Large Language Models (LLMs) are expected to enhance medical education through personalized clinical skills training. However, their practical application from the student user experience perspective remains underexplored. This gap is critical because without understanding students’ needs, LLM-based tools risk poor adoption and suboptimal learning outcomes. This study explores medical students’ challenges and expectations when using LLM-based clinical skills training through a two-phase investigation involving 14 medical students. We integrated five Type 2 Diabetes cases into a probe platform and conducted probe-based studies followed by co-design workshops. We identified challenges across three categories: dialogue content (lack of realism, insufficient knowledge depth differentiation); dialogue presentation (information overload, single modality limitations); and dialogue interaction (inadequate guidance and feedback). Co-design workshops revealed expectations for enhanced patient modeling, personalized content delivery, structured presentation frameworks, and collaborative features. These findings provide design considerations for developing more effective, user-centered LLM-based medical education systems.