Programming for AI: your first working example

Why this example matters

A school coordinator recorded scores from a short practice activity. Comparing every score with a reference value illustrates a basic data transformation: the original list stays available while a new list contains meaningful differences.

What you will be able to do

• Recognize the input values and describe what they represent in Programming for AI.
• Run the Programming for AI program and explain why its displayed output follows from the recorded operations.
• Identify a limitation of the Programming for AI activity before applying similar reasoning to a real system.

Original worked program

The following Programming for AI demonstration uses small invented values so you can trace every operation. The Programming for AI activity uses only Python standard-library features; it downloads no model and calls no paid service.

raw_scores = [9, 12, 15, 18]
baseline = 12
changes = [score - baseline for score in raw_scores]
print("Changes:", changes)
print("Largest increase:", max(changes))

Actual output from Python

Changes: [-3, 0, 3, 6]
Largest increase: 6

Read each instruction

1. raw_scores = [9, 12, 15, 18]The assignment gives a name to a value or a calculated result in Programming for AI. In the Programming for AI activity, check the named values on that line rather than memorizing symbols.
2. baseline = 12The assignment gives a name to a value or a calculated result in Programming for AI. In the Programming for AI activity, check the named values on that line rather than memorizing symbols.
3. changes = [score - baseline for score in raw_scores]The assignment gives a name to a value or a calculated result in Programming for AI. In the Programming for AI activity, check the named values on that line rather than memorizing symbols.

Observe the results

To verify the Programming for AI result, copy the program into a fresh file and run it once without modification. Then change only one input. For the Programming for AI trial, record what you predict before pressing Run again. Compare the Programming for AI prediction with the observed result.

Second original practice: Variables, numbers, strings and printing

The Programming for AI extension changes the task, not just the numbers in the first example. Study its new data structure or operation, then decide how the result supports the milestone “Variables, numbers, strings and printing”.

counts = {"correct": 7, "incorrect": 3}
total = sum(counts.values())
print("Attempts:", total)
print("Correct share:", round(counts["correct"]/total, 2))

Actual second output

Attempts: 10
Correct share: 0.7

For the Programming for AI extension, read the code from its first assignment or import to its final print statement. Explain the additional Programming for AI idea introduced by this extension, then design a check that could reveal a mistake. The Programming for AI extension output above was recorded by running that independent program.

What the example does not prove

The comparison does not tell us why a score changed. A program cannot infer causes merely because its arithmetic is correct; the measurements and reference value need interpretation.

A reliable Programming for AI project also needs an explicit objective, test cases that were not used to tune the approach, documentation of the assumptions, and review of situations where the result might mislead the user. Do not mistake the small Programming for AI program for evidence of accuracy or safety in a deployed application.

How to validate your own result

For Programming for AI, a convincing result starts with a written claim about the actual task, not the appearance of a successful print statement. For Programming for AI, list the initial values and the one value you will vary; record a prediction that could be disproved. Save both Programming for AI outputs as separate records to avoid mixing up the results of the two investigations. A correct Python run only establishes that the particular instructions executed; it does not establish that the assumptions match every situation in which Programming for AI might be used.

Connect this trial to “Collections and indexing” without claiming that the milestone is already complete. Name a measurement you would collect for Programming for AI, a plausible error source and a person able to review the decision. The later topic “Conditions and branching” deserves its own investigation with different evidence. Keeping these questions separate prevents your Programming for AI learning journal from becoming a collection of exercises that all make the same claim.

Finally, keep the Programming for AI experiment reproducible: include the Python version, exact script, input values, observed results and any unexpected behavior. If a Programming for AI investigation uses external data, document the source and your permission to use those records. A new learner should be able to explain every output line in ordinary words before moving into the next milestone of Programming for AI.

Second verified lab: Data in practice

To deepen Chapter 1 of Programming for AI, compare the first calculation with a different, executable experiment. The small data lab below illustrates a related technique, not every possible implementation of installing python and running a script.

# Course: Programming for AI | Chapter 1 | Subject: Installing Python and running a script
# Validate a toy input table before using it downstream
records=[{"id":1,"value":4},{"id":2,"value":None},{"id":3,"value":4}]
valid=[row for row in records if isinstance(row["value"],int)]
print("Rows received:",len(records))
print("Valid rows:",len(valid))
print("Missing rows:",len(records)-len(valid))
print("Valid total:",sum(row["value"] for row in valid))

Actual output of the displayed code

Rows received: 3
Valid rows: 2
Missing rows: 1
Valid total: 8

While examining your first working example in the programming for ai course (chapter 1), apply this test: Explicit schema and missing-value checks make the transformation reproducible. The example does not silently treat missing observations as zeros. For the campus shuttle passenger counts exercise, distinguish what the code observes from any stronger predictive or causal claim.

While examining your first working example in the programming for ai course (chapter 1), apply this test: Interpretation limit. Filtering records changes the represented population; document what was excluded and why. Record the changed inputs and compare both outputs before extending the result to a project.Interpretation limit. Filtering records changes the represented population; document what was excluded and why. Record the changed inputs and compare both outputs before extending the result to a project.

20 practice questions and worked answers

Test your understanding of your first working example in the programming for ai course (chapter 1). Compare the claims in both runnable labs, and use the outputs to defend each numerical response.

Build one original mini-project

Chapter 1 checkpoint: Explain how installing python and running a script affects the campus shuttle passenger counts example; demonstrate one failing input; then propose a revision with an acceptance test and a recorded output. This checkpoint belongs to the Programming for AI course.

Create a simple reading-time summary using four invented daily minute counts. Calculate deviations from a chosen goal and check the arithmetic by hand.

A six-step practice plan

1. Installing Python and running a script. For the “Installing Python and running a script” exercise, identify one observable result and record what would demonstrate progress in Programming for AI.
2. Variables, numbers, strings and printing. For the “Variables, numbers, strings and printing” exercise, identify one observable result and record what would demonstrate progress in Programming for AI.
3. Collections and indexing. For the “Collections and indexing” exercise, identify one observable result and record what would demonstrate progress in Programming for AI.
4. Conditions and branching. For the “Conditions and branching” exercise, identify one observable result and record what would demonstrate progress in Programming for AI.
5. Loops and iteration. For the “Loops and iteration” exercise, identify one observable result and record what would demonstrate progress in Programming for AI.
6. Functions and reusable units. For the “Functions and reusable units” exercise, identify one observable result and record what would demonstrate progress in Programming for AI.

Save your original inputs, your changed inputs, the two actual outputs and a short explanation of what you learned about Programming for AI. The Programming for AI record forms evidence for the first milestone of your project portfolio.

Where you go next

Your Programming for AI roadmap has 18 subject-specific milestones progressing toward evaluation and a capstone. Future Programming for AI lessons will require new demonstrations and evidence, beyond the skills taught in this first chapter.