Artificial Intelligence

Specialty Area

Content Progression

Artificial intelligence (AI) is a human-made system involving techniques for getting computers to do things that normally require human intelligence to do, such as making decisions and predictions (AI4GA). The foundational content for all students includes some learning outcomes related to AI. For continued learning beyond the foundation, we have defined the following content progression that includes two additional levels (fundamentals and specialty) that progressively build on this content. The AI content may require more prior mathematical knowledge than other pathways. This progression might lead to an AI major and to careers as a machine learning engineer, computer vision engineer, or AI ethics and policy analyst, among others.

Foundation

Prioritized foundational content specific to AI:

How algorithms are used
Difference between traditional and AI/ML algorithms, including the role of data in AI/ML
Patterns/commonalities in problems, data, and programs
Evaluate outputs for biases and accuracy
Societal impacts of AI (e.g., biased data, attribution)
Basic data formats and metadata
Cleaning data
Visualizing data
Impact of emerging technologies

Fundamentals

What is AI: history, levels of AI, future careers, laws
Intro to AI programming and intro to prompt engineering
AI projects
Natural interaction, semantics, chatbots
Representation and reasoning, k-nearest neighbors (KNN), vectors
AI programming (projects), using AI tools to solve problems
Ethical frameworks, philosophy, psychology, bias
Sensors, perception, classification
Using datasets, regression, probabilistic thinking
Convolutional neural network (CNN), decision trees, bias
Ethical design and empathy interviews

Specialty

Fundamentals of electronics, mechanisms, circuits, gears, sensors
Computer vision, sensor applications, models, perceptions
Robot hardware manipulation (or software simulators)
Using data: collection, cleaning, data types, validity, bias
ML models: optimization, accuracy, decision-making, ethical considerations
Linear algebra, matrices, vectors, probability, statistics
Programming applications with math
Biases in data collection, analysis, and reporting
Preparation for industry certification

Example Course Pathway

The artificial intelligence content progression can be packaged in a variety of ways to meet the local context and needs of individual schools and districts. This artificial intelligence course pathway serves as an example of how content in this specialty can be implemented in high schools. Each box represents a course and can be expanded to view a corresponding description.

Foundation

see below

Computer Science Foundations

Computer Science Foundations supports all high school students, regardless of postsecondary goals, in developing the knowledge, skills, and dispositions necessary to navigate and understand the technology-driven world in which they live. Course content, organized into five Topic Areas (Algorithms, Programming, Data and Analysis, Computing Systems and Security, and Preparing for the Future), rests upon four Key Pillars (Computational Thinking, Inclusive Collaboration, Human-Centered Design, and Impacts and Ethics). Topic Areas and Pillars are essential components of this course and the student experience (see Section 2 of this report for more details).

Fundamentals

see below

Programming the Future

Programming the Future provides students who have a foundational understanding of computer science with an opportunity to explore various topics such as cybersecurity, artificial intelligence, and data science. While developing their programming skills, students will apply fundamental ideas in these areas to solve meaningful and interesting problems. Content covered in this course aligns with fundamentals content from the Programming, Cybersecurity, Artificial Intelligence, and Data Science content progressions as defined in Sections 3.1, 3.2, 3.3, and 3.5.

Specialty

see below

AI and ML Programming

AI and ML Programming is intended to follow foundational programming and introductory AI learning experiences. Students will build upon this prerequisite knowledge to leverage AI in practical and innovative applications as well as to interrogate when opportunities to use AI may be unsafe or unreliable. This course includes a significant emphasis on data and needs to be paired with appropriate math learning. Content covered in this course aligns with specialty content from the Artificial Intelligence content progression as defined in Section 3.3.

Advanced Application

see below

Artificial Intelligence Capstone

The Pathway Capstone Course is an opportunity for students to apply advanced computer science knowledge and problem-solving, communication, and collaboration skills to tackle a personally meaningful computing project. Students will design innovative solutions and present them to authentic audiences, preparing them for future academic and professional pursuits. This course is designed to inspire creativity, foster collaboration, and demonstrate proficiency in real-world application of the knowledge, skills, and dispositions developed during prior coursework and experiences.

View the Implementation and Integrating CS pages to learn more about how to teach foundational and specialty content to students.

Possible Careers:

Machine Learning Engineer, Data Scientist, AI Research Scientist, Computer Vision Engineer, Natural Language Processing Engineer, Robotics Engineer, AI Ethics and Policy Analyst, Autonomous Vehicle Engineer, AI Cybersecurity Engineer

Specialty Area