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Tenth Grade STEM Curriculum

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🔬 Systems Specialists: Modeling, Ethics & Scalable Solutions

An annual 8-assignment plan for tenth grade, designed to master data analytics, systems engineering, and AI ethics.

Main Objective of the Plan

To develop in tenth grade students a specialist mindset, capable of modeling complex systems, applying advanced data science, and leading ethical debates about technology implementation at scale.

STEM Disciplines and Skills

Science: Pharmacology (mechanisms of action), biochemistry (protein folding), statistics (clinical trials).
Technology: Molecular modeling software (Foldit), protein databases (PDB).
Engineering: Bioengineering (design of drug delivery systems, e.g. nanotechnology patches).
Mathematics: Statistical analysis (p-value, significance), dose-response, half-life.


Critical Thinking: Analyze the structure of a clinical trial (phase I, II, III). Why do so many drugs «fail»?
Collaboration: Simulate an FDA review panel to approve or reject a drug.

Hands-on activities

  • Protein Folding (Foldit): Use the game/software «Foldit» to compete and solve protein folding puzzles, understanding the concept of pharmacological target.
  • Clinical Trial Analysis (Simulated): Given a (simulated) data set from a trial, use Google Sheets to calculate efficacy and side effects.
  • Debate: Cost vs. Cure: Investigate the cost of an innovative drug (e.g., gene therapy). Discuss: How is the price set? Who should pay?
Hybrid/Remote Adaptation (Foldit): Foldit is a perfect online platform for individual or team competition from home.
Foldit (web), Protein Data Bank (PDB), Google Sheets, articles on clinical trials.

Formative Evaluation

  • Scoring and reflection on Foldit.
  • Statistical analysis and recommendation (approve/reject) of the drug.
  • Rubric for participation in the ethical debate.

Integration of Ethical Values

Ethics in Clinical Trials: Discuss «placebo» and informed consent.
Animal Testing: Debate the necessity and ethics of animal testing for drug development.

STEM Disciplines and Skills

Science: Physics (aerodynamics, photovoltaics), chemistry (battery storage).
Technology: Electrical network simulators (Microgrids), CAD software (turbine design).
Engineering: Electrical (load balancing), mechanical (turbine design), civil (site) engineering.
Mathematics: Efficiency calculations, cost-benefit analysis (ROI), network optimization.


Systemic Thinking: How do you balance an electrical grid when the source (sun, wind) is intermittent? The role of storage.
Data Literacy: Read and analyze energy production data in real time.

Hands-on activities

  • Turbine Blade Design: Design wind turbine blades (with cardboard or 3D printing) and test them with a fan and a multimeter (connected to a DC motor) to measure the power generated.
  • Smart Grid simulation: Use a simulator (online or Scratch) to manage a city. Balance demand (factories, houses) with supply (solar, wind, gas) without causing a blackout.
  • ROI Analysis of Solar Panels: Use Google Project Sunroof to analyze the solar potential of a building. Calculate initial cost and payback time.
Hybrid/Remote Adaptation (Simulation): The Smart Grid simulation can be done individually. Students share their strategies to avoid outages during a «peak demand».
Tinkercad, Google Project Sunroof, microgrid simulators (web), Scratch.

Formative Evaluation

  • Laboratory report (blade design vs. power).
  • Performance in the «Smart Grid» simulation.
  • Calculation and presentation of the ROI of solar panels.

Integration of Ethical Values

Environmental Justice: Where are the power plants (solar, wind, gas) located? Who benefits and who suffers the visual/environmental impact?
Resource extraction: The ethics of mining lithium and cobalt for batteries.

STEM Disciplines and Skills

Science: Data science (complex systems modeling), behavioral psychology (economics).
Technology: Modeling software (NetLogo), pitch deck platforms (Figma, Canva).
Engineering: Software engineering (development of a MVP - Minimum Viable Product).
Mathematics: Modeling (supply/demand), finance (valuation, market capitalization, ROI).


Critical Thinking: What is a «valuation»? How does a VC (Venture Capitalist) decide what to invest in?
Planning: Create a convincing business plan and pitch deck.

Hands-on activities

  • Macroeconomics Simulation (NetLogo): Use a NetLogo model (e.g. «Sugarscape») to simulate how simple rules (metabolism, vision) lead to macroeconomic outcomes (wealth distribution).
  • Create your Pitch Deck: In groups, develop a (technology) startup idea, define the problem, the solution (MVP), the market and the team. Create a «pitch deck» of 10 slides.
  • Simulation of «Shark Tank» / VC: One group acts as VC (investor) and the other groups «pitch» (present) their startup. The VC must decide how to distribute $1,000,000 of investment.
Hybrid/Remote Adaptation (Pitch Deck): Groups collaborate on the pitch deck using Figma or Google Slides. They record their 5-minute pitch and publish it for VCs to evaluate.
NetLogo (web), Figma, Canva, Google Slides, pitch deck analysis (Y Combinator).

