# Artificial intelligence and the transformation of higher education institutions

arXiv: https://arxiv.org/abs/2402.08143 · HTML: https://mlsanity.com/paper/2402.08143/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 135 references that are in the map.
- First posted on arXiv 13 Feb 2024.

## Builds on (most cited first, 11 of 11)

- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [The Impact of AI on Developer Productivity: Evidence from GitHub Copilot](https://mlsanity.com/paper/2302.06590/) (2023)
- [A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT](https://mlsanity.com/paper/2303.04226/) (2023)
- [GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models](https://mlsanity.com/paper/2303.10130/) (2023)
- [On the Educational Impact of ChatGPT: Is Artificial Intelligence Ready to Obtain a University Degree?](https://mlsanity.com/paper/2303.11146/) (2023)
- [Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models](https://mlsanity.com/paper/2304.03271/) (2023)
- [Transformative Effects of ChatGPT on Modern Education: Emerging Era of AI Chatbots](https://mlsanity.com/paper/2306.03823/) (2023)
- [Assigning AI: Seven Approaches for Students, with Prompts](https://mlsanity.com/paper/2306.10052/) (2023)
- [What Should Data Science Education Do with Large Language Models?](https://mlsanity.com/paper/2307.02792/) (2023)
- [Better by you, better than me, chatgpt3 as writing assistance in students essays](https://mlsanity.com/paper/2302.04536/) (2023)
- [Unpacking the "Black Box" of AI in Education](https://mlsanity.com/paper/2301.01602/) (2022)

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

- [Evaluating AI-Powered Learning Assistants in Engineering Higher Education: Student Engagement, Ethical Challenges, and Policy Implications](https://mlsanity.com/paper/2506.05699/) (2025)

## Abstract (arXiv)

Artificial intelligence (AI) advances and the rapid adoption of generative AI tools like ChatGPT present new opportunities and challenges for higher education. While substantial literature discusses AI in higher education, there is a lack of a systemic approach that captures a holistic view of the AI transformation of higher education institutions (HEIs). To fill this gap, this article, taking a complex systems approach, develops a causal loop diagram (CLD) to map the causal feedback mechanisms of AI transformation in a typical HEI. Our model accounts for the forces that drive the AI transformation and the consequences of the AI transformation on value creation in a typical HEI. The article identifies and analyzes several reinforcing and balancing feedback loops, showing how, motivated by AI technology advances, the HEI invests in AI to improve student learning, research, and administration. The HEI must take measures to deal with academic integrity problems and adapt to changes in available jobs due to AI, emphasizing AI-complementary skills for its students. However, HEIs face a competitive threat and several policy traps that may lead to decline. HEI leaders need to become systems thinkers to manage the complexity of the AI transformation and benefit from the AI feedback loops while avoiding the associated pitfalls. We also discuss long-term scenarios, the notion of HEIs influencing the direction of AI, and directions for future research on AI transformation.