Computer Science · 11th Grade · Artificial Intelligence and Ethics · Weeks 19-27

Introduction to Artificial Intelligence

Students will define AI, explore its history, and differentiate between strong and weak AI.

• Common Core State Standards
• CSTA: 3B-AP-09

About This Topic

Machine learning is a subset of artificial intelligence that allows computers to learn from data without being explicitly programmed. This topic introduces students to the concepts of supervised and unsupervised learning, as well as the role of training data and model accuracy. It aligns with CSTA standards 3B-AP-09 and 3B-DA-07, which focus on using data to train models and making predictions.

Students learn how machine learning models are used in everything from image recognition to personalized recommendations. They also explore the importance of data quality and the potential for bias in AI systems. This topic is highly engaging when students can experiment with simple machine learning tools and see how their models improve as they provide more and better data.

Key Questions

1. Explain the core concepts and goals of Artificial Intelligence.
2. Differentiate between various subfields of AI (e.g., machine learning, robotics, natural language processing).
3. Analyze the historical development of AI and its major milestones.

Learning Objectives

• Define Artificial Intelligence and explain its fundamental goals.
• Differentiate between strong AI and weak AI, providing examples for each.
• Analyze the historical timeline of AI development, identifying key milestones and figures.
• Compare and contrast major subfields of AI, including machine learning, natural language processing, and robotics.

Before You Start

Introduction to Computer Science Concepts

Why: Students need a foundational understanding of algorithms, data representation, and basic programming logic to grasp how AI systems function.

History of Computing

Why: Familiarity with early computing devices and key figures provides context for the historical development of AI.

Key Vocabulary

Artificial Intelligence (AI)	The simulation of human intelligence processes by machines, especially computer systems. These processes include learning, problem-solving, and decision-making.
Strong AI	A hypothetical type of AI that possesses consciousness and sentience, capable of performing any intellectual task that a human being can.
Weak AI	AI designed and trained for a particular task, such as virtual assistants or image recognition software. It does not possess consciousness or general intelligence.
Machine Learning	A subset of AI that enables systems to automatically learn and improve from experience without being explicitly programmed. It uses algorithms to analyze data, identify patterns, and make predictions.
Natural Language Processing (NLP)	A branch of AI focused on enabling computers to understand, interpret, and generate human language. This powers applications like chatbots and translation services.

Watch Out for These Misconceptions

Common Misconception

Machine learning models are 'smart' like humans.

What to Teach Instead

Machine learning models are mathematical tools that find patterns in data; they don't 'understand' things the way humans do. Peer-led discussions on the limitations of AI help students see the difference between pattern recognition and true intelligence.

Common Misconception

AI models are always 100% accurate.

What to Teach Instead

AI models are only as good as the data they are trained on and can make mistakes. Hands-on exercises in 'tricking' a machine learning model help students understand its limitations and the importance of testing.

Active Learning Ideas

Inquiry Circle: Training a Classifier

Groups use a simple machine learning tool (e.g., Teachable Machine) to train a model to recognize different objects or gestures. They then test their model's accuracy and discuss how to improve it.

45 min·Small Groups

Simulation Game: Supervised vs. Unsupervised Learning

Students act as 'learning algorithms.' In one scenario, they are given labeled data (supervised), and in another, they must find patterns in unlabeled data (unsupervised). They discuss the differences in their experience.

30 min·Whole Class

Think-Pair-Share: The Role of Training Data

Pairs discuss how the quality and quantity of training data affect the performance of a machine learning model. They share examples of how biased or incomplete data could lead to incorrect predictions.

20 min·Pairs

Real-World Connections

• The development of AI can be traced back to early computing pioneers like Alan Turing, whose 1950 paper 'Computing Machinery and Intelligence' proposed the Turing Test as a measure of machine intelligence.
• Companies like Google use AI in their search algorithms to understand user queries and provide relevant results, while also employing machine learning for features like Google Photos' image recognition.
• Robotics, a subfield of AI, is transforming manufacturing with automated assembly lines and is increasingly used in healthcare for precision surgery and patient care.

Assessment Ideas

Exit Ticket

On a slip of paper, ask students to write a one-sentence definition of AI in their own words. Then, have them list one example of weak AI and one hypothetical example of strong AI.

Quick Check

Present students with a list of AI-related technologies (e.g., self-driving cars, Siri, chess-playing programs, a calculator). Ask them to classify each as an example of strong AI or weak AI and briefly justify their choice.

Discussion Prompt

Facilitate a class discussion using the prompt: 'Considering the historical development of AI, what do you believe are the most significant milestones, and why? How might these milestones influence future AI advancements?'

Frequently Asked Questions

What is the difference between supervised and unsupervised learning?

Supervised learning uses labeled data to train a model to make predictions. Unsupervised learning finds patterns and relationships in unlabeled data without any prior guidance.

How can active learning help students understand machine learning?

Active learning allows students to 'see' the learning process in action. By training their own models and testing their accuracy, they can better understand how data influences the performance of an AI system.

What is 'training data' in machine learning?

Training data is the initial dataset used to teach a machine learning model how to make predictions or find patterns. The quality and diversity of this data are critical for the model's success.

Why is data quality important for AI models?

High-quality data is essential for building accurate and reliable AI models. If the training data is biased, incomplete, or inaccurate, the model's predictions will also be flawed.