Formative Evaluation

  • Reflection on the NetLogo simulation.
  • Quality and feasibility of the pitch deck.
  • Heading of the presentation and investment decision of the «VC».

Integration of Ethical Values

Impact Investing (ESG): Discuss the role of investment in solving social/environmental problems vs. just maximizing profits.
Inequality: How do VC models contribute to wealth inequality?

STEM Disciplines and Skills

Science: Physics (Standard Model of particles, fission vs. fusion, E=mc²), chemistry (isotopes).
Technology: Simulators (virtual fog chamber), data analysis (CERN - concept).
Engineering: Nuclear engineering (Tokamak/Stellarator design - conceptual), confinement (magnetic/inertial).
Mathematics: Orders of magnitude, balancing of nuclear equations.


Critical Thinking: Why is fusion so difficult to achieve on Earth (vs. the Sun)? What is a «boson» vs. a «fermion»?
Creativity: Explain a quantum concept (e.g., quark «flavor») with an analogy.

Hands-on activities

  • CERN Data Analysis (Simulated): Use an online «fog chamber» simulation to identify different particles (electrons, positrons, muons) based on their trajectories.
  • Magnetic Confinement Model: Simulate a «Tokamak» using magnets and iron filings (or ferrofluid) to show how a magnetic field can «confine» the (conceptual) plasma.
  • Debate: Fission Now or Fusion Later? Debate energy policy: Should we invest massively in fission (nuclear energy today) to combat climate change now, or go all in on fusion (energy of the future)?
Hybrid/Remote Adaptation (Data Analysis): Students can use online fog chamber simulators and share their findings of «exotic» particles.
Fog Chamber Simulators (web), Phet, CERN videos, Kurzgesagt.

Formative Evaluation

  • Particle identification report.
  • Explanation of the Tokamak model.
  • Argumentative essay on nuclear energy policy.

Integration of Ethical Values

Cost of «Big Science»: Is it ethical to spend billions on CERN or ITER (fusion) when there is global poverty?
Dual Use: The historical connection between nuclear physics and weapons.

STEM Disciplines and Skills

Science: Optics, neuroscience (how the brain sees), data science (classification).
Technology: Programming (Python with OpenCV - basic), AI (Teachable Machine).
Engineering: Robotics (sensor/actuator integration), navigation algorithms (e.g. A* - concept).
Mathematics: Linear algebra (matrices for image filters), geometry (spatial mapping - SLAM).


Critical Thinking: Why is «seeing» so difficult for a computer? What is «bias» in a CV model?
Collaboration: Train a team CV model and analyze why it fails.

Hands-on activities

  • Train a VC Model (Teachable Machine): In groups, train a model (e.g. «recyclable vs. non-recyclable») using the webcam. Test it with new objects and analyze its failures.
  • Autonomous Robot Simulation (Labyrinth): Program a robot (virtual or physical, e.g. Arduino with ultrasonic sensor) to navigate a maze and reach the end without human intervention.
  • Debate: Autonomous Vehicles and the «Trolley Problem»: Discuss the «Trolley Problem» applied to autonomous vehicles. How should a car be programmed to decide in an unavoidable accident?
Hybrid/Remote Adaptation (Teachable Machine): It is web-based 100%. Students can train their models and share links, competing for model «accuracy».
Google Teachable Machine, Tinkercad Circuits (Arduino), robotics simulators (web).

Formative Evaluation

  • Teachable Machine model (with failure and bias analysis).
  • Robot success in the maze (or functional code).
  • Reflective writing on the «Trolley Problem» and the ethics of AI.

Integration of Ethical Values

Surveillance: The use of facial recognition by governments and companies.
Autonomous Weapons: Discuss the ethics of «killer robots» (LAWS).
Bias: What if a CV model is trained with biased data and used for recruitment?

STEM Disciplines and Skills

Science: Computational linguistics, data science (embeddings models).
Technology: Programming (Python with NLP libraries, e.g. NLTK), LLM APIs.
Engineering: Software engineering (design of AI «agents»), system architecture (RAG - Retrieval-Augmented Generation).
Mathematics: Statistics (probability of tokens), linear algebra (vector spaces).


Digital Literacy: Understand how fine-tuning and RAG make an LLM smarter.
Critical Thinking: What is an AI «agent» (e.g. Auto-GPT) and how can we «trust» them?

