Comparative Analysis Vision of Worldwide AI Courses

TL;DR

This paper compares undergraduate AI curricula from leading universities worldwide to identify trends, core topics, and differences, aiming to inform better alignment of AI education with industry needs.

Contribution

It provides a comprehensive analysis of global AI curricula, highlighting commonalities, divergences, and alignment with CS2023 standards, enhancing understanding of international AI education practices.

Findings

Identification of core AI topics across universities

Analysis of curriculum convergence and divergence with CS2023

Insights into global educational priorities and methodologies

Abstract

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.