Bridging Borders with Artificial Intelligence: Transforming Curriculum and Assessment in International Sports Communication

This study explores the integration of artificial intelligence (AI), specifically deep learning (DL), into instructional effect assessment for international sports communication education. The study employed DL algorithms to construct an instructional effect assessment model. Data were collected from students’ learning activities, cleaned, standardized, and transformed into usable formats. Relevant features, such as study habits, knowledge mastery, and interaction metrics, were extracted and selected for model training. Comparative analysis with traditional models, such as linear regression and decision trees, highlighted the advantages of DL in achieving faster convergence and lower prediction error. The findings showed that the DL model could accurately predict instructional outcomes and provide personalized feedback to educators, facilitating data-driven improvements in teaching strategies. Additionally, the integration of AI into curriculum design enabled a holistic learning experience, combining theoretical knowledge, practical skills, and cultural literacy. This research contributes to bridging gaps in interdisciplinary education by modernizing assessment methods and curriculum frameworks through AI highlighting the transformative potential of AI in modernizing interdisciplinary education, particularly in complex fields such as international sports communication. Future research should focus on hybrid AI models, expanding datasets for generalizability, and addressing ethical challenges to ensure the responsible application of AI in education.