Specialty Area

Artificial Intelligence

Example Course Pathway

In this section, we present examples of how to package this content into meaningful course sequences across particular specialty areas. The boxes in each diagram represent discrete courses.

Course Definitions

This section contains example courses and descriptions, with the assumption that individual schools and districts may modify the offerings to meet local contexts and needs.

Content Progression

Table 3.3 shows the content progression for artificial intelligence. 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.

Foundational CS Content

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

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