ended software development tasks. Building on the growing interest in prompt engineering, recent research has proposed the use of prompt pattern catalogues as structured, reusable solutions to common interaction challenges with LLMs. Similar to software design patterns, these prompt patterns help guide users towards more effective and efficient prompting strategies across various domains, including software development and education. This approach moves beyond individual prompt examples to classify and document broader interaction strategies, enabling users to develop mental models for structuring prompts [11].