AI Literacy Across Every Major: Middle East Universities Move Fast-and Put Ethics First

Universities Urged to Embed AI Literacy Across All Disciplines

AI is rewriting how work gets done, and campuses across the Middle East are moving fast to keep pace. The UAE has seen a 344% year-over-year surge in enrollments for Generative AI courses and is introducing AI as a formal subject across all public schools from 2025.

Student adoption is widespread, but critical evaluation lags. That gap is now an institutional priority for leaders across higher education.

Why AI literacy must go beyond tools

AI literacy is more than prompt tricks or app demos. It's technical fluency paired with ethics and critical thinking-using AI, then questioning it with "appropriate caution."

Students should learn how these systems work, where they fail, and how to make grounded decisions when outputs look confident but aren't.

The data educators can't ignore

• UAE: 344% year-over-year jump in Generative AI course enrollments (Coursera 2025 Global Skills Report).
• 92% of students report using AI in their academic work (2025 HEPI survey).
• 83% of faculty worry students can't critically evaluate AI-generated output.
• Only 7% of knowledge workers are proficient with AI tools (Sections AI Proficiency Report).

Translation: usage is high, judgment is uneven, and employers are wary. Universities need clear standards and practice-based learning to close the gap.

Make AI cross-curricular

AI literacy sticks when it's embedded where students already think and build. Architecture students generate design options with GenAI and critique feasibility. Life Sciences students run machine learning on genomic data and validate results. Business students test AI-assisted forecasting against historical performance. Liberal arts scholars mine historical texts, then verify sources.

Interdisciplinary, hands-on, and accountable-this is where skills compound.

Build a verification habit

Generative tools can produce convincing but false or biased content. Large language models can fabricate answers and citations. That's why verification is a core skill, not an optional add-on.

Design coursework where students compare manual work to AI output, trace claims back to sources, and document what holds up. Include bias cases-such as demonstrations of face recognition errors on darker skin tones-to make fairness concrete and measurable in class projects.

Partner with industry for labs and standards

Co-develop AI labs with tech companies so students work with current tools, real datasets, and real constraints. This addresses employers' concerns about graduate readiness and exposes students to live issues like data privacy, security, and bias mitigation.

A practical blueprint for academic leaders

• Define program-level AI literacy outcomes for every discipline (technical fluency, critique, ethics, and communication).
• Publish an "acceptable AI use" policy for assessments: disclosure rules, citation format for AI assistance, and consequences.
• Embed structured AI modules: prompting basics, limits and failure modes, source checking, bias testing, and responsible use.
• Redesign assessments: open-AI tasks with documented process, paired with no-AI checks (in-class writing, oral defenses, whiteboard problem-solving).
• Run verification drills: fact-checking, citation tracing, retrieval vs. generative comparisons, reproducibility checks.
• Add bias audits to projects: define fairness metrics, test outcomes across subgroups, report and mitigate.
• Invest in faculty upskilling: short intensives, peer clinics, and micro-credentials. Curate course pathways by job role and skill level via Complete AI Training.
• Stand up safe sandboxes: approved AI tools, data-governed environments, and logging for learning analytics.
• Formalize partnerships: MOUs with vendors and employers for guest lectures, capstones, internships, and tool access.

Measure what matters

• Student disclosure rates for AI use and quality of documentation.
• Performance on model-critique challenges (spotting errors, verifying claims).
• Bias audit quality in student projects and corrective actions taken.
• Employer feedback on graduate readiness and internship conversion rates.
• Incidents of academic misconduct related to AI and time-to-resolution.
• Equity outcomes: whether AI-supported teaching narrows or widens gaps.

Economic context and urgency

The region's digital economy plans depend on graduates who can work confidently with AI. PwC projects AI could add around $320bn to the Middle East's GDP by 2030. That opportunity favors institutions that move first and execute well.

Read PwC's analysis.

Bottom line

Combine structured AI modules, critical thinking exercises, strong ethical guidelines, and industry partnerships. Treat AI as a starting point-then verify, question, and improve. That's how universities graduate students who are both tech-savvy and ethically grounded, ready to work with AI responsibly across every discipline.