Improving student learning performance in machine learning curricula: A comparative study of online problem‐solving competitions in Chinese and English‐medium instruction settings

Lay Description What is already known about this topic Historically, artificial intelligence (AI) education focused on theory and skills, but now there are AI competitions that encourage real‐world problem‐solving (AIdea. Competitions. 2023. https://aidea-web.tw/about?lang=zh ). Competition‐based learning bridges the gap between academia and industry, fostering creativity and talent discovery (Abou‐Warda and Roberts. International Journal of Educational Management . 2016; 30(5): 698). Computer science education globally uses English as the primary language (Alhamami. Education and Information Technologies , 2021; 26: 6549–6562). Non‐English speaking nations are adopting English as the medium of instruction, impacting teaching effectiveness (Alhamami. Education and Information Technologies , 2021; 26: 6549–6562). What this paper adds This study combines online problem‐solving competitions with machine learning courses, using both Chinese and English instruction. Individual tutoring tailored to each team's competition topic provided real‐world problem‐solving experience and fostered school‐enterprise interactions. A rubric was created for evaluating domain knowledge, proposal writing, presentation skills, AI model accuracy, and competition outcomes by external experts, instructors, TAs, and peers. Implications for practice and/or policy Combining competition‐based learning with machine learning courses can boost students' domain knowledge, competition skills, and outcomes. This study confirms that using Chinese instruction in machine learning benefits non‐native English‐speaking students more than English instruction. Our teaching approach for information technology courses can be applied to develop students' relevant skills in this field.