Recently Added Curriculum

Students collect data from a sugar-level simulation by categorizing different food and drink solutions and measuring their impact on glucose levels. They then use this data to train a machine learning model using the "Machine Learning for Kids" platform. By inputting and organizing their data, students train the model to predict blood sugar responses and classify meals as either healthy or unhealthy. They test their model’s accuracy with new inputs and make adjustments to improve its performance. Through this hands-on process, students gain an understanding of how machine learning works, the importance of high-quality data, and how these technologies can support real-world health applications, such as managing diabetes.

Students investigate nitinol (nickel-titanium alloy), a shape memory alloy known for its ability to return to a pre-set shape when heated and its super elastic properties. Through hands-on exploration, they examine how phase transformations between martensite and austenite influence nitinol’s behavior and discuss its applications in fields such as biomedicine, robotics, and aerospace. By connecting microscopic atomic structures to macroscopic material properties, students develop a deeper understanding of how engineers design advanced materials to solve real-world problems.

Students explore the concept of dragging friction and participate in an activity to understand the cultural significance of the minga tradition. They then apply this knowledge to design a method for transporting materials up a hill. Their solution must reduce friction without altering the inclined plane, incorporate principles of the minga tradition, and utilize discarded materials commonly found in the area.

Students explore how animals and humans process sensory information and respond by conducting a reaction time experiment of dropping a ruler to see how quickly they can catch it. They then build a model using various materials to represent how their brain and body work during the experiment. Additionally, students learn the importance of brain protection by using the engineering design process to design and construct their own bicycle helmet using materials such as cardboard, egg cartons, and bubble wrap.

With a design-thinking approach, students incorporate neuroscience into their robotics learning experiences in this activity. They perform an experiment to design a basic human-robot interface through which electromyography (EMG) signal readings from the muscle movements in their arm are translated to simple movements in their robots. Students brainstorm factors they believe will vary between arm movements, and use these factors to develop a data processing program for the EMG data collected. In doing so, students form an understanding of the considerations that are involved in designing, building, and evaluating a human-machine interface.

Students dive deeper into fear conditioning by exploring the neural pathways involved in tone and shock responses. They review the basics of synapses and neural pathways before using a virtual lab simulation to connect tone and shock pathways in the amygdala, aiming to create a circuit that results in fear conditioning. Throughout the process, students experiment with different firing rates and configurations, troubleshooting through trial and error to find the correct values that activate the neurons. The task culminates in a more advanced exploration of calcium ion plasticity, enhancing students' understanding of how fear conditioning works at multiple levels in the brain.

The brain is a complex computer with its own hardware and ‘software.’ Students are introduced to the brain’s function as an electrical circuit by exploring the similarities between neurons and electrical circuits. Students first learn about neurons, their structure, and how they transmit signals using electrical impulses, much like wires in a circuit. Through interactive activities, they build and analyze simple electrical circuits, drawing parallels to neural pathways in the brain. By understanding how the brain processes information and learns fear, students connect neuroscience concepts to real-world applications such as neuroplasticity, brain-machine interfaces, and biomedical engineering.

Students build on their understanding of how the brain uses circuits to respond to external stimuli, learning about Pavlovian conditioning through the lens of neural circuits. By exploring Pavlov’s dog experiment, students connect their knowledge of neurons and neural pathways to understand how animals, including humans, learn through association. The lesson emphasizes the concept of learning and synaptic plasticity, which are key to understanding how neural circuits control behavior. Students engage in hands-on activities, such as drawing circuits, discussing the Pavlov experiment, and using tools such as Google Colab to explore fear learning and the role of the amygdala. With the help of videos and group discussions, they examine the neural pathways involved in both reward and fear conditioning.

Humans (and all animals) have the fascinating ability to learn and adapt their bodily movements and thoughts as they navigate their lives in a complex world. This three-activity unit introduces how engineers and scientists study the ability of our brain to learn in general, using the classic paradigm of Pavlovian learning where a dog can be subconsciously taught to associate a bell tone with a food reward after an initial training period. Or a rodent can be taught to learn to fear a tone after a training period where the tone is paired with a foot-shock.

Students engage in hands-on activities that provide an opportunity to learn about biological and environmental engineering. Students learn about perfluoroalkyl and polyfluoroalkyl substances (PFAS), and how to remove them from drinking water. Students are given water from a local water source and must create a filtration system. The students will then take the filtered water and attempt to purify that water.

Students use the engineering design process to engage in a hands-on investigation of how atmospheric conditions impact learning while connecting their findings to real-world sustainability goals. By constructing and using an Arduino air quality monitor, students collect and analyze data on factors such as temperature, humidity, carbon dioxide levels, and air particulates. Through this process, they explore how these environmental properties influence cognitive function, concentration, and overall well-being. Students then interpret their data, draw evidence-based conclusions, and relate their findings to the United Nations Sustainable Development Goals (SDGs), particularly those related to health, education, and sustainable cities. Finally, they apply their knowledge by proposing actionable improvements to optimize classroom air quality, fostering healthier, more effective learning environments.

Students use the engineering design process as they work in groups to research one of five genetic disorders and learn about CRISPR-Cas9 using a paper model and an online interactive tool. They adapt the paper model to simulate how CRISPR-Cas9 could potentially cure their assigned disorder. Using their research and models, they create a pitch for research funding in the form of a trifold poster. Finally, the entire class debates and discusses which disease should receive the most funding to develop a CRISPR-based cure, considering humanity’s need for a cure (number of cases, disease severity, availability of other treatments, etc.) and the feasibility of targeting their disease with CRISPR.

Students discover how electric and magnetic fields exert forces on objects by using the engineering design process to design and build a small model maglev train. Students add weight, such as pennies, to test how much their model can hold. Through this hands-on activity, students gain a deeper understanding of magnetic repulsion and attraction. They identify and describe contact and non-contact forces by investigating the properties of magnets and their interactions.

Students use Microsoft Visual Studio Code and GitHub Copilot to master writing functions in Python, actively assessing the effectiveness of these tools to define future success criteria for engineers developing similar technologies. Through hands-on coding activities, they explore the significance of functions, enhancing code readability and enabling more innovative programming approaches. Additionally, students examine the impact of these technologies in programming, offering valuable suggestions to refine and advance engineering tools.

Students learn the basic principles of the humoral immune response and use a model to understand how antibodies and antigens interact in an enzyme-linked immunosorbent assay (ELISA). The ELISA is a laboratory diagnostic tool used to identify and quantify antigens. Through this model, students discover how the interactions between antigens and antibodies can aid in disease diagnosis and provide valuable information for disease management.