WPAFB STEM Outreach

In this lesson, students will pair up to explore face-sensing AI using Scratch’s drag-and-drop coding system. They will work through interactive activities in their packets, which include a variety of hands-on tasks and an optional extension activity for deeper learning. This lesson introduces or reinforces drag-and-drop coding concepts while sparking discussion about the positive and negative implications of face-sensing AI.

Possible Time Needed to Complete:
Approximately 90 minutes, depending on student abilities and pacing.

Grade Levels:
6-10

NGSS Standards for High School:

• HS-ETS1-4: Use a computer simulation to model the impact of a proposed solution on people, society, and the natural world.

• HS-ETS1-3: Evaluate a solution to a complex real-world problem based on prioritized criteria and trade-offs that account for a range of constraints, including cost, safety, reliability, and aesthetics.

• HS-LS1-3 (Optional Extension): Plan and conduct an investigation to provide evidence that feedback mechanisms maintain homeostasis. (This can be applied when comparing the human nervous system's feedback loops to AI systems processing facial inputs in real time.)

NGSS Standards for Middle School:

• MS-ETS1-1: Define the criteria and constraints of a design problem with sufficient precision to ensure a successful solution.

• MS-ETS1-2: Evaluate competing design solutions using a systematic process to determine how well they meet the criteria and constraints of the problem

• MS-LS1-8: Gather and synthesize information that sensory receptors respond to stimuli by sending messages to the brain for immediate behavior or storage as memories.

Critical Technology Connections:

• Artificial Intelligence (AI) Students will be introduced to the fundamentals of machine learning and facial recognition, which are core aspects of AI. They will learn how algorithms are used to recognize patterns in images, such as identifying faces, laying the groundwork for more complex AI applications in real-world systems like security or autonomous vehicles.

• Semiconductors and Microelectronics In real-world applications, face sensing AI systems rely on microchips to process large volumes of data and perform tasks like face detection in real-time. These chips are crucial for powering devices that enable face recognition in smartphones, security cameras, and other smart devices.

• Future Computing Technologies As students explore machine learning for face recognition, they will understand how future computing innovations, like cloud computing and edge devices, will accelerate AI performance. These technologies enable faster, more efficient data processing, which is critical for real-time applications like face detection and recognition.