Module 1 Overview: What is GenAI?
📌 About Module 1
🔎 Module 1 Learning Objectives
By the end of this module students will:
- Learn a basic history and evolution of AI;
- Learn the basics of how AI works including large language models, neural networks, deep learning, AI training, and algorithm
The module is designed to address the following elements based on the credit and contact hour limitations of the relevant course:
| Essential | What AI is, Key concepts in AI |
| Recommended | Comparison of Platforms, AI in Your Discipline |
| Optional | History of AI |
🗂️ Module Components
This module contains:
📌About Module 1
- Overview
- Learning Objectives
- Module Components
- Key Concepts
🧭 Lesson Plans for Module 1
- 60 minute lesson plan
- 90 minute lesson plan
- Studio Hour lesson (60 mins)
📖 Teaching Resources for Module 1
- Essential Resources
- Recommended Resources
- Optional Resources
🎲 Student Activities
Instructors are encouraged to review the available materials and use their discretion to remix and integrate elements of the module as suited to their unique pedagogical goals and circumstances.
💡 Key Concepts
This is a ready-to-use list. This language has been written as student-facing so you can copy and paste it directly into Brightspace or your instructional slides. In addition, we also share links to additional resources if you would like to add to this list.
AI Algorithm
An algorithm is defined as a set of instructions to be followed in calculations or other operations. An AI algorithm takes in training data and uses that information to learn and grow. It then completes tasks using the training data as a basis.
Artificial General Intelligence (AGI)
A hypothetical type of AI that could understand, learn, and apply knowledge across any task—just like a human can. Unlike today’s AI (which is good at specific tasks like writing or image recognition), AGI would be able to do anything a human mind can do. We don’t have AGI yet; it’s still a goal researchers are working toward.
Deductive Reasoning
Drawing specific conclusions from general rules or principles. Think of it like a math proof: if you know “all mammals have lungs” and “dogs are mammals,” you can deduce that “dogs have lungs.” You start with what you know is true and work down to a specific answer.
Generalizations
Broad conclusions drawn from specific examples or patterns. For instance, if you meet three friendly cats, you might generalize that “cats are friendly”—even though this might not be true for all cats. Generalizations help us make sense of the world quickly, but they can sometimes be inaccurate.
Hallucinations
When AI generates information that sounds confident and plausible but is actually false or made up. For example, an AI might confidently cite a research study that doesn’t exist or describe events that never happened. The AI isn’t “lying”—it’s making mistakes in how it predicts what information should come next.
Inductive Reasoning
Drawing general conclusions from specific observations or examples. The opposite of deductive reasoning: you start with specific cases and work up to a broader pattern. For example, noticing that the sun has risen every day of your life and concluding “the sun will rise tomorrow” is inductive reasoning.
Large Language Models (LLMs)
AI systems trained on massive amounts of text to understand and generate human-like language. Examples include ChatGPT and Claude. They work by predicting what words should come next based on patterns they learned from billions of examples. They don’t “think” like humans—they’re very sophisticated pattern-matching systems.
Machine Learning
A type of AI where computers learn from examples rather than following explicit programmed rules. Instead of a programmer writing step-by-step instructions, the computer finds patterns in data on its own. It’s like learning to recognize spam emails by seeing thousands of examples rather than being given a checklist of spam characteristics.
Moore’s Law
An observation by Gordon Moore in 1965 that the number of transistors on computer chips doubles approximately every two years, making computers faster and cheaper over time. This pattern held true for decades and explains why your smartphone is more powerful than supercomputers from the 1990s. However, this trend is slowing down as we reach physical limits.
Neural Network
A type of AI modeled loosely on how the human brain processes information, using layers of interconnected nodes (like neurons). Each layer transforms data in different ways until the network produces an output—like identifying whether an image contains a cat or recognizing spoken words. The “learning” happens by adjusting the connections between nodes.
Perception
The ability to interpret sensory information from the environment—like seeing, hearing, or feeling. In AI, perception means a computer’s ability to take in and make sense of inputs like images, sounds, or text. For example, facial recognition technology uses perception to identify people in photos.
Problem-Solving
The process of finding solutions to difficult or complex questions. In AI, this means using algorithms and reasoning to work through challenges—like finding the shortest route between two cities or figuring out the next move in a chess game. Different types of problems require different problem-solving strategies.
Processes
A series of steps or actions taken to achieve a specific result. In computing, a process is a program running on your computer. In AI, processes refer to the systematic methods used to train models, make decisions, or complete tasks—like the step-by-step way an AI analyzes your question and generates a response.
Training Data
Training data is information that is used to teach a machine learning model how to make predictions, recognize patterns, or generate content.
Turing Test
A test proposed by Alan Turing in 1950 to measure whether a machine can exhibit intelligent behavior indistinguishable from a human. In the test, a human judge has text conversations with both a human and a machine. If the judge can’t reliably tell which is which, the machine is said to have passed. It’s debated whether this truly measures “intelligence.”
Vectors
A vector is a mathematical approach for expressing and organizing data. Vectors are the building blocks that serve as AI’s universal language, essentially a list of numbers that represent data e.g. a word or an image.
These 3 resources also offer additional concepts if you would like to add to the list:
- https://aipedagogy.org/guide/key-terms
- https://www.bowdoin.edu/hastings-ai-initiative/resources/initiative-created-resources/ai-glossary_-from-basic-to-technical-terms.pdf
- https://www.turing.ac.uk/news/data-science-and-ai-glossary
🔗 Links to Module 1 Materials
(these also appear as subpages under the Module 1 dropdown menu)
