# Comparative Analysis Vision of Worldwide AI Courses

arXiv: https://arxiv.org/abs/2407.16881 · HTML: https://mlsanity.com/paper/2407.16881/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 15 references that are in the map.
- First posted on arXiv 4 Jun 2024.

## Builds on (most cited first, 2 of 2)

- [Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services](https://mlsanity.com/paper/2303.16129/) (2023)
- [A Comparative Study of AI-Generated (GPT-4) and Human-crafted MCQs in Programming Education](https://mlsanity.com/paper/2312.03173/) (2023)

## Built on by (most cited first, 1 of 1)

- [A Tool to Map AI Programs in the U.S.: A Snapshot from April 2026 and an Analysis of Requirements for AI Majors and Minors](https://mlsanity.com/paper/2606.12428/) (2026)

## Abstract (arXiv)

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.