Grades 9-12

Unit 1 - Problem Solving with AI

Unit 2 - Foundations of AI Programming

Unit 3 - AI and the Systems That Power It

Unit 4 - The Fabric of the Internet and AI

Unit 5 - Cybersecurity and Global Impacts

Unit 6 - Insights from Data and AI

Investigate how AI generates responses, using neural networks, transformers, and probabilistic models.

Critically evaluate AI-generated outputs, identifying bias, misinformation, and hallucinations.

Refine AI-generated content through prompt engineering and debugging techniques.

Explore AI's role in creativity, forecasting, and decision-making, balancing its strengths with ethical considerations.

Design and test AI-assisted solutions to real-world challenges, ensuring that AI is used responsibly and with human oversight.

Define and explain the purpose of algorithms and their applications.

Design algorithms using sequence, selection, and iteration.

Analyze the functionality of algorithms and propose improvements.

Write Python programs using variables, conditional statements, loops, and functions.

Debug and test Python programs systematically to ensure correctness and reliability.

Use modular design principles to write reusable functions with parameters and return values.

Use AI tools to support problem-solving, algorithm design, and debugging, while recognizing their limitations and biases

Evaluate the ethical implications of using AI in programming and implement strategies for ethical and fair implementation.

Identify career pathways in computing and connect programming skills to real-world opportunities.

Use computational thinking principles to design, analyze, and troubleshoot programs.

Apply abstraction, decomposition, and pattern recognition to solve problems effectively.

Explain how computational systems process data and how hardware, software, and operating systems collaborate to perform tasks.

Investigate and optimize computing systems to improve performance and achieve specific goals.

Design computing solutions that integrate hardware and software to collect and exchange data.

Apply human-centered design principles to create effective and inclusive systems or protocols.

Analyze the societal and ethical implications of AI and emerging technologies, including issues related to privacy, fairness, and accessibility.

Assess the benefits and risks of AI in various industries and real-world applications.

Use AI tools to investigate problems, generate solutions, and evaluate the accuracy, fairness, and reliability of outputs.

Apply AI to analyze data, improve workflows, and develop creative solutions.

Describe the role of data in AI applications and how different types of data (e.g., audio, visual, environmental) are collected and used computationally.

Engage in inclusive collaboration by valuing diverse perspectives and addressing ethical concerns in computing projects.

Identify career pathways in computing and describe how AI and emerging technologies are transforming industries and disciplines.

Explore the interdisciplinary applications of computing concepts in solving real-world problems.

Describe how data is transmitted, routed, and reassembled over the Internet.

Explain the roles of IP addressing, DNS, and redundancy in ensuring efficient communication.

Analyze the role of AI in enhancing network security.

Assess the implications of the digital divide and propose strategies to promote equitable Internet access.

Evaluate the ethical considerations of AI in network security and propose responsible solutions.

Identify and describe roles in networking and cybersecurity, such as Network Engineers and AI Cybersecurity Specialists.

Analyze how these careers contribute to addressing societal challenges and improving technology.

Use abstraction, decomposition, and pattern recognition to solve problems related to Internet communication and security.

Propose and evaluate solutions to complex networking challenges, including security risks and accessibility issues.

Understand the foundations of data science and its interdisciplinary nature.

Create and refine data questions to guide investigations.

Analyze datasets and apply various analysis techniques.

Develop visualizations to help answer data questions.

Use analysis results to tell a compelling data story.

Consider ethical concerns when creating data questions, analyzing data, and telling a data story.