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Browse project ideas by Polygence mentors
Outdoor Folklore: A Montessori Lesson Plan
This lesson plan is meant to highlight the natural relationship between science and the humanities. Science is a way of understanding the world around us, and we can widen the perspective that it gives us by integrating folklore into the way we guide students through scientific reasoning. It is developed for Montessori students between ages 6 and 12.
Creative Writing, History, Literature, Arts
Product Carbon Footprints: Understanding the Climate Impact of What We Buy
Every product we purchase—from smartphones to sneakers to groceries—has a hidden carbon footprint that extends far beyond what we see on the shelf. This project explores how to calculate and compare the full lifecycle carbon emissions of consumer products using Watershed's Open CEDA (Comprehensive Environmental Data Archive), a free global emissions database covering 148 countries and 400 industries. Students will learn to estimate product carbon footprints by analyzing components, manufacturing locations, and supply chains. Potential directions include: 1) Comparing carbon footprints of similar products (electric vs. gas cars, local vs. imported foods, different smartphone brands), 2) Building a calculator or interactive tool for estimating purchase impacts, 3) Analyzing how manufacturing location affects emissions—the same product can have up to 70% higher emissions depending on the country due to different energy grids, or 4) Identifying "carbon hotspots" in supply chains where the biggest reductions could happen. Students will develop skills in lifecycle assessment, supply chain analysis, and understanding Scope 3 emissions (indirect emissions that make up the majority of most companies' carbon footprints). This project combines environmental science, economics, and data science while addressing real-world questions about sustainable consumption. The technical level ranges from beginner (spreadsheets) to advanced (Python with supply chain modeling). Likely outcome is a beginner to advanced research paper, potentially with an interactive tool useful for real consumers.
AI/ML, Environmental Science
Satellite Imagery Analysis: Finding Patterns from Space
Satellite imagery has become freely accessible through platforms like Google Earth Engine, opening up exciting possibilities for high school students to explore our planet from space. In this project, students can apply computer vision and machine learning techniques to detect and classify objects in satellite images. Google Earth Engine provides a cloud-based platform with access to decades of satellite imagery and allows for planetary-scale analysis without requiring powerful local computers. Potential project directions include: 1) Tracking deforestation by comparing satellite images over time using vegetation indices like NDVI (Normalized Difference Vegetation Index), 2) Detecting buildings, roads, or infrastructure in urban areas using object detection models, 3) Finding sports fields, solar panels, or agricultural areas using classification techniques, 4) Monitoring changes in water bodies or coastal erosion over time, or 5) Analyzing urban expansion and land use changes in your hometown. Students will learn to work with multispectral imagery, apply spectral indices, use machine learning for image classification, and create compelling visualizations. The project can range from beginner-friendly approaches using pre-built tools to advanced implementations involving training custom deep learning models. Many tutorials and datasets are freely available, making this an accessible entry point into geospatial data science. No prior remote sensing experience needed—just curiosity about seeing Earth from a new perspective! Likely outcome is a beginner to advanced research paper that can include interactive visualizations or web applications.
AI/ML, Environmental Science
Understanding AI's Environmental Footprint: Data Centers, Energy, and Sustainability
As AI systems like ChatGPT, Google's Gemini, and other large language models become ubiquitous, their environmental impact is coming under increasing scrutiny. Training and running these models requires massive data centers that consume enormous amounts of electricity and water for cooling. In this project, students will investigate the environmental costs of AI by analyzing data on data center energy consumption, water usage, and carbon emissions. We can explore questions like: How much energy does it take to train GPT-4 versus smaller models? What are the trade-offs between model performance and environmental impact? How do different regions' energy grids (renewable vs. fossil fuel-based) affect AI's carbon footprint? Students will learn to find and analyze real-world data on tech infrastructure, create compelling visualizations, and think critically about the sustainability of emerging technologies. This project is perfect for students interested in both AI and environmental issues, and can lead to policy recommendations or technical analyses. No prior AI experience needed—just curiosity! Likely outcome is a beginner to advanced research paper.
AI/ML, Environmental Science
Renewable Energy Optimization and Forecasting
The transition to renewable energy is one of the most important challenges of our time, but solar and wind power come with unique complications—they're intermittent and weather-dependent. In this project, students can explore questions around optimizing renewable energy systems. Potential directions include: 1) Using machine learning to forecast solar or wind energy production based on weather data, 2) Analyzing the economics of renewable energy adoption in different regions, 3) Modeling optimal battery storage strategies to handle renewable energy variability, 4) Investigating how geographic placement of wind farms or solar arrays affects efficiency, or 5) Comparing the lifecycle carbon footprint of different renewable technologies. Students will gain experience with time series forecasting, optimization algorithms, or economic modeling depending on their interests. This project offers flexibility to emphasize either technical data science skills or policy/economic analysis. Likely outcome is an advanced research paper that can be submitted to high school research journals.
AI/ML, Environmental Science
Scientific Review Paper
In this project, you will conduct a scientific review on the topic of your choice. Topics could range from "the relationship between long-term antihistamine usage and dementia" and "the history of environmental justice in the United States". Not only will you gain in-depth knowledge of your topic, but you will also learn how to sort their resources, identify appropriate peer-reviewed and grey literature sources, and complete proper citations.
Public Health, Environmental Science, Quantitative
Engineering Physics — Building a DIY Heat Engine Model
Field of Expertise: Thermodynamics and mechanical systems. What Students Will Learn: How engines convert heat into motion and how efficiency is measured. Process: Students research different types of engines (Stirling, steam, internal combustion) and build or model a small-scale heat engine using simple materials or simulations. Student Outcomes: A prototype or simulation with an accompanying technical explanation of how energy is transformed.
AI/ML, Math, Physics
Mechanical Systems — Smarter Energy Storage
Field of Expertise: Renewable energy and mechanical design. What Students Will Learn: The principles of energy conversion and storage (mechanical, thermal, or electrical). Process: Students research how batteries, flywheels, or compressed air systems store energy and propose an innovative or improved concept for small-scale renewable storage. Student Outcomes: A technical concept report, sketch, or 3D model illustrating their proposed solution.
AI/ML, Math, Physics
Mathematics & AI — Predicting the Future with Data
Field of Expertise: Mathematical modeling and machine learning. What Students Will Learn: How data patterns can be used to make predictions. Process: Students collect simple datasets (like local temperature trends, sports stats, or school performance data) and use basic regression or online AI tools to make short-term predictions. They analyze uncertainty and data limitations. Student Outcomes: A research paper or visual dashboard explaining their model, predictions, and insights.
AI/ML, Math, Physics
Harnessing Big Ideas: Teaching Through Storytelling
How can a complicated idea, like a new science discovery, a moment in history, or an important social value, become easy to understand? In this project, you’ll take on the role of storyteller and designer, writing and illustrating a children’s book that both educates and entertains. You’ll practice the craft of storytelling, visual communication, and simplifying complex concepts for the audience of your choosing. By the end, you’ll have a polished book in your hands that highlights empathy, imagination, and your ability to make complex concepts understandable.
Neuroscience
