Universities · Engineering schools · Private higher education
AI and automation for universities and higher education in Morocco
Published · by Abdellah El Ghazi, founder of TeamElGhazi
Short answer: TeamElGhazi offers higher education institutions an "Applied AI and automation with Python and n8n" module (typically 20 hours over 5 sessions), workshops for teaching staff and technical supervision of final-year projects. Pricing is on quote, based on teaching hours and the number of student groups.
AI is already in the lecture hall
Students already lean on AI assistants for assignments, reports and code. The real question is no longer whether they use it, but whether they can wire it properly into a real process: pull in data, process it with Python, trigger a chain of actions, have a person check the result and document the whole thing. That is what employers expect from a computing or data graduate, and it is what this module teaches, hands-on. Lecturers, for their part, can save time preparing lessons and assessments, as long as they stay in charge.
Three ways to work with us
For students
The applied AI and automation module, built into a semester or taught as an intensive course. It is designed for engineering schools and computer science, data or information systems programmes.
For teaching staff
Workshops on preparing lessons, quizzes and marking with AI while staying in control of content and assessment. A lecturer can also take our AI-Augmented Teacher course on their own.
For final-year projects
Technical supervision of final-year projects in AI and automation: scoping the topic, architecture, code reviews, getting ready for the defence.
The student module in 5 sessions
- 01 · Python for automation: Functions, CSV and JSON files, REST API calls, error handling.
- 02 · n8n workflows: Triggers, webhooks, conditions and routers, logs and alerts.
- 03 · Putting an LLM inside a process: Structured prompting, JSON outputs, human review before anything is approved.
- 04 · Document RAG: Embeddings, document chunking, search in a vector database.
- 05 · Team mini-project: One real process automated end to end, documented and presented.
Learning by doing
- Every session: a short demo, a guided exercise, then an independent exercise corrected in class.
- Tools: Python 3, VS Code, n8n (course instance), a language model through an API, Git.
- Assessment: continuous assessment on the exercises plus the mini-project (demo, commented code, documentation).
- Student deliverables: project Git repository, workflow diagram, handover sheet.
- Prerequisites: basic programming (variables, loops, functions). To brush up beforehand, our Python Fundamentals course (6 weeks online, 1,000 MAD) is enough.
Going further
Students or recent graduates who want to go deeper after the module can join our long course Artificial Intelligence and Automation (4 months, blended, 4,500 MAD), which covers RAG architectures, multi-agent systems and production deployment, and ends with a capstone project. For an individual lecturer, the AI-Augmented Teacher course (2 months online, 1,500 MAD) covers designing materials, personalised follow-up and ethics. You can see examples of projects we have supported on our student projects page.
For your admissions and admin teams, see also our school websites with online enrolment, and for staff training, our corporate AI training page.
How we work together
- Free call: programme, student level, number of groups, where the module sits in the semester.
- Syllabus and quote fitted to your curriculum, with hours and schedule.
- Agreement if your institution needs one: request it from our partnership agreement page.
- Sessions, mini-projects and review with the teaching team.
Frequently asked questions
How much does the module cost for an institution?
Pricing is on quote, based on the number of teaching hours and student groups. The typical format is 20 hours over 5 sessions, adapted to your curriculum. The quote is free.
What level do students need?
Basic programming: variables, loops and functions. The module suits students in engineering schools and in computer science or data programmes. Students who have never coded can get those basics first with our Python Fundamentals course.
Which tools do students use?
Python 3, VS Code, n8n (a course instance), a language model accessed through an API, and Git. These are tools used in industry, and every student leaves with a Git repository of their project.
How are students assessed?
Through continuous assessment on the exercises and the mini-project: a demo, commented code and documentation. Deliverables are the project's Git repository, the workflow diagram and a handover sheet.
Can the module fit into an existing semester?
Yes. It can replace or complement a computing, data or information systems module, or run as an intensive week. Hours and schedule follow your curriculum.
Won't students just let AI do their homework?
The module teaches them to build with AI rather than hand their work over to it: every session moves from a demo to a guided exercise and then an independent exercise corrected in class, and session 3 requires a human check before anything is approved. For younger learners, our TEMO tutor follows the same idea: it guides without giving away the answer.
Do you supervise final-year projects?
Yes, in AI and automation, alongside the institution's own supervisor. Some of the student projects we have supported are shown on our student projects page.
Do you work with institutions outside Casablanca?
Yes. Our office is in Maarif, Casablanca, and we also work with institutions in other Moroccan cities and abroad; the format (on your campus, online or mixed) is agreed with you in the quote.
Get the detailed module syllabus and a quote for your programme.
formation@teamelghazi.com · contact@teamelghazi.com · +212 6 60 66 59 52 · 26 rue Nahas Nahoui, Maarif, Casablanca