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Unit 6 – Teacher Resources
Instructor Resource · AIBIP Course
Unit 6 Teacher Resources
Introduction to Coding & AI Development
Everything you need to teach, assess, and support students through Unit 6 — lesson guides, learning objectives, discussion prompts, common misconceptions, differentiation strategies, a pacing guide, and the full project rubric.
Why Unit 6 Matters
Unit 6 is where the AIBIP course pivots from learning about AI to building it. Students meet Python, algorithmic thinking, and the real developer toolkit for the first time. The machine-learning ideas introduced conceptually in earlier units (supervised vs. unsupervised learning, training data, bias) become concrete here as algorithms students can actually run — regression, classification, clustering, and reinforcement learning.
The biggest instructor challenge in this unit is coding anxiety. Many students freeze the moment they see code. The unit is designed to lower that barrier: Python was chosen for its readability, all activities can run in a browser via Google Colab (no installs), and every lesson includes a prompt-engineering activity where students use an AI assistant as a coding partner. Your job is less about teaching syntax perfectly and more about building confidence — normalize errors, encourage running examples, and celebrate small wins.
🎯 Teaching through-line: Keep it hands-on. Students remember what they type and run far better than what they only read. Wherever possible, do live coding demos, have students debug real snippets, and let them see an algorithm turn into a working model on screen. The unit ends in a build project for a reason.
Unit 6 At a Glance
3
Lessons: 6.1 through 6.3
8–10 hrs
Estimated total time over 4–5 sessions
14
Knowledge-check questions across lessons
12 pts
Build a Simple AI-Powered Application project
| Lesson | Title | Core Concepts |
|---|---|---|
| 6.1 | Introduction to Python for AI | Why Python for AI; syntax & indentation; variables, loops, conditionals; lists & dictionaries; functions; NumPy & Pandas |
| 6.2 | Writing and Understanding AI Algorithms | What an algorithm is; binary search & linked lists; regression, classification, clustering, reinforcement learning; the model-building workflow |
| 6.3 | AI Development Tools and Platforms | Jupyter Notebooks & Google Colab; scikit-learn; TensorFlow/PyTorch; AutoML; Kaggle; GPUs |
Lesson-by-Lesson Teaching Guides
Lesson 6.1 — Introduction to Python for AI
Students will be able to
- Explain why Python is a popular language for AI development and its core features
- Read and write basic Python syntax — indentation, variables, loops, and conditionals
- Use fundamental data structures (lists and dictionaries) to manage data
- Write simple functions in Python to perform specific tasks
Key teaching points
- Emphasise that Python's indentation defines code blocks — this is the #1 source of beginner errors. Demonstrate an if/else and a loop with proper indenting.
- Contrast a list (ordered, uses square brackets) with a dictionary (key–value pairs, uses curly braces) using relatable examples.
- Show a tiny function with
defto make "reusable code" concrete — reinforce that functions avoid repetition. - Mention that libraries like NumPy and Pandas build on these basics; students don't need to master them yet, just know they exist.
Activity note
- The Prompt Engineering Activity has students ask an AI assistant to write a simple program (e.g. sum 1–10), then refine the prompt. Encourage them to read and understand the generated code, not just copy it — and to notice how a clearer prompt yields better output.
Lesson 6.2 — Writing and Understanding AI Algorithms
Students will be able to
- Define what an algorithm is and explain why algorithms matter in programming and AI
- Describe classic algorithms and data structures — binary search and linked lists
- Distinguish the AI algorithm types: regression, classification, clustering, and reinforcement learning
- Outline the AI model workflow: data collection, preparation, training, evaluation, and deployment
Key teaching points
- Anchor "algorithm" with the recipe analogy — step-by-step instructions turning input into output.
- Demo binary search live: pick a number 1–100 and have the class guess by halving the range. Stress that it only works on sorted data.
- Use a scavenger-hunt analogy for linked lists (each clue points to the next node).
- Match each AI algorithm to the kind of problem it solves: regression → numbers, classification → categories, clustering → groups, RL → learning from feedback.
