Course Title:
AI Technology Honors
UC Subject Category: Interdisciplinary (G)
Delivery Mode: Online & Classroom-Based
Grade Levels: 10–12
Credits: 10 UC-Approved High School Credits
Prerequisites: Algebra I and basic computer literacy (Python recommended but not required)
Course Description:
AI Technology Honors introduces students to the foundations, applications, and implications of artificial intelligence in modern society. Through a blend of technical instruction and project-based learning, students explore core AI concepts such as machine learning, data analysis, neural networks, computer vision, and natural language processing. Students learn how AI technologies are applied across industries—from self-driving cars to personalized recommendation engines—and critically evaluate the ethical, social, and economic impacts of AI systems.
Using hands-on tools and platforms, students experiment with datasets, train simple models, and collaborate on real-world inspired projects. The course balances computational thinking and coding practice with interdisciplinary inquiry, preparing students for advanced studies and careers in AI, data science, and related fields.
This course is aligned with UC A–G standards, College Board recommendations, and the educational competencies expected of 21st-century learners.
Syllabus Overview: AI Technology Honors
| Unit | Topics Covered | Key Activities & Assessments |
|---|---|---|
| Unit 1: Introduction to Artificial Intelligence | What is AI? History and types of AI. Rule-based systems vs. machine learning. | Group discussion on AI in everyday life; reading reflection; quiz |
| Unit 2: Data & Algorithms | Data structures, algorithmic logic, data preprocessing, bias in datasets | Python data manipulation mini-lab; ethics case study; quiz |
| Unit 3: Supervised & Unsupervised Learning | Classification, regression, clustering, model training & testing | Build a simple classifier with Scikit-learn or Teachable Machine |
| Unit 4: Neural Networks & Deep Learning | Artificial neurons, multilayer networks, deep learning overview | Visualize neural networks; project: handwriting recognition |
| Unit 5: Natural Language Processing (NLP) | Tokenization, sentiment analysis, chatbots, language models | NLP mini-project using text datasets; reflective journal |
| Unit 6: Computer Vision & Image Recognition | Pixels, convolutional neural networks, facial recognition | Image classification lab using TensorFlow or ScratchAI |
| Unit 7: Ethics, Equity & AI in Society | Surveillance, job automation, AI fairness, misinformation | Research paper or debate on an AI ethics issue |
| Unit 8: Capstone Project | Collaborative or individual final project demonstrating understanding of AI technologies | Final presentation + written report; peer review; instructor evaluation |
Course Materials & Tools
- Online learning platform: Canvas LMS
- Programming environment: Google Colab, Jupyter Notebook
- Tools: Teachable Machine, ScratchAI, TensorFlow Lite
- Reading materials: Instructor-curated articles and excerpts
Grading Breakdown
- Class participation and discussions – 10%
- Quizzes and short assignments – 15%
- Labs and coding exercises – 25%
- Unit projects – 25%
- Final Capstone Project – 25%
Student Outcomes
By the end of the course, students will be able to:
- Understand foundational AI concepts and terminology
- Apply basic machine learning techniques to real-world problems
- Evaluate the ethical implications of AI systems
- Communicate their findings through written, visual, and oral presentations
- Work collaboratively in solving interdisciplinary problems using AI tools