Comparative Analysis Vision of Worldwide AI Courses
It provides insights for educators and policymakers to align AI education with industry needs, but it is incremental as it focuses on descriptive analysis without proposing new methods.
This research analyzed undergraduate AI course curricula from universities worldwide to understand global AI education structures, comparing them with CS2023 guidelines to identify trends and differences.
This research investigates the curriculum structures of undergraduate Artificial Intelligence (AI) education across universities worldwide. By examining the curricula of leading universities, the research seeks to contribute to a deeper understanding of AI education on a global scale, facilitating the alignment of educational practices with the evolving needs of the AI landscape. This research delves into the diverse course structures of leading universities, exploring contemporary trends and priorities to reveal the nuanced approaches in AI education. It also investigates the core AI topics and learning contents frequently taught, comparing them with the CS2023 curriculum guidance to identify convergence and divergence. Additionally, it examines how universities across different countries approach AI education, analyzing educational objectives, priorities, potential careers, and methodologies to understand the global landscape and implications of AI pedagogy.