The First High School AI Literacy Course in New York State Begins This January—At Geneva High School!

Course Description

This one-semester course offers an introduction to the fundamental concepts, techniques, and ethical considerations in artificial intelligence (AI). It covers the history of AI, key breakthroughs in machine learning, neural networks, natural language processing, and computer vision, with an emphasis on real-world applications and societal impacts. Through a blend of theoretical study and hands-on projects, students will gain a solid understanding of how AI works and how it is transforming industries like healthcare, finance, and technology. The course will also explore the ethical dilemmas posed by AI and discuss the future implications of this rapidly evolving field.

Rationale

Artificial Intelligence (AI) is rapidly transforming every aspect of modern life, from healthcare and finance to transportation, entertainment, and communication. As AI continues to advance at unprecedented speeds, the ability to understand, critically engage with, and effectively harness these technologies is no longer optional—it's essential. Artificial Intelligence: From Basics to Breakthroughs is designed to equip students with the knowledge and skills necessary to navigate a future shaped by AI, preparing them not only as informed digital citizens but as innovators and ethical leaders in a technologically driven world.

At its core, AI represents one of the greatest scientific and societal revolutions in history, akin to the Industrial Revolution and the advent of the internet. Yet, AI is not just a technological tool; it is reshaping how we think about intelligence, creativity, ethics, and our collective future. To meet this challenge, students must learn to go beyond using AI as a black-box technology. They must understand how AI works, its potential and limitations, and the ethical questions it raises.

This course will provide a comprehensive foundation in AI concepts, from the basics of algorithms and machine learning to advanced topics like neural networks and deep learning. Students will explore real-world applications, analyzing how AI is already revolutionizing industries such as medicine, finance, and transportation. They will also grapple with the profound ethical dilemmas posed by AI—issues like algorithmic bias, data privacy, and the future of human labor.

Perhaps most importantly, this course offers students the chance to explore the future of AI: what it could mean for society, how it could reshape industries, and the ways in which they themselves might contribute to shaping its development. In an era where AI fluency will be as important as digital literacy, this course is an essential step toward ensuring that our students are not just passive consumers of technology but active and thoughtful contributors to its evolution.

Understanding AI is not just about gaining technical skills; it’s about comprehending the profound changes in the world we live in and helping to shape a future that reflects our shared human values.

I hope you are ready to embark on this journey together.

Course Objectives

1. Understand foundational AI concepts: Learn the key principles behind machine learning, neural networks, and deep learning.

2. Explore the history of AI: Gain insight into the evolution of AI from its origins to the modern day.

3. Analyze AI algorithms: Understand basic AI algorithms like decision trees, k-nearest neighbors, and neural networks, and their applications.

4. Apply AI in practical scenarios: Utilize AI models in problem-solving, including areas like natural language processing, computer vision, and recommendation systems.

5. Evaluate the ethical implications of AI: Examine the societal, moral, and ethical considerations of AI use in various sectors.

6. Understand the future of AI: Predict trends and future breakthroughs in AI, considering their potential impact on society, business, and policy.

Texts

1. Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell

2. Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

3. Superintelligence: Paths, Dangers, Strategies by Nick Bostrom

4. You Look Like a Thing and I Love You: How AI Works and Why It’s Making the World a Weirder Place by Janelle Shane

5. Selected academic papers and case studies on machine learning, AI ethics, and applications in industries.

Units of Study

Unit 1: Introduction to AI and its History

1. Define artificial intelligence (AI) and explain its significance in contemporary technology.

2. Identify key historical milestones and breakthroughs in the development of AI.

3. Describe the contributions of major AI pioneers such as Alan Turing and John McCarthy.

4. Differentiate between AI’s subfields, including machine learning, neural networks, and robotics.

5. Analyze the impact of early AI systems, such as Deep Blue and AlphaGo, on public perception of AI.

Unit 2: The Building Blocks of AI – Algorithms and Machine Learning

1. Explain the basic principles of machine learning, including supervised, unsupervised, and reinforcement learning.

2. Identify and describe key machine learning algorithms, including decision trees, k-nearest neighbors, and linear regression.

