Rule-Based Learning Path Recommender

How to use this project

Reuse your Python setup, then create five files

This project uses the Python and VS Code setup introduced in Project 02. You do not need to install pandas, matplotlib or any other library.

Prerequisite setup

Confirm Python and VS Code are ready

If you have not installed them, complete the installation instructions at the beginning of Project 02 first.

Open Python Setup

Part A

Create and open the folder

Keep every Project 03 file together so Python can import the recommendation rules.

Create a folder named project-03-learning-path-recommender.
Open VS Code and select File → Open Folder.
Select the folder you created.
To create a file manually, select the New File icon in the Explorer panel and enter the complete filename, such as rules.py.
Open a VS Code terminal and run python --version. On some Windows systems, use py --version.
Use Python: Select Interpreter if VS Code has not selected your installed Python version.

Part B

Download all five files

Download the Starter Files below and move them into the main project folder.

app.pyCollects learner input and displays the resultrules.pyContains the recommendation function and rulestest_recommender.pyChecks important recommendations automaticallyREADME-template.pdfRename to README.mdproject-checklist.txtTracks final completion evidence

Keep the Python files together

app.py and test_recommender.py import code from rules.py. They will fail if rules.py is renamed or stored in another folder.

Part C

Review and run the downloaded code

Read downloaded code before allowing VS Code to execute it. Then use the terminal inside the project folder.

VS Code terminal

python app.py
python test_recommender.py

Ready to begin when: app.py asks for the learner’s experience and test_recommender.py prints All tests passed.

Recommended project workflow

Read one guided step → edit the relevant file → save with Ctrl + S → run the program → compare the result → mark the checkpoint only when it works.

What you will build

An explainable lesson recommender

Create a Python program that asks about a learner’s experience, goal, Python knowledge and completed topics, then recommends a suitable next lesson.

Skills you will learn

Rule-Based AiPython conditionsFunctionsUser inputRecommendation logicExplainable decisionsBasic testing

Requirements

LevelBeginner

CodingBasic Python

Time60–90 minutes

SoftwarePython and VS Code

ComputerNormal laptop; no GPU

Starter files

app.pyUser interface ↓rules.pyRecommendation logic ↓test_recommender.pyAutomated tests ↓README-template.pdfDocumentation ↓project-checklist.txtFinal checklist ↓

project-03-learning-path-recommender/
├── app.py
├── rules.py
├── test_recommender.py
├── README.md
└── project-checklist.txt

Five guided steps

Build the recommender

Create the learner profile, recommendation rules, command-line interface and tests.

Step 01

Define the learner information

Decide which limited information the rules need.

ExperienceBeginnerGoalMachine LearningPython knowledgeNoCompleted topicAi Foundations

Experience levelLearning goalPython knowledgeCompleted topics

Step 1 completeI defined the four learner inputs.

Step 02

Create the recommendation function

Place the ordered if–then rules inside rules.py.

rules.py

def recommend_lesson(
    experience: str,
    goal: str,
    knows_python: bool,
    completed_topics: list[str],
) -> tuple[str, str]:
    """Recommend the next lesson and explain the decision."""

    experience = experience.strip().lower()
    goal = goal.strip().lower()
    completed = {topic.strip().lower() for topic in completed_topics}

    if experience == "beginner" and "ai foundations" not in completed:
        return "Ai Foundations", "Start with the basic concepts first."

    if not knows_python:
        return "Python Foundations", "Python is needed for practical projects."

    if goal == "machine learning" and "data preparation" not in completed:
        return "Data Preparation", "Machine Learning requires clean data."

    if goal == "machine learning":
        return "Supervised Learning Basics", "You are ready for ML basics."

    if goal == "generative ai":
        return "Prompt Engineering", "Start with structured model interaction."

    if goal == "computer vision":
        return "Image Classification Basics", "Start with a core vision task."

    return "Ai Applications Overview", "Explore the main application areas."

Why rule order matters

The first matching rule returns the recommendation. Foundation and prerequisite checks should therefore appear before specialization rules.

Describe one additional rule you could add

Step 2 completeI created at least five rules with an explanation for every result.

Step 03

Create the user interface

Collect input safely and display both the lesson and reason.

app.py — essential flow

from rules import recommend_lesson

experience = input("Experience level: ")
goal = input("Learning goal: ")
knows_python = get_yes_no("Do you know basic Python? Yes/No: ")
completed_text = input("Completed topics, separated by commas: ")
completed_topics = [topic.strip() for topic in completed_text.split(",") if topic.strip()]

lesson, reason = recommend_lesson(
    experience, goal, knows_python, completed_topics
)

print("\nRecommended next lesson:", lesson)
print("Reason:", reason)

Run the programpython app.py

Recommendation produced by your first run

Input safety

Use the supplied get_yes_no() function in the complete starter file so invalid answers are requested again.

Step 3 completeThe program collects input and displays an explained recommendation.

Step 04

Test different learners

Confirm that different profiles follow the intended rules.

Test case 1

Experience: Beginner
Goal: Machine Learning
Python: No
Completed: Ai Foundations

Expected: Python Foundations

Test case 2

Experience: Beginner
Goal: Machine Learning
Python: Yes
Completed: Ai Foundations, Data Preparation

Expected: Supervised Learning Basics

Results and unexpected behaviour

Step 4 completeI tested at least two different learner profiles.

Step 05

Add automated tests

Use assertions to check the recommendation function automatically.

test_recommender.py

from rules import recommend_lesson

lesson, _ = recommend_lesson(
    experience="Beginner",
    goal="Machine Learning",
    knows_python=False,
    completed_topics=["Ai Foundations"],
)
assert lesson == "Python Foundations"

lesson, _ = recommend_lesson(
    experience="Beginner",
    goal="Machine Learning",
    knows_python=True,
    completed_topics=["Ai Foundations", "Data Preparation"],
)
assert lesson == "Supervised Learning Basics"

print("All tests passed.")

Run the testspython test_recommender.py

Expected result

All tests passed.

Automated test result

Step 5 completeBoth automated tests pass.

Learner information↓If–then rules↓Recommended lesson↓Reason↓Learner accepts or chooses another

Final output

Complete your recommender

Working Python recommenderAt least five rulesExplanation for every resultTwo or more test casesREADME file

Responsible Ai check

The recommendation is clearly explainedThe learner can ignore or change itNo sensitive personal data is collectedMissing information is handled safelyThe system does not claim to know the learner perfectly