| Sumario: | Background: Numerous higher education institutions worldwide have adopted English‐language‐medium computer science courses and integrated online problem‐solving competitions to bridge gaps in theory and practice (Alhamami Education and Information Technologies, 2021; 26: 6549–6562). Objectives: This study aimed to investigate the factors influencing the use of online competitions in machine learning courses and their impact on student learning. We also analyse disparities in learning outcomes and instructional language effects (Chinese vs. English). Methods: Among 123 participants at northern Taiwan university, 74 chose Chinese instruction (CMI), and 49 opted for English instruction (EMI). The course spanned 18 weeks: team formation in week one, data analysis, machine learning, and deep learning from week 2 to 8, draft proposals and oral presentations by week 9, instructor guidance in weeks 9–17, followed by off‐campus competitions. In week 18, students presented projects for evaluation by judges. Results: The results showed improved scores in competition proposal writing and oral presentations, especially for CMI students, who excelled in these areas and in terms of creativity. CMI students emphasized domain knowledge, implementation completeness, and technical depth in proposals. The EMI students focused on implementation completeness and artificial intelligence model accuracy, along with creativity. Conclusion: CMI students achieved superior outcomes in machine learning courses, particularly in terms of competition proposals, oral presentations, and increased creativity. Instructional language choice significantly influenced learning trajectories, leading to distinct knowledge development focuses for CMI and EMI. 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.
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