3. Demonstrate how to apply simple machine learning algorithms to classify and predict data.

4. Understand the structure and function of neural networks and the concept of backpropagation.

5. Develop a basic AI classification model using machine learning tools.

Unit 3: Neural Networks and Deep Learning

1. Understand the architecture of neural networks, including layers, neurons, and activation functions.

2. Explain how deep learning works and differentiate between CNNs (Convolutional Neural Networks) and RNNs (Recurrent Neural Networks).

3. Analyze real-world applications of deep learning, including image recognition and natural language processing.

4. Explore the key breakthroughs in AI that have been driven by deep learning techniques.

5. Develop and evaluate a simple deep learning model for image recognition using appropriate tools and libraries.

Unit 4: AI Applications in the Real World 

1. Identify and describe AI applications in various industries, including healthcare, finance, transportation, and entertainment.

2. Understand the role of AI in healthcare, particularly in diagnostics, treatment planning, and drug discovery.

3. Explain how AI is used in finance for fraud detection, algorithmic trading, and customer service.

4. Analyze the impact of AI in autonomous systems like self-driving cars and drones.

5. Evaluate the role of AI in recommendation systems and its influence on user behavior in media platforms.

Unit 5: The Ethics of AI 

1. Understand the ethical challenges posed by AI, including bias, privacy issues, and accountability.

2. Explore the ethical implications of AI in decision-making processes, including the potential for bias in algorithms.

3. Analyze the societal impact of AI on employment, discussing job displacement and the future of work.

4. Debate the ethical boundaries of AI surveillance and data collection in both public and private sectors.

5. Evaluate existing ethical frameworks and propose methods for responsible AI governance and regulation.

Unit 6: The Future of AI: Trends and Breakthroughs 

1. Identify emerging trends and advancements in AI, including quantum computing and edge AI.

2. Discuss the potential future roles of AI in shaping global industries, societies, and economies.

3. Explore the concept of Artificial General Intelligence (AGI) and superintelligence, evaluating both possibilities and risks.

4. Analyze the possible long-term societal impacts of advanced AI technologies, including ethical, economic, and political consequences.

5. Develop a project that predicts or theorizes future AI applications or impacts in a chosen field, incorporating knowledge from throughout the course.

Types of Assignments

1. The AI Chronicles – Newsletter Contributions (Ongoing)

Students will take turns writing short, public-facing pieces for The AI Chronicles, our District’s AI newsletter. Each contribution should translate complex AI concepts into accessible language and may take the form of an explainer, a profile of an AI system, or an opinion ed-itorial. This cultivates communication skills and demonstrates how AI intersects with everyday life.

2. Algorithmic Bias Case Study (Early Semester)

Students will investigate a real-world AI system (e.g., predictive policing, hiring algorithms, healthcare diagnostics). They will explain its functioning, identify potential ethical pitfalls (bias, fairness, privacy), and propose remedies. In class, they will role-play different stake-holders to debate solutions.

3. AI and the Future of Work – Visionary White Paper (Mid-Semester)

Students will select an industry of interest and forecast how AI might transform it over the next 20 years. Deliverables include a written white paper and an oral presentation framed as advice to a “District AI Task Force.” This project encourages students to think critically about innovation, disruption, and long-term societal implications.

4. AI Meets the Humanities: Remix Assignment (Mid-Semester)

Using an AI tool, students will generate a creative artifact (text, image, music, or multimedia). They will then write a critical reflection analyzing the process: How did the AI work? Where did it succeed or fail? What does this reveal about authorship, creativity, and human-machine collaboration?

5. The Ethics Hackathon (Late Semester)

Working in teams, students will design ethical guidelines for an emerging AI application (e.g., AI companions, autonomous drones, medical decision-making). At the end, they will pitch their frame-works to the class and community guests, who will evaluate the most compelling and responsible solutions.

6. AI Autobiography (Final Reflection)

For the final assignment, students will write an “autobiography” from the perspective of an AI system. This creative and reflective piece should combine technical explanation (how the system functions) with philosophical exploration (what it might mean to “exist” as AI). This assignment invites students to synthesize technical, ethical, and humanistic insights from the course.