Before You Begin

Essential Questions: 

• How do robots communicate data to users? 

• What factors affect vision, and why?

Unit Understandings:

• The scientific method can be used to explore robot features.
• AI Vision data can change based on the robot's environment.

Standards Alignment

Computer Science Teachers Association (CSTA)

• 1B-CS-02: Model how computer hardware and software work together as a system to accomplish tasks.
• 1B-DA-06: Organize and present collected data visually to highlight relationships and support a claim.
• 1B-DA-07: Use data to highlight or propose cause-and-effect relationships, predict outcomes, or communicate an idea.
• 1B-AP-12: Modify, remix, or incorporate portions of an existing program into one's own work, to develop something new or add more advanced features.
• 2-DA-08: Collect data using computational tools and transform the data to make it more useful and reliable.
• 3B-DA-07: Evaluate the ability of models and simulations to test and support the refinement of hypotheses.
• 3B-AP-09: Implement an artificial intelligence algorithm to play a game against a human opponent or solve a problem.

View additional standards from ISTE, Common Core, NGSS, and other standards bodies as well as descriptions of how and where each of these standards are met in this unit here.

Materials Needed (per group):

• VEX AIM Coding Robot
• One Stick Controller
• 1 orange barrel
• 1 blue barrel
• 1 sports ball
• AprilTag ID 0
• AIM Field (4 tiles and 8 walls)
• Journal
• Assorted classroom materials for explorations (see below)

Students will be exploring different properties of AI Vision throughout the unit. For these explorations, students will need additional classroom materials. Specifics about the materials needed for each exploration are provided in the teacher notes on the following page.

Need additional help on incorporating explorations in your classroom? PD+ All Access members can book a 1-on-1 session to brainstorm with a VEX expert. 

Suggested Time for this Unit: 7-12 Sessions

While pacing will vary classroom to classroom, suggested timing can help you plan effectively. A ‘session’ is considered approximately 45-50 minutes. You know your students best, so adjust timing as needed to best meet the needs of your students in your setting.

• Introduction: 1 session
• Exploring AI Vision: 1-2 sessions per exploration (Total time: 4-8 sessions for all 4 explorations)
• Putting It All Together: 2-3 sessions

After students have watched the video, follow the established procedure to facilitate a whole-class discussion to elicit students' observations and prepare them for co-creating learning targets for the unit. 

Next, help students make a real-world connection to the content in this unit and engage prior knowledge using the following prompt: 

Let's think back on the everyday sensors we discussed in the last unit. How do these sensors report data back to users? How do things in the sensor's environment affect the way sensors report data? Use some of the examples below to help students get started: 

• Automatic garage doors stop closing if they detect something in the way—but they can miss fast movement, like someone running under the door, or be blocked by something like a fallen branch.
• Phones that automatically adjust the screen brightness may assume it is dark all around if you open the screen in a pocket or if you huddle over the phone away from the light.
• Cars that use backup cameras to help avoid obstacles frequently will beep before the driver is in any immediate danger of hitting something else. If the camera is covered with rain or ice, this functionality can be impacted.

Guide students as a whole class through the process of co-creating learning targets. 

1. Brainstorm with students what they will need to know to complete the activities shown in the video above. Frame these as “I can” statements.
   1. Example “I can” statements for this unit include:
      1. I can identify different factors that affect AI Vision and why.
      2. I can follow scientific method to make a hypothesis, test, and draw a conclusion from data.
2. Co-create learning targets based on that list.

For more guidance on co-creating learning targets with your students, see this VEX Library Article. Then, go further and learn more about co-creating learning targets with this lesson from a VEX PD+ Masterclass.