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Overview and Introduction:

Author: Jim Burnham - TopClown@STEAMClown.org. License: Distributed as Open Source. 

Prerequisites:

• No explicit prerequisite course work or coding knowledge is required, but students are expected to have a good understanding of basic computer principles.

Caution & Safety  Considerations:

Primer:  "Aaron, I can imagine no way in which this thing could be considered anywhere remotely close to safe. All I know is I spent six hours in there and I'm still alive... You still want to do it?"

• As with any activity, please make sure you are using appropriate safety equipment.   If you are coding, writing, reading, or working a lab, make sure you stand up and stretch every hour or so,  Please consider any safety issues connecting to a Raspberry Pi, Arduino, computers and other electronic equipment.

Essential Questions:

• How do machines learn from data, and how is this different from how humans learn?

  • Focus: Machine Learning

  • Why it matters: Helps students compare human and machine intelligence, and understand how data-driven systems evolve over time.

• What are Large Language Models (like ChatGPT), and how do they process and generate human-like language?

  • Focus: LLMs and Natural Language Processing

  • Why it matters: Encourages exploration into the structure of LLMs, training methods, and real-world applications (and limitations) in communication, education, and media.

• In what ways can machines “see” the world using Machine Vision, and how does this technology impact fields like medicine, security, and robotics?

  • Focus: Machine Vision

  • Why it matters: Connects visual processing by machines to authentic uses, while raising questions about ethics and reliability.

• What are the potential risks and benefits of deep learning systems making decisions without human input?

  • Focus: Deep Learning and Ethics

  • Why it matters: Promotes reflection on AI decision-making, bias, fairness, and the role of humans in oversight.

Key Academic Vocabulary or Concepts:

GENERAL AI & MACHINE LEARNING TERMS

• Artificial Intelligence (AI) - The science of making machines that can perform tasks that normally require human intelligence, such as learning, reasoning, or decision-making.

• Machine Learning (ML) - A branch of AI where computers learn patterns from data and improve their performance without being explicitly programmed.

• Training Data - The data used to teach a machine learning model how to make predictions or decisions.

• Model - A computer program that has been trained to recognize patterns and make predictions or decisions.

• Algorithm - A step-by-step procedure or set of rules a computer follows to solve a problem or perform a task.

• Supervised Learning - A type of ML where the model is trained on labeled data (where the correct answer is known).

• Unsupervised Learning - A type of ML where the model finds patterns in data without being told what to look for.

• Bias - An error in a machine learning model that occurs when the training data is not representative of real-world scenarios, leading to unfair or incorrect outcomes.

LARGE LANGUAGE MODELS (LLMs) - Like ChatGPT

• Large Language Model (LLM) - A type of AI model trained on massive amounts of text to generate human-like language, answer questions, or carry on a conversation (e.g., ChatGPT).

• Token - A small chunk of text (like a word or piece of a word) used by language models to process and generate language.

• Prompt - The input or question given to an LLM to generate a response.

• Natural Language Processing (NLP) - A field of AI focused on enabling machines to understand, interpret, and generate human language.

MACHINE VISION

• Computer Vision / Machine Vision - A field of AI that trains computers to interpret and understand images or video, similar to how humans use sight.

• Image Recognition - When a computer identifies objects, people, or patterns in images.

• Object Detection - An advanced form of image recognition that identifies specific objects and locates them within an image.

• Sensor - A device that detects and responds to input from the environment, such as a camera capturing images for vision systems.

DEEP LEARNING

• Neural Network - A computer system designed to simulate how the human brain processes information, used in deep learning.

• Deep Learning - A type of machine learning that uses many layers of neural networks to model complex patterns in large amounts of data.

• Layer - A group of connected nodes (neurons) in a neural network that processes data. More layers = "deeper" learning.

• Backpropagation - A method used by neural networks to improve accuracy by adjusting weights based on errors made during training.

Unit Topics and Lessons

Reference, Attribution & Resources:

Teachers - Unit, Module and Lesson Plans

If you are a teacher and want to connect and teach this Lesson or Module, discuss how I teach it, give me feedback, please contact me at TopClown@STEAMClown.org 

To access this Lesson Plan and the Teacher collaboration area, you will have needed to connect with me so I can Share the content with you.  Please go to  the Teachers & Partner Page, check out my Licensing and fill out my Collaboration and Curriculum Request Form.  I'll review and then grant you access to the requested areas and lesson plans if they exist.

If you have questions or feedback on how I can make a presentation, lesson, lab better please give use my Feedback Form.

Standards Alignments & Objectives:

NGSS (Next Generation Science Standards)

Science and Engineering Practices (SEPs)

• SEP 1: Asking Questions and Defining Problems -  Students ask questions about the limitations of AI systems and ethical design considerations.

• SEP 2: Developing and Using Models -  Students model how a neural network or image recognition system works.

• SEP 4: Analyzing and Interpreting Data -  Students analyze datasets used in training machine learning systems.

• SEP 6: Constructing Explanations and Designing Solutions -  Students propose how machine vision might improve accessibility or public safety.

• SEP 8: Obtaining, Evaluating, and Communicating Information -  Students investigate sources of bias in AI systems and present solutions.

NGSS Engineering Standards (ETS – in grades 9–12)

• HS-ETS1-1 -  "Analyze a major global challenge to specify qualitative and quantitative criteria and constraints for solutions."

