How Our Approach is Different
At STEM AI Studio, students don’t just learn AI—they also receive expert guidance for their future:
Top Universities & Researchers: Students interact with leading academics and AI researchers.
Industry SMEs: Experts from engineering, healthcare, business, and creative industries provide real-world insights.
Q&A & Guidance Sessions: Regular opportunities for students to ask questions, explore career paths, and understand how AI connects to future studies.
University & Career Path Support: Instructors and consultants help students identify pathways for higher studies and make informed choices aligned with their passions and skills.
Outcome
Students graduate with AI skills, STEM knowledge, and informed guidance—ready to make confident decisions about university programs and future careers..
Why This is Important for Future Engineers, Doctors, and Innovators
Even if your dream is to become a doctor, engineer, scientist, or entrepreneur, AI and data skills are crucial for the future:
Medicine & Healthcare: Analyze medical images, predict patient outcomes, and track public health trends.
Engineering & Infrastructure: Optimize designs, simulate systems, monitor energy or bridge safety.
Business & Finance: Forecast trends, automate decisions, and personalize customer experiences.
Science & Research: Make sense of complex datasets, model systems, and accelerate discoveries.
Creative Fields: Generate ideas in art, music, film, or fashion using AI tools.
Learning AI and data skills now gives students a competitive edge, making them future-ready problem solvers in any field.
Phase 1 – Core Foundations (2 months / 8 weeks)
Build confidence in AI, ML, data science, and math—no-code first, with optional coding for advanced students.
Taught by: AI instructors collaborating with Math, Physics, and Bio/Medical SMEs to ensure every project is accurate, credible, and STEM-grounded.
What you’ll learn:
Working with datasets & visualization
Probability & statistics fundamentals
Regression, algorithms, and model thinking
Ethical AI and responsible data use
Hands-on mini-projects each week
Sample Mini-Projects:
Spam detector
Regression predictor
Image classifier
Dashboards
Phase 1 Capstone:
End-to-end AI Project → Clean data → visualize → model → present.
Outcome: 3–4 mini-projects plus 1 integrated capstone, giving students a solid foundation before moving to Phase 2 specialization.
Phase 2 – Applied AI & Domain Focus (6 months / 24 weeks)
Specialize in a domain cluster and build portfolio-ready projects.
Cluster Options:
Engineering: Smart bridges, robotics, climate dashboards
Healthcare: Heart health predictors, synthetic image classifiers
Transportation: Flight delay predictors, smart traffic systems
Creative Arts: AI music, generative art, script analyzers, fashion trends
Business & Social Sciences: Chatbots, financial predictors, AI tutors
Format:
Exploration (first 2 months): 2–3 small projects in your chosen cluster
Specialization (last 4 months): 1–2 polished, larger projects for your portfolio
Consultants (Math, Physics, Bio/Policy) step in to validate accuracy and enrich projects
Capstone Showcase:
One major individual or small-team project, presented as a portfolio artifact (demo + report + presentation).
Outcome: Students graduate with a specialized, consultant-reviewed portfolio, ready to showcase for university/college applications.
Why This is Important for Future Engineers, Doctors, and Innovators
Even at Grades 10–11, understanding AI, data, and STEM fundamentals gives students a head start in any career path:
Medicine & Healthcare: Begin exploring data-driven health insights and wellness trends.
Engineering & Technology: Learn to model, analyze, and optimize systems with AI tools.
Business & Social Sciences: Understand data patterns, build simple predictive tools, and explore automation.
Creative Fields: Experiment with AI-generated art, music, and design.
Science & Research: Develop problem-solving skills and analytical thinking early on.
By building these foundational AI and data skills now, students gain confidence, curiosity, and a portfolio of projects that prepares them for the Innovator Track and future university or career opportunities.
Phase 1 – Core Foundations (2 months / 8 weeks)
Strengthen no-code AI skills with math and data literacy integrated in context.
Taught by: AI instructors collaborating with Math & Science SMEs for accuracy and domain relevance.
