Integrating large language models (LLMs) into higher education, particularly in programming education, reshapes how students interact with learning materials and develop coding skills. However, while the general utility of LLMs like ChatGPT, Gemini, and Claude has been acknowledged, a critical gap exists in understanding how specific prompting strategies influence student learning outcomes. This issue is significant in the context of programming education, where problem-solving, critical thinking, and conceptual understanding are essential yet complex cognitive skills. Although prior research has classified prompting behaviors, it has largely failed to assess their impact on actual learning. To address this gap, we explored how IT students employ various prompting strategies when engaging with LLMs during programming tasks. A mixed-methods approach was adopted, primarily qualitative and supported by basic quantitative analysis, to examine 842 prompts generated by 140 students across four core software development modules. The results revealed five dominant prompting strategies, which varied significantly in how they facilitated learning. Our findings suggest that prompting strategies significantly shape how students interact with LLMs and influence the depth of their learning.