• HS-ETS1-2 -  "Design a solution to a complex real-world problem by breaking it down into smaller, more manageable problems."

• HS-ETS1-3 -  "Evaluate a solution to a complex real-world problem based on prioritized criteria and trade-offs."

• HS-ETS1-4 -  "Use a computer simulation to model the impact of proposed solutions to a complex problem."

CTE Engineering and Architecture Pathway Standards

(California CTE Model Curriculum Standards – Engineering and Architecture Industry Sector – Engineering Technology Pathway)
(These can be adapted to other state CTE frameworks.)

CTE Anchor Standards

• Anchor Standard 2.0: Communications -  Use AI-generated content responsibly and communicate technical findings effectively.

• Anchor Standard 4.0: Technology -  Use advanced tools like AI platforms and simulation software to support project-based learning.

• Anchor Standard 10.0: Technical Knowledge and Skills -  Apply programming logic and data analysis to solve engineering problems.

Pathway-Specific Engineering Standards (Engineering Technology Pathway)

• ET 4.2 -  "Use computer-aided design (CAD), computer-aided manufacturing (CAM), and simulation tools to support engineering processes."

• ET 8.3 -  "Understand how sensors, control systems, and intelligent systems (like robotics and AI) are used in engineering."

• ET 9.0: Problem Solving and Critical Thinking -  Design solutions that integrate intelligent systems such as machine learning or vision.

• ET 10.1 -  "Use algorithms and software in automation and control systems."

Resource Attribution:
Sites Referenced or Summarized:

• Reference Text Book - Basic College Mathematics with Early Integers 4th edition - Elayn Martin-Gay - University of New Orleans - Pearson

• Reference Sites - 

Images:

• https://imgbin.com/png/ZJtzkYZZ/under-construction-png

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Random Stuff To Sort

Resource Key:

<Unit> - <Module Name> - Lessons - Daily/Weekly Lesson Plan

Key: 📰 Slides / Audio 🎧 / 📽️▶️ Video/YouTube / 🎧▶️📽️ Audio/Video / ✨ Resources /  🖼️ Tutorial / 📖 Reading Activity / 📝 Writing Activity / 📖 📝 Reading/Writing / 📟 Coding / 🛠️ LAB Activity / 🚀 Quiz /  🔎 Review /  ✔️ Mastery Check / ✍️ Sign Up /🍕 Extra Credit / 🕸️ Web Links / 👩🏽‍🎓🧑🏽‍🎓🧑🏿‍🎓👩‍🏫 Class / 🏵️📜📃 Certificate / 🗂️ 📈 Collecting Survey Data

/🧟 Review / 🦾 Practice / 🆙Level Up /

🎚️🦑📤🎯  🚧

- 🦑 Special Project -

Assignment Type: ⚓ Establishing (Minimum Standard) / ⛏️ Developing (Digging Deeper) / 💎 Aspiring (Putting It Together)

This is an ⚓ Establishing Assignment (Minimum Standard)  - "Everyone Do" Assignment

This is an ⛏️ Developing (Digging Deeper) - "Everyone Should Do, To Stretch" Assignment

This is an 💎 Aspiring (Putting It Together)  - "When you have done the ⚓ Establishing and⛏️ Developing" Assignment

• 🚀 Formative Quiz - 🔎 Review

• 🚀 Quiz -🔀 Mastery Path

• 🚀 Summative Quiz -✔️ Skills Mastery Check

Quiz - verify that they are all listed as a "Formative", "Mastery Path", or "Summative" 

• 🚀 Formative Quiz - These are quizzes that the students can take a few times. I have them either set for unlimited times, or 3-5 times, where the final score is their average. The idea is that these Formative Quizzes are designed for students to learn and master a skill.  while I want them to ger 100%, and when it's set to unlimited tries, the student should get 100% eventually.  When the quiz is set to 3-5 tries with an average, then they should be prepared and should take the quiz seriously. I set the quiz to not show the right answer, but I do let them see their wrong answer.  I also put the explanation of the right and wrong answer in the right and wrong answer prompt for each question.  That way they can see why they got the answer wrong and learn from that experience.  

  • 8.1.0.3.2.4 - Python - Ch 3 - Functions - Quiz #2 -Built-In Functions - 🚀 Formative Quiz

• 🚀 Quiz -🔀 Mastery Path - These Mastery path quizzes are to be presented after the student has had a chance to do some labs and some Formative quizzes.   The goal is to let students have 2 chances to take this quiz, and take the average of the 2 attempts.  Based on the average, they will be presented with a Canvas Mastery Path, where they will have an option for take additional quiz and assignments to help with remediation.  This will get them ready to take the Summative Quizzes.

  • 8.1.0.3.3.1 -  Python - Ch 3 - Functions - Mastery Quiz #1 - 🚀 Quiz -🔀 Mastery Path

• 🚀 Summative Quiz -✔️ Skills Mastery Check - These Mastery path quizzes are to be presented after the student has had a chance to do some labs and some Formative quizzes.   The goal is to let students have 2 chances to take this quiz, and take the average of the 2 attempts. That will be their final module/subject topic grade.

  • 8.1.0.3.3.1 -  Python - Ch 3 - Functions - Skills Mastery Check Quiz #1 - 🚀 Summative Quiz -✔️ Skills Mastery Check