隨著科技進步，諸如生物辨識等人工智慧科技應用已逐漸普及於產業界，其中，關節辨識可以說是輔助了解動作過程的有利助手，妥善運用此技術可以幫助民眾了解運動的軌跡與人體的姿勢，進而增進其學習表現。雖然這類技術在初期都需要有高規格的設備，但是在完善的建立之後，對於設備的效能要求將可以下降，未來更可能可以普及到每個家庭中，因此，如何有效的運用此技術就成為了很好的研究標的。

本研究運用關節辨識軟體處理影片的學習方式，針對台灣科技大學的碩士研究生蒐集了60份樣本，其中有30份為實驗組、30份為對照組的方式進行實證研究，以此來探討運用此技術的認知回饋與結果回饋這兩種不同的學習方式是否會產生差異，結果顯示認知回饋有助於改善心流體驗與學習效果，心流與學習效果之間的中介效果並不顯著。

As the development of technology, artificial intelligence systems, such as biometrics have been widely used upon industries. Skeleton detection, one of those technologies, can help us with realizing the process of an action. To implement this system wisely is helpful for learning the pose of an action. This can result in the enhancing of learning performance. Though this system requires high-performance equipment in the beginning, we can use a lower version after it is well-established. In the near future, we can expect that almost every family can afford this system. Therefore, it is a good question that how to use it with efficiency.

This study uses skeleton detection as a learning tool. 60 NTUST doctor degree students are invited to learning with this system. We divided them into two groups and the difference is using cognitive feedback or not. We find out that cognitive feedback can facilitate flow experience and learning curve. However, the mediation effect between flow and learning is not supported.

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