Grade 6-12 AI-native engineering foundation program

Build with AI. Think like an engineer.

A 5-month computer science foundation for ambitious students who want to understand computers, reason clearly, verify AI outputs, and build real projects instead of passively consuming tools.

Preparing the next generation of innovators.Join the next cohort.
Join the next cohort View syllabus
AI-Native
Frontend + Backend
AI Certifications
Production-Grade Projects
> cohort.load("future_builders")
> teach(thinking, systems, code)
> ai.use(mode="assist")
> verify(outputs).then(build)
5months
80+hours
40days
00 / About Me

Mentorship from someone already building in the field.

Mitra Boga headshot

Hi! I am Mitra Boga, a Computer Science Engineering student with a Mechanical Engineering minor at GITAM, after initially studying Accounting and Financial Management at the University of Waterloo. My goal is to help young, driven students build a practical foundation in software, systems, architecture, artificial intelligence and problem solving - not just memorize school material.

I teach from the belief that AI will become a normal and required aspect of engineering work, and those who ignore the inevitable will get outpaced by those who leverage it. Students need the judgment to use it well: how to ask better questions, verify answers, debug code, understand systems, and build real projects with confidence. This is where I can help!

EducationB. Tech, CSE @ GITAM - 8.22 CGPA
Prev. educationBAFM, Data Analytics @ UW
Professional experienceFull Stack Developer @ OpenSauced - GitHub Octernship
Teaching visionHands-on Foundations and Practical Building for intentional students
01 / Positioning

Not a coding class. An AI-first CS foundation.

Kids who only learn technical skills will be outpaced by AI. Students who understand how computers work, how to reason, and how to verify AI outputs become builders instead of consumers.

AI is a tool - not a replacement for thinking.
01

Human reasoning first

Students learn logic, computational thinking, debugging, and systems thinking before treating AI as an answer machine.

02

AI leverage, not dependency

They practice prompting, checking claims, avoiding hallucinations, and using AI to accelerate learning without outsourcing judgment.

03

Projects over memorization

Every concept points toward a build: websites, simple apps, games, automations, diagrams, and final presentations.

04

University-ready confidence

The program builds an early edge for high school CS, hackathons, STEM competitions, and future engineering pathways.

02 / Syllabus

Five months from foundations to real-world builds.

Foundations & computational thinking

Outcome: logic + Python basics

Core CS

  • What is a computer?
  • Components, binary, flowcharts
  • Pseudocode and reasoning
  • Input, output, and data processing
  • How software interacts with hardware
  • Computational problem-solving fundamentals

AI literacy

  • How ChatGPT works, simplified
  • Responsible AI usage
  • Hallucinations and verification
  • How to fact-check AI outputs
  • Understanding AI strengths vs weaknesses
  • Using AI as a learning accelerator

Programming

  • Python variables
  • Loops and conditionals
  • Simple beginner projects
  • Functions and reusable code
  • Debugging beginner errors
  • Logic-building coding exercises

Web development & digital creation

Outcome: first web portfolio

Build

  • HTML, CSS, JavaScript basics
  • Interactive web elements
  • Personal website and portfolio
  • Responsive web layouts
  • Website publishing fundamentals
  • Beginner frontend project deployment

Design

  • UI/UX fundamentals
  • Clean layouts and hierarchy
  • How to avoid AI slop
  • Typography and spacing principles
  • Color theory for web design
  • User-focused design thinking

AI workflow

  • Prompting for code help
  • Debugging with AI
  • Testing generated code
  • Refining AI-generated frontend designs
  • Comparing multiple AI solutions
  • Building faster using AI copilots

Systems, networking & cloud

Outcome: internet intuition

Systems

  • Operating systems
  • CPU, memory, file systems
  • How programs actually run
  • RAM vs storage understanding
  • Processes and multitasking
  • Terminal and command-line basics

Networking

  • IP addresses and servers
  • Internet architecture
  • APIs and data flow
  • How websites communicate online
  • DNS and domain fundamentals
  • Network security basics

Cloud

  • Cloud computing basics
  • Why GPUs matter
  • AI infrastructure diagrams
  • Intro to AWS and cloud platforms
  • Cloud storage and databases
  • Scalable application concepts

Artificial intelligence & engineering mindset

Outcome: verify before trusting

AI concepts

  • Machine learning basics
  • Neural networks
  • Large language models
  • AI training vs inference
  • Computer vision fundamentals
  • Real-world AI applications

Judgment

  • AI ethics
  • Prompt engineering
  • Human + AI collaboration
  • Detecting misinformation
  • Evaluating AI-generated answers
  • Decision-making with AI assistance

Engineering

  • Debugging and tradeoffs
  • Optimization
  • Architecture and systems thinking
  • Breaking large problems into smaller systems
  • Efficiency and scalability concepts
  • Engineering project planning

Real-world building & final projects

Outcome: present a build

Project paths

  • Websites and AI tools
  • Simple apps and games
  • Automation systems
  • AI-powered productivity tools
  • Beginner cloud-based projects
  • Personal portfolio development

Process

  • Brainstorming and scoping
  • Independent thinking
  • Using AI effectively
  • Iterative improvement workflows
  • Research and experimentation
  • Technical documentation basics

Presentation

  • Final demo day
  • Explain engineering process
  • Communicate technical ideas
  • Presentation and storytelling skills
  • Receiving and applying feedback
  • Building confidence as a creator
03 / Credibility

Guided by a builder with academic and industry proof.

Education

Computer Science Engineering

I am currently studying Computer Science and Engineering with a minor in Mechanical Engineering at GITAM University, Hyderabad. I have a background in Accounting and Financial Management from the University of Waterloo, where I specialized in Business Analytics.

8.22 CGPA noted
Certifications

Harvard + AWS

Officially Completed and Certified in Python, SQL, AI and Cybersecurity via Harvard's CS50 courses by Harvard University. Also Attained my AWS Solutions Architect Professional Certification.

AI + CS foundation
Industry

GitHub Octernship

Professional experience includes a Full Stack Developer internship through GitHub Octernship, bringing real software development context into my Matrix Tutoring Academy classroom.

Real-world context
04 / Pricing + Cohort

Flexible tutoring structure with a serious cohort spine.

Program rate
$30CAD / hour

Designed for weekend learning: 1-2 hours per day on Saturdays and Sundays, approximately 80+ guided hours across the 5-month program.

Duration5 months
Cohorts2 per year, 1 month break
Weekly2-4 hours total
IST timing7:30 PM - 9:30 PM
Eastern timing9:00 AM - 11:00 AM EST
05 / Next step

For students ready to become early AI-first engineers.

This program is positioned for curious Grade 6-12 students who want a meaningful edge: technical foundations, project confidence, and the judgment to use AI responsibly.