<![CDATA[Mandarin has become one of the most important global languages, particularly in the fields of education and business. The HSK exam, as a standard measure of Mandarin language proficiency, requires effective and structured preparation. However, the manual methods still used by language institutions such as Happy Mandarin Course are considered inefficient in helping students identify their personal weaknesses. This study aims to design a web-based HSK exam simulation application equipped with a question recommendation system using the Collaborative Filtering algorithm. The recommendation system is supported by similarity calculations using the Pearson Correlation Coefficient to match questions to users' abilities based on previous results. The application was developed using the waterfall approach, implemented with the Python programming language and the Flask framework. Testing results show that the system can recommend questions adaptively, assist students in improving their skills, and ease the teaching process for instructors. The application can identify users’ weaknesses individually and provide targeted practice, thus offering an effective and adaptive solution for enhancing HSK exam readiness.]]>
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