📄 social_science

Generative Artificial Intelligence Policies and Guidelines across Top Ranked Universities: Insights from AI Ecological Education Policy Framework for AI Integration and University Policy Development Framework for GAI

This study analyzes GenAI policies across top-ranked universities in the MENA region, revealing a strong focus on governance and academic misconduct but highlighting critical gaps in stakeholder engagement, local contextualization, and the mitigation of environmental and psychological risks.

Original authors: Mounyah Basil, Abderrezzaq Soltani, Banan Mukhalalati

Published 2026-06-25

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Mounyah Basil, Abderrezzaq Soltani, Banan Mukhalalati

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine the world of universities as a massive, bustling city. For a long time, this city had a set of traffic rules: "Don't run," "Stop at red lights," and "Write your own essays." But suddenly, a new, incredibly fast, and magical vehicle arrived: Generative AI (GenAI). This vehicle can write essays, solve math problems, and create art in seconds.

The city's leaders (the universities) were caught off guard. Some panicked and banned the cars; others let them drive wild. This paper is like a city inspector who went to the top 500 most prestigious universities in the Middle East and North Africa (MENA) to see: Who has written new traffic rules for these magical cars? What do those rules say? And are they missing anything important?

Here is the story of what they found, broken down into simple parts.

1. The Big Picture: Who Has Rules?

The researchers looked at the top universities in 20 countries (from Morocco to Saudi Arabia). They found that about two-thirds (68%) of these top schools have actually written down some rules or guidelines for using GenAI.

The Good News: Most schools aren't ignoring the problem. They are trying to figure out how to handle it.
The Bad News: About one-third of the top schools still have no official rules at all, leaving students and teachers guessing.

2. The Two "Rulebooks" Used to Check the Policies

To make sense of all the different rules, the researchers used two special "checklist" frameworks (like two different sets of glasses to look at the data):

The "Chan" Glasses: These focus on the school's daily life. They ask: How does this affect teaching? How do we stop cheating? How do we make sure everyone is treated fairly?
The "UPDF-GAI" Glasses: These focus on how people feel and think about the technology. They ask: Is the tech easy to use? Is it risky? Do people trust it? Do they feel capable of using it?

By using both sets of glasses, the researchers got a complete picture.

3. What the Rules Actually Say (The Main Themes)

When the researchers read the rules, three big topics stood out, like the three pillars holding up a tent:

A. The "Don't Cheat" Pillar (Academic Misconduct)

This was the most common topic. Schools are very worried about students using the magical car to do their homework without doing the work themselves.

The Rule: "If you use AI, you must admit it."
The Problem: Many schools say "Don't cheat," but they don't have a clear plan for how to catch cheaters or what to do if they do. It's like having a "No Littering" sign but no trash cans or trash collectors.

B. The "Safety First" Pillar (Privacy & Security)

Schools are worried that if you type your secret research or personal student data into a public AI, that data might get stolen or used to train the AI forever.

The Rule: "Don't put your private secrets into the AI."
The Gap: While schools talk about "privacy" and "security," they often use the words interchangeably without explaining the difference. It's like telling people to "be safe" without explaining the difference between locking your door and wearing a seatbelt.

C. The "Help Us Learn" Pillar (Training & Support)

Schools realize they can't just ban the cars; they need to teach people how to drive them safely.

The Rule: "We will offer workshops and guides."
The Gap: Many schools say they will offer training, but they haven't actually built the classrooms yet. Also, they often forget to teach students how to critically think about what the AI says. They need to learn that the AI can lie (called "hallucinations") and that they must double-check its work.

4. What Was Missing? (The Blind Spots)

The researchers found some surprising things that the schools forgot to write about:

The "Human Touch" is Missing: Very few schools mentioned the psychological risks. They didn't warn students: "Don't use AI to talk about your feelings or mental health; it doesn't have a soul and might give bad advice."
The "Planet" is Missing: Almost no one talked about the environmental cost. Using these AI tools uses a massive amount of electricity and water. It's like driving a gas-guzzling car but never mentioning the smoke.
The "Local Voice" is Missing: Many schools just copied rules from big international universities (like Harvard or Oxford) without asking their own local teachers and students what they think. It's like a city in the desert copying the traffic rules of a city in the snow without realizing they don't need snow tires.

5. The Final Verdict

The paper concludes that while universities in the MENA region are waking up to the reality of AI, they are still in the "drafting phase."

They are good at: Saying "Don't cheat" and "Protect your data."
They are weak at: Explaining how to fix problems, training everyone properly, considering the environment, and asking local people for their opinions.

The Bottom Line:
The universities have started building the traffic signs, but they haven't finished building the roads, the safety barriers, or the driving schools. To move forward, they need to stop just copying other cities and start writing rules that fit their own unique neighborhoods, involving everyone from the students to the teachers in the conversation.