MIU AI CAMP for High School Students

Course Overview:

• This CAMP introduces students to Artificial Intelligence through hands-on practice and guided learning.
• Students begin with Python programming fundamentals, then transition into applied AI using Large Language Models and prompt engineering techniques.
• The program incorporates structured workshop content focused on building LLM applications using prompt engineering.
• The final day focuses on building a simple chatbot using guided code templates and a user interface powered by Streamlit or similar applications.

 

Learning Outcomes:

At the end of this CAMP, students will be able to:

• Understand foundational AI concepts.
• Understand Python programming fundamentals.
• Apply basic programming concepts to solve simple problems.
• Explain the basic idea of Large Language Models (LLMs).
• Design and improve prompts for AI systems.
• Use prompt engineering techniques such as zero-shot and few-shot prompting.
• Gain practical exposure to LLM-based applications.
• Modify and complete code snippets to build a chatbot.
• Run and test a simple AI application.

Prerequisites:

• Basic computer usage skills
• Interest in AI and technology

No prior programming experience is required.

Target Certification

• Certificate of Completion from MIU.
• Students will be provided complimentary access to the NVIDIA Deep Learning Institute (DLI) workshop titled “Building LLM Applications with Prompt Engineering.” Upon successful completion, participants will receive an official certificate issued by NVIDIA.

Who Should Attend

• High school students aged 15–18.
• Students who are interested in AI and technology.
• Beginners with no programming experience.
• Students who are interested in ChatGPT and AI tools.
• Students who enjoy practical projects.

Course Content:

• Module 1: Python Programming Fundamentals
• Introduction to Python
• Variables and data types
• Input / Output
• Conditional statements, Loops and Functions
• Hands-on exercises
• Module 2: Introduction to AI & LLMs
• What is Artificial Intelligence?
• Introduction to Large Language Models
• Real-world applications of AI
• Examples using ChatGPT
• Module 3: Prompt Engineering Concepts
• Introduction to LLMs and Generative AI
• Prompt engineering fundamentals
• Designing effective prompts
• Zero-shot and few-shot prompting
• Prompt refinement and optimization
• Controlling output style and structure
• Understanding limitations and safety
• Hands-on notebook exercises
• Module 4: Chatbot Development (Guided Implementation)
• Understanding chatbot architecture
• Working with pre-written code
• Editing prompt templates
• Completing missing Python code snippets
• Running a chatbot using Streamlit
• Testing and improving responses

Recommended Next Course

• Intermediate Python Programming
• Machine Learning Fundamentals
• Deep Learning & Computer Vision

Estimated Time to Completion ≃ 30 Hours

• 6 Hours / Day
• 5 Training Days
• Daily Structure: 5 training hours + 1-hour break

Schedule

Day	Training Session 1	Break	Training Session 2	Topic
Day 1	9:00 AM – 12:00 PM	12:00 – 1:00 PM	1:00 PM – 3:00 PM	Python Basics: Variables, Data Types, and I/O
Day 2	9:00 AM – 12:00 PM	12:00 – 1:00 PM	1:00 PM – 3:00 PM	Python Control Flow, Functions, and Exercises
Day 3	9:00 AM – 12:00 PM	12:00 – 1:00 PM	1:00 PM – 3:00 PM	Introduction to LLMs and Prompt Engineering Fundamentals
Day 4	9:00 AM – 12:00 PM	12:00 – 1:00 PM	1:00 PM – 3:00 PM	Advanced Prompting and Building LLM Applications
Day 5	9:00 AM – 12:00 PM	12:00 – 1:00 PM	1:00 PM – 3:00 PM	Mini Project: Chatbot using Streamlit

Detailed Daily Breakdown

• Day 1 (Python Programming – Basics & Logic)
• What is Programming? Python Overview
• Variables, Data Types, Input/Output
• Conditionals (if-else)
• Loops: for, while
• Practice Problems + Day Recap
• Day 2 (Python Programming – Data Structures & Functions)
• Recap & Warm-up
• Lists, Tuples, Dictionaries
• Functions and Modular Code
• File I/O and Error Handling
• Mini Project: (Part 1)
• Mini Project: (Part 2)
• Day 3 (Prompt Engineering – Part 1)
• Introduction to Large Language Models
• Generative AI concepts
• Prompt structure and design
• Zero-shot prompting
• Few-shot prompting
• Hands-on guided exercises
• Day 4 (Prompt Engineering – Part 2)
• Prompt refinement techniques
• Controlling output format and behavior
• Evaluating prompt effectiveness
• Building simple LLM-based applications
• Working with structured prompts
• Mini exercises and experimentation
• Day 5 (Mini Project)
• Overview of chatbot system
• Modify prompt templates
• Complete missing code sections
• Run chatbot locally
• Test and improve responses
• Mini Project Description
• Students will complete a partially implemented chatbot.
• Tasks include:
• Editing prompts
• Filling missing Python code
• Customizing chatbot responses
• Final output: A working chatbot application.

Assessment Method

• Hands-on exercises
• Participation
• Final mini project

Tools & Platforms

• Python
• Streamlit or similar

Price & Registration:

• MIU Staff Discount: 50% off for children of MIU staff and alumni
• Partner Schools Discount: 20% discount for schools that have a partnership with MIU.
• Includes: Training, certificates, materials

Camp Dates and Batches

Batch 1.	July 5 – July 9
Batch 2.	July 12 – July 16
Batch 3.	July 19 – July 23
Batch 4.	July 26 – July 30
Batch 5.	August 2 – August 6
Batch 6.	August 9– August 13
Batch 7.	August 16– August 20
Batch 8.	August 23 – August 27

Transportation Service

The university may provide optional bus transportation for students at an additional cost of 1,000 EGP. This service is optional for students who wish to use it.

Each batch is limited in capacity to 30 trainees. Registration is on a first-come, first-served basis.

Discounts

Discount Type	Course Discount	Bus Discount
MIU Staff Discount	50%	100%
Alumni Children Discount	50%	50%
Partner Schools Discount	20%	20%