Motor-skill learning systems in XR rely on persistent cues. However, constant cueing can induce overreliance and erode memorization and skill transfer. We introduce a skill-adaptive, dynamically transparent ghost instructor whose opacity adapts in real time to learner performance. In a first-person perspective, users observe a ghost hand executing piano fingering with either a static or a performance-adaptive transparency in a VR piano training application. We conducted a within-subjects study (N=30), where learners practiced with traditional Static (fixed-transparency) and our proposed Dynamic (performance-adaptive) modes and were tested without guidance immediately and after a 10-minute retention interval. Relative to Static, the Dynamic mode yielded higher pitch and fingering accuracy and limited error increases, with comparable timing. These findings suggest that adaptive transparency helps learners internalize fingerings more effectively, reducing dependency on external cues and improving short-term skill retention within immersive learning environments. We discuss design implications for motor-skill learning and outline directions for extending this approach to longer-term retention and more complex tasks.

翻译：扩展现实（XR）中的运动技能学习系统通常依赖持续性提示。然而，恒定的提示可能导致学习者产生过度依赖，削弱记忆效果与技能迁移能力。本研究提出一种技能自适应、透明度动态调节的幽灵导师系统，其不透明度可根据学习者表现实时调整。在一款VR钢琴训练应用中，用户以第一人称视角观察演示钢琴指法的幽灵手，该幽灵手采用静态透明度或基于表现的自适应透明度。我们开展了一项被试内实验（N=30），学习者分别使用传统静态（固定透明度）模式与我们提出的动态（表现自适应）模式进行练习，并在无指导条件下立即接受测试及10分钟保持间隔后再次测试。相较于静态模式，动态模式在音高与指法准确性方面表现更优，错误增幅有限，且节奏准确性相当。这些结果表明，自适应透明度有助于学习者更有效地内化指法，降低对外部提示的依赖，并提升沉浸式学习环境中的短期技能保持效果。我们探讨了该设计对运动技能学习的启示，并展望了将此方法延伸至长期保持及更复杂任务的研究方向。