Large language models present new opportunities for teaching and learning. The response accuracy of these models, however, is believed to depend on the prompt quality which can be a challenge for students. In this study, we aimed to explore how undergraduate students use ChatGPT for problem-solving, what prompting strategies they develop, the link between these strategies and the model's response accuracy, the existence of individual prompting tendencies, and the impact of gender in this context. Our students used ChatGPT to solve five problems related to embedded systems and provided the solutions and the conversations with this model. We analyzed the conversations thematically to identify prompting strategies and applied different quantitative analyses to establish relationships between these strategies and the response accuracy and other factors. The findings indicate that students predominantly employ three types of prompting strategies: single copy-and-paste prompting (SCP), single reformulated prompting (SRP), and multiple-question prompting (MQP). ChatGPT's response accuracy using SRP and MQP was significantly higher than using SCP, with effect sizes of -0.94 and -0.69, respectively. The student-by-student analysis revealed some tendencies. For example, 26 percent of the students consistently copied and pasted the questions into ChatGPT without any modification. Students who used MQP showed better performance in the final exam than those who did not use this prompting strategy. As for gender, female students tended to make extensive use of SCP, whereas male students tended to mix SCP and MQP. We conclude that students develop different prompting strategies that lead to different response qualities and learning. More research is needed to deepen our understanding and inform effective educational practices in the AI era.