## 5 Discussion  
The results indicate that LLMs play a dual role in assisting students with web development tasks. On the one hand, students who focused on error fixing were more likely to produce functioning applications, suggesting that LLMs excel at helping students resolve bugs and issues efficiently. On the other hand, the improvement in performance for students who asked about code understanding points to the educational value of AI, as these students could leverage the tool to deepen their knowledge of the underlying concepts. These results suggest that LLMs are particularly effective debugging and conceptual clarification tools, reinforcing their value in software engineering education. More specifically, our research shows the following. RQ1: How does using LLM-based coding assistance impact the likelihood of students producing runnable code, considering their prior coding experience? Using tagged queries for EF and CU significantly increased the rate of runnable submissions, indicating that LLM-based assistance can improve coding outcomes when students frame their requests around debugging or conceptual comprehension. This partially supports H1: LLM-based assistance does boost runnable code production overall, but its effects are strongest in contexts where the problem is clearly specified. RQ2: How does the formulation of queries when using LLM-based coding assistance influence the effectiveness of the assistance? The statistically significant improvements for EF (U = 135.50, \(p\) = 0.042) and CU (U = 105.00, \(p\) = 0.030) confirm H2: precise query formulation substantially enhances the efficacy of LLM support. In contrast, other query types showed no significant differences, highlighting the critical role of clarity and specificity in prompt design for debugging and understanding tasks. RQ3: How does prior coding experience affect the benefits students receive from LLM-based coding assistance?While novice students exhibited the most pronounced gains in EF and CU conditions, differences across experience levels in other categories did not reach statistical significance. Thus, H3 is only partially supported: less experienced students benefit markedly from well-tagged queries in debugging and comprehension, but further research with larger samples is needed to clarify how expertise influences LLM-driven improvements in feature implementation, optimization, or best-practice inquiries. Interestingly, the findings show that query types like feature implementation or code optimization did not significantly correlate with better performance. This might imply that students are not yet fully equipped to translate AI-generated feature suggestions into well-functioning code or that optimizing code might not have been a priority in the limited time frame of the experiment. Additionally, optimization often requires a broader understanding of trade-offs, which may not be fully conveyed through LLM-generated responses. Furthermore, the results suggest that query formulation skills are critical for maximizing the benefits of AI-assisted programming. Students who specified their needs, especially in error fixing and conceptual understanding, received more actionable guidance from the LLM. This finding reinforces the importance of prompt engineering as a skill in modern software development and education. Additionally, the study highlights the complementary role of LLMs in education. While LLMs can assist with debugging and explaining concepts, they are not a substitute for fundamental programming skills. Educators should encourage students to combine AI assistance with traditional problem-solving approaches to prevent over-reliance on automated tools. The findings of our study complement existing research [24] by providing a domain-specific examination of AI-assisted tasks in software development, whereas prior studies focus on AI's role in supporting creative processes within early-stage startups.