CS-STEM Curriculum

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Software Foundations (through VRC)

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Software Foundations (through VRC)

VRC: Applying for your Software Foundations Micro-Certification
As a member of a VEX Robotics Competition (VRC) team, you have built up a number of valuable technical skills. Micro-Certifications, brought to you by RECF and the Carnegie Mellon Robotics Academy, let you showcase these skills as part of a college application or resume, or simply let others know what you have accomplished through VRC.

The Software Foundations Micro-Certification covers all of the concepts that were covered in Robotics Integrations as well as programming sensors, and a vision system (or camera).

The exam portion of this Micro-Certification will test your knowledge of these concepts to determine if you meet or exceed the foundational knowledge needed as a Robotics Technician.

To apply for yours:
  • (Optional) Review the lesson materials to brush up and see what is covered by the exam 
  • [REQUIRED] Upload photos of your work to your portfolio according to the prompts 
  • [REQUIRED] Obtain approval of your uploads by CMRA or a Certified Teacher**
  • [REQUIRED] Take the online exam (60 min, multiple choice, passing = 70%)**
  • **Requires purchasing Student Certification Access and have CMRA or a CMRA Certified instructor approve uploads
Process for applying for the Micro-Certification

Topics Covered

Exam Prep: Testbed (through VRC)
Exam Prep: Open-Loop Navigation (through VRC)
Exam Prep: Sensing (through VRC)
Exam Prep: Camera Navigation (through VRC)
Exam Prep: Camera Programming (through VRC)

BGC NVIDIA AI Literacy Curriculum for Teachers

View BGC NVIDIA AI Literacy Curriculum for Teachers

Take your students on a journey learning about Artificial Intelligence. Help them discover what is and is not possible for AI today, how AI works and even how they can use it in the future!

Topics Covered

Getting Started with AI Literacy
Lesson 1: Intelligence in AI (Teacher)
Lesson 2: AI: Fiction vs. Reality (Teacher)
Lesson 3: AI Data - Inputs and Outputs (Teacher)
Lesson 4: AI and Data (Teacher)
Lesson 5: Building Models for AI (Teacher)
Lesson 6: Facial Recognition (Teacher)
Lesson 7: Other Uses of AI Today (Teacher)
Lesson 8: Generative AI (Teacher)
Lesson 9: What Can Go Wrong With AI (Teacher)
Lesson 10: AI of the Future (Teacher)
Lesson 11: Careers with AI

BGC NVIDIA AI Literacy Curriculum for Students

View BGC NVIDIA AI Literacy Curriculum for Students

Embark on a journey learning about Artificial Intelligence. Discover what is and is not possible for AI today, how AI works and even how you can use it in the future!

Topics Covered

Lesson 1: Intelligence in AI (Student)
Lesson 2: AI: Fiction vs. Reality (Student)
Lesson 3: AI Data - Inputs and Outputs (Student)
Lesson 4: AI and Data (Student)
Lesson 5: Building Models for AI (Student)
Lesson 6: Facial Recognition (Student)
Lesson 7: Other Uses of AI Today (Student)
Lesson 8: Generative AI (Student)
Lesson 9: What Can Go Wrong With AI (Student)
Lesson 10: AI of the Future (Student)
Lesson 11: Careers with AI
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