- Walk the model workflow end to end so students see the big picture, not just the algorithm.
Activity note
- The Prompt Engineering Activity asks the AI to implement binary search and then explain it step by step. Have students trace the function with a small sorted list (e.g.
[1,3,5,7,9], target 5) so they verify understanding rather than trusting output blindly.
Lesson 6.3 — AI Development Tools and Platforms
Students will be able to
- Identify popular AI development tools and describe their purposes (Jupyter, Google Colab)
- Explain how notebook environments streamline writing and testing AI code
- Recognise key libraries and frameworks — scikit-learn, and that TensorFlow/PyTorch exist for deep learning
- Explain what AutoML tools do and how they make AI development more accessible
Key teaching points
- Frame a notebook as a document that mixes runnable code, output, charts, and notes — ideal for the "write a bit, run it, see the result" loop.
- Highlight Google Colab as free, browser-based, and no-install, with free GPU access — the easiest on-ramp for students.
- Position scikit-learn as the beginner's ML library (
model.fit()/model.predict()); mention TensorFlow/PyTorch as advanced deep-learning frameworks students will meet later. - Explain AutoML as tools that auto-try models and settings — great for beginners, but frame it as a starting point, not a replacement for understanding.
Activity note
- The Prompt Engineering Activity has students ask an AI how to use a chosen tool or library (e.g. loading a CSV with Pandas in Colab, or training a simple scikit-learn model). Encourage follow-up prompts when an answer is unclear — modeling how AI can act as an on-demand tutor.
Discussion Prompts
Lesson 6.1
Why do you think Python's readable syntax matters so much for AI teams?
Draw out collaboration and maintenance: clean, readable code is easier for teammates to understand, debug, and build on. Connect to Python's use of indentation for structure.
Lesson 6.2
Can you describe an everyday task as a step-by-step algorithm?
Have students break down something familiar (making a sandwich, finding a contact). Reinforce that algorithms are just clear, ordered instructions — and that precision matters.
Lesson 6.2
Given a real problem, how would you decide between regression, classification, and clustering?
Use the "predicting a number vs. a category vs. finding groups" test. Offer scenarios (temperature forecast, spam filter, customer segments) and have students match.
Lesson 6.3
What are the pros and cons of letting an AutoML tool build a model for you?
Balance convenience and speed against the risk of not understanding what the model does. Tie to the theme that tools help, but understanding still matters.
All lessons
When is using an AI assistant to write code helpful — and when could it hold back your learning?
Encourage students to use AI as a tutor and partner, but to always read, test, and understand generated code. Connect to the prompt-engineering activities throughout the unit.
Common Misconceptions & Fixes
Misconception
"Indentation in Python is just for looks, like in an essay."
The Fix
In Python, indentation is required — it defines code blocks like the bodies of loops and functions. Wrong indentation causes real errors.
Misconception
"A list and a dictionary are basically the same thing."
The Fix
A list is an ordered collection accessed by position; a dictionary stores key–value pairs accessed by key. Choose based on how you'll look things up.
Misconception
"Binary search works on any list."
The Fix
Binary search only works on a sorted list. On unsorted data it can miss the target entirely — the list must be sorted first.
Misconception
"Regression and classification are the same because both are supervised learning."
The Fix
Both are supervised, but regression predicts continuous numbers while classification predicts discrete categories. The output type is the key difference.
Misconception
"Google Colab is a programming language or a library."
The Fix
Colab is an environment — a cloud-based Jupyter Notebook service. scikit-learn is a library; Python is the language. They play different roles.
Misconception
"AutoML means you don't need to understand machine learning at all."
The Fix
AutoML automates model selection and tuning, but you still need to understand the problem, the data, and how to interpret the results it produces.