Hands-on activities

  • Sentiment Analyzer (Python/Colab): Use Python and NLTK/VADER to build a simple parser that rates movie reviews as «positive» or «negative».
  • AI «Agent» Design (Conceptual): Design a system of «agents» (e.g. «Research Agent», «Writing Agent», «Critical Agent») to perform a complex task (e.g. «plan a vacation»).
  • Debate: »Stochastic Parrots» or «Sparks of AGI»? Debate whether LLMs «understand» the world (as Microsoft argued) or whether they are simply «stochastic parrots» repeating patterns.
Hybrid/Remote Adaptation (Colab): The sentiment analyzer is an ideal programming activity for Colab. Students can try it with their own sentences.
Google Colab (Python, NLTK), Google Gemini, articles (Sparks of AGI, Stochastic Parrots).

Formative Evaluation

  • Functional Colab Notebook (with precision analysis).
  • Flowchart of the AI «agent» system.
  • Argumentative essay on «understanding» LLMs.

Integration of Ethical Values

Ownership and Plagiarism: Is it «plagiarism» if an AI writes the 90% of your work?
LLM Security: «Prompt injection», «jailbreaking» and the risk of LLMs generating malicious code or disinformation.

STEM Disciplines and Skills

Science: Remote Sensing, GIS, oceanography, data science (satellite image analysis).
Technology: GIS software (QGIS - free), satellite databases (Landsat, Sentinel).
Engineering: Aerospace engineering (satellite orbit design), logistics (optimization of shipping routes).
Mathematics: Spherical geometry, optimization (e.g. «commuter problem»).


Creativity: Using satellite data to tell a «story» about a change in the Earth.
Critical (Systemic) Thinking: How does a «traffic jam» in the Suez Canal (logistics) impact satellite data (ships waiting) and the global economy?

Hands-on activities

  • Deforestation Analysis (GIS): Use QGIS or Google Earth Engine (Code Editor) to compare Landsat imagery (e.g. of the Amazon) from 1990 and 2024. Calculate (approx.) area lost.
  • Logistics Route Optimization: Given a map and 5 cities, calculate (manually or with a simple algorithm) the shortest route for a delivery truck (Commuter Problem).
  • Satellite «Constellation» Design: Design a constellation (e.g. for global internet or disaster monitoring). Discuss: How many satellites, what orbit (LEO, MEO, GEO)?
Hybrid/Remote Adaptation (GIS): Google Earth Engine (Code Editor) is web-based and allows very powerful GIS data analysis remotely.
QGIS (desktop), Google Earth Engine, Landsat/Sentinel data, Google Maps.

Formative Evaluation

  • Comparative GIS map (with deforestation analysis).
  • Calculation of the optimized logistics route.
  • Presentation of the constellation design.

Integration of Ethical Values

Light Pollution and Space Debris: The impact of megaconstellations (Starlink) on astronomy and orbital safety.
Privacy and Oversight (EOS): Who can «watch» from space? How detailed?

STEM Disciplines and Skills

Science: Advanced research methodology, data analysis (t-tests, chi-square).
Technology: Product development (MVP), version control (Git/GitHub), publishing (web).
Engineering: Software development life cycle (SDLC), user testing (UX/UI).
Mathematics: Statistical analysis, financial projections (business model).


Collaboration: Project management (Agile/Scrum), team roles (PM, Dev, UX, Researcher).
Critical (Systemic) Thinking: Take a project from an abstract idea to a tangible product/paper and defend it publicly.

Hands-on activities

  • Track 1 (Entrepreneurship): «Pitch + MVP»: Identify a problem, design a solution (Figma), build a functional MVP (Replit/Glitch), and create a business plan/pitch to «launch» it.
  • Track 2 (Research): «Hypothesis + Paper»: Formulate an original research question, design an experiment, collect data, analyze it statistically and write a full paper.
  • Launch and Publication« Fair (Final): Present the project (MVP or paper) to a panel of judges (teachers, local professionals, other students) as if it were a Demo Day or symposium.
Hybrid/Remote Adaptation (Virtual Project): Use GitHub/GitLab for code, Figma for design, and a website (Google Sites) as the virtual trade show «booth», including a demo video.
GitHub, Figma, Google Colab, Replit/Glitch, Google Sites, Trello (management).

Formative Evaluation

  • Quality of the MVP (functionality, design) or of the «Paper» (rigor, analysis).
  • Project documentation (e.g. README on GitHub).
  • Final presentation and defense of the project (rubric).

Integration of Ethical Values

Intellectual Property (IP): Discuss licenses (MIT, GPL) vs. patents. Who owns the project?
Launching« philosophy: Debate «Move fast and break things» (Facebook) vs. «Slow science» (rigorous science).
Resilience: The value of a «successful failure» (an experiment that disproves the hypothesis).

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