What you’ll learn:
No-code ML platforms (Teachable Machine, Lobe, AutoML)
Data types: text, images, time series
Statistics & probability basics
Visualization & dashboards
Supervised vs unsupervised learning
Ethical AI: bias & fairness
Mini-Projects:
Animal image classifier
Movie review sentiment analyzer
Pollution data dashboard
Phase 2 – Applied AI & Cluster Projects (6 months / 24 weeks)
Apply AI in real-world clusters with group projects and instructor guidance.
Cluster Options:
Health & Medicine: Fitness trends, wellness apps
Engineering & Smart Tech: IoT sensors, robotics basics
Environment: Climate dashboards, recycling prediction
Creative Tech: AI art, music remixing, story generators
Business & Society: Chatbots, recommendation systems
Format:
Exploration: Rotate across clusters with 2–3 small projects
Specialization: Pick one cluster → build 1–2 larger projects
Capstone Showcase:
Team-based project (example: Smart Campus Assistant → chatbot + IoT + dashboard).
Outcome: 3–5 polished projects + 1 group capstone, ready for school fairs, hackathons, or stepping stones to the Innovator Track.
Why This is Important for Future Engineers, Doctors, and Innovators
Even at an early stage, learning AI, data, and STEM fundamentals helps students develop curiosity, problem-solving skills, and foundational knowledge that will benefit any career:
Medicine & Healthcare: Explore basic data insights and health trends.
Engineering & Technology: Begin experimenting with logic, sensors, and simple AI tools.
Business & Social Sciences: Learn patterns, create small predictive tools, and understand decision-making with data.
Creative Fields: Discover AI-generated art, music, and interactive projects.
Science & Research: Build analytical thinking and early coding/logical reasoning skills.
Starting early prepares students to confidently progress to Builder Track and beyond.
Phase 1 – AI Foundations (2 months / 8 weeks)
Introduce AI, ML, and Data Science concepts through no-code tools, making learning fun and relatable.
Taught by: AI instructors collaborating with Math & Science SMEs to ensure projects are accurate and STEM-grounded.
What you’ll learn:
What AI is & everyday examples in STEM
Data basics: numbers, text, images
Pattern recognition & logic puzzles
Probability through games (dice, cards, experiments)
Data visualization: charts and simple dashboards
Intro to supervised learning with visual, no-code tools
AI ethics & safety basics
Weekly Mini-Projects:
Rock–Paper–Scissors predictor
Image sorter (pets vs objects)
Emoji sentiment detector
Weather trend visualization
Phase 1 Capstone:
AI Around Us → Each student picks a dataset (sports, space, health, environment) and creates a simple AI demo with visualization.
Outcome: 3–4 mini-projects plus 1 capstone, giving students early portfolio pieces and foundational AI confidence.
Phase 2 – Applied STEM Projects (4 months / 16 weeks)
Apply no-code AI & Data Science to STEM themes. Students explore multiple domains to see AI in action across different contexts.
Themes & Sample Projects:
Health & Biology: Predict daily step counts, nutrition tracker (synthetic data)
Engineering: Smart light controller with rule-based logic
Environment: Recycling classifier, CO₂ emission dashboard
Space & Physics: Predict rocket launches from weather, visualize planetary data
Creative STEM: AI-generated art based on shapes, music remix with simple models
Format:
First half: Short guided projects across multiple themes
Second half: One larger project of choice (solo or pair)
Capstone Showcase:
Individual (or pair) project; example: AI & Climate Explorer → Dashboard predicting temperature trends and recycling outcomes.
Outcome: 4–6 projects across domains, plus 1 larger project, building confidence and curiosity for the Builder Track.
Real Projects. Real Skills. Real Results.
Program Schedule & Tuition
Student Success Journeys Case Studies
How students turned learning into impact.
Created a no-code AI healthcare project featured in
pre-med essays.
Designed a virtual AI robot project that impressed University Admissions.
Applied engineering principles with AI to solve real-world classroom challenges.
Used data skills to build a predictive model for a school innovation fair.
Developed coding and AI projects showcased at a local STEM competition.
Gained confidence to present AI-driven solutions in University Interviews.