Differentiation Strategies
🟢 Support
Provide a printed cheat sheet of Python syntax and vocabulary to reference during activities
Pair students for the debugging challenge and coding activities
Give starter code with comments, so students fill in small pieces rather than writing from scratch
Offer the simplest project option (Option A/B) with a ready-made dataset
🔵 Core
Read, write, and run Python examples for each lesson independently
Choose and complete one project option with their own code and explanations
Use the self-assessment rubric to reflect on their own work
Complete each prompt-engineering activity and interpret the AI's output
🟣 Extend
Build a rule-based chatbot in Python using conditionals and user input
Experiment with an AutoML tool and compare its model to a hand-built one
Run a model-evaluation experiment: add a feature or more data and measure the change
Propose and build their own project idea (Option D) with visualizations
Unit 6 Project Rubric — Build a Simple AI-Powered Application (12 pts)
Students build a simple AI-powered application in Python (in Jupyter or Colab): they handle data, apply an algorithm or model, and produce a result with explanation. Each criterion is worth up to 3 points (Excellent = 3, Good = 2, Needs Improvement = 1), for 12 points total. A score of 10/12 or above is a strong pass.
| Criterion | Excellent (3) | Good (2) | Needs Improvement (1) |
|---|---|---|---|
| Code Implementation | Correct, well-organized, fully functional; clear use of Python basics; minimal errors and handles edge cases | Runs correctly for the main scenarios with minor issues; accomplishes the task; few runtime errors | Incomplete or errors often; poorly organized; may not fulfill the requirements |
| AI Application | Applies a well-chosen, justified algorithm and interprets its output (accuracy, error, cluster meaning) insightfully | Uses a suitable model and gives some basic interpretation of results | No clear algorithm, or one misapplied, with little interpretation or understanding |
| Use of Tools & Libraries | Proficient use of a notebook plus libraries like scikit-learn/Pandas; smooth, reproducible workflow | Uses the required environment and libraries appropriately with minor missteps | Little evidence of using the recommended tools or libraries properly |
| Documentation & Presentation | Clear markdown/comments describe each step and rationale; easy to follow; thoughtful reflection | Some explanations covering the major parts; results stated with brief comment | Little to no explanation; mostly raw code without context or reflection |
✅ Grading tip: Actually run each student's notebook where possible to verify it works end-to-end. Watch for hard-coded paths or missing imports that suggest the code wasn't truly run. Give partial credit when comments show understanding even if the implementation is incomplete.
Suggested Pacing
| Session | Focus | Time |
|---|---|---|
| Session 1 | Lesson 6.1 — Python syntax, variables, loops, lists, dictionaries, functions, plus a debugging activity | ~2 hrs |
| Session 2 | Lesson 6.2 — Algorithms (binary search, linked lists), AI algorithm types, the model workflow, algorithm-matching game | ~2 hrs |
| Session 3 | Lesson 6.3 — Notebooks & Colab, scikit-learn, AutoML, Kaggle, plus a mini AI model lab | ~2 hrs |
| Session 4 | Unit 6 Project — introduction, choosing an option, and a supervised build workshop | 1–2 hrs |
📌 Flexible delivery: A fifth session works well for project completion and short presentations. For a compressed 4-session schedule, combine the conceptual parts of Lessons 6.2 and 6.3, and assign coding practice and project work as homework so students still get hands-on experience.
Instructor FAQ
No installation is required. Google Colab runs entirely in a web browser and provides Python plus common libraries for free. Any device with a browser (including Chromebooks) works, which makes Colab the recommended environment for the whole unit.
None. Unit 6 assumes students are complete beginners at coding. Lesson 6.1 starts from the very basics of Python syntax. The focus is on building confidence and understanding, not on advanced programming.
Yes — the prompt-engineering activities are built around it, and using AI as a coding partner is a real, valuable skill. The key expectation is that students read, test, and understand any generated code rather than pasting it blindly. Ask them to explain what their code does.
Option B (classification with the built-in Iris dataset) is the most scaffolded — scikit-learn ships the data and the models, so students can focus on the workflow. Provide starter code with comments for support students, and reserve Option D (own idea) for those ready to extend.
Unit 6 turns earlier conceptual learning into practice. The supervised/unsupervised learning ideas become runnable algorithms; the AI examples from earlier units become things students can build. Completing a working AI application here is a major milestone that prepares students for more advanced project work in later units.