S1-S3 Where Hong Kong lives: comparing housing choices with a scoring model

• StageS1-S3
• SubjectMathematics
• Learning strandreflection on the application of AI
• Official documentsAI Literacy Learning Framework for Primary and Secondary Schools (Examples of Application Scenarios)
• Session timeOne lesson (60 min)

Pupils turn 'where is the better place to live?' into a small mathematical model: at most three factors, whole-number scores from 1 to 5, three weights that add to 10, and one weighted average — then fill in a method review sheet that says when the model can be trusted and when it cannot.

• AudienceTeachers Students
• ThemeHong Kong inquiry

• EquipmentProjector; printed worksheet; a calculator if you want one; internet access to reach the official tables
• Version1.1
• Last updated2026-10-17

What this solves

Where Hong Kong people live, and how much space they have, is something pupils see every day and almost never check. Ask an AI one question and it produces a confident answer — a floor area, a share, a trend — usually with no source, and with no explanation of how it compared anything.

This lesson turns the question into a small mathematical model: choose at most three factors, write a whole-number scoring rule from 1 to 5 for each one, set three weights that add to 10, and calculate a total with one weighted average. Pupils build the model on the fictional example figures printed in this page’s worksheet, then fill in a method review sheet that says where the model is subjective, where it needs real data, and how official statistics would replace the example figures.

The habit of checking stays exactly where it was: a tool can suggest a direction, but every figure needs a source and a comparison with the official published statistics. The model helps us write our thinking down clearly; it does not decide which choice is “correct”.

This lesson uses one weighted average only. Standard deviation, regression, sensitivity analysis, statistical significance and correlation coefficients are outside its scope.

The lesson sits inside the junior-secondary data-and-statistics unit and shows how professional gatekeeping works in a maths classroom: when an AI states a figure confidently and without a source, that is an AI hallucination, and the only way to remove it is to fact-check it against the official published statistics. It also fits the reflection on the application of AI strand of a school’s digital education plan, keeps critical discernment in view, and reminds pupils not to drift into over-reliance on the tool — choosing the factors, writing the rules and doing the arithmetic stay in their own hands.

Learning objectives

Pupils will be able to:

1. Turn “where is the better place to live?” into a model they can calculate, choosing at most three factors.
2. Write a whole-number scoring rule from 1 to 5 for each factor, and set three weights that add to 10.
3. Calculate the total for three options with one weighted average, rank them and explain the result.
4. Fill in a method review sheet that names the model’s three limits (the choice of factors, the subjectivity of scoring, the source of the data) and explains how official figures would replace the fictional example.

Written for the junior-secondary stage, it covers the reflection on the application of AI strand of AI literacy, with human judgement first and the tool second.

Teaching steps

1. Pose the question and define the model (15 minutes) — Project the three housing options and their fictional figures from the worksheet, and ask: if you had to compare these three, what would you look at? Narrow the class’s suggestions to at most three factors, and write them as one modelling question: “Which option is better overall on ___, ___ and ___?”
2. Set the scoring rules and the weights (20 minutes) — In groups, write what 1 and what 5 look like for each factor, and fill in 2 to 4 as a class. Then share out three weights that add to 10. Each group writes one sentence saying why a factor deserves its weight.
3. Calculate, rank and check (18 minutes) — Use the worksheet’s weighted average: total = (factor one × weight one + factor two × weight two + factor three × weight three) ÷ 10. Check that every total lies between 1 and 5, list the ranking, and re-check each calculation.
4. Review the method and close (7 minutes) — Each group completes the method review sheet, then reports in one sentence: “Our model puts ___ first, and its biggest limit is ___.”

The model’s limits (unchanged for the whole lesson)

• At most three factors — a fourth may not be added.
• Each score is a whole number: 1, 2, 3, 4 or 5.
• The three weights add to 10.
• One weighted average only.

Fictional example figures (for the exercise on this page, not data)

Every figure above is a fictional example written for this exercise. None of it describes a real estate, district or market price. To replace it with real figures, take them from the official statistics of the Census and Statistics Department, the Hong Kong Housing Authority and Housing Department, and the Rating and Valuation Department, and record the year and the table number.

Official sources (for adding real figures afterwards; pupils fetch these themselves and record the source and the year)

Answer key and marking notes

Groups may choose different factors and weights. One workable model is below, to show what the calculation and the record should look like:

Putting the three fictional options through it:

Ranking: C 3.2 > A 3.0 > B 2.8. C has the least space but the lowest rent and the shortest commute, so it comes first under these weights — a result worth discussing: a model can be calculated correctly and still not be the right decision, because the weights are ours to choose.

There is no single right answer. A different but reasonable set of factors or weights can change the ranking; as long as the four limits above are respected and every step has a stated reason, the answer is valid. What is being judged is whether the model and the record agree — not which option is “really” best.

Marking is process-based:

Three things the method review sheet must show:

1. The choice of factors — why these three? A different three could give a different answer.
2. The subjectivity of scoring — two people can score the same flat differently; the more specific the descriptors, the smaller the disagreement.
3. The source of the data — the example figures are fictional. Real figures must come from official statistics, with the year and the table number written down.

The key judgement: “impossible to check” is still a legitimate answer. If a figure has no source, uses incompatible units, or states an estimate as a fact, it cannot go into the model as data — and that is exactly where an AI hallucination hides.

If pupils want to use an AI to suggest factors or descriptors, they may — but treat the AI’s answer as a draft: ask for its source, compare it with the official figures, and record in the method review which parts came from the AI and which they changed.

Files

Hong Kong housing scoring-model worksheet (editable Word document)

Includes: teacher notes, the fictional figures for three housing options, the scoring-model table (factor / 1 / 3 / 5 / weight), the weighted-average calculation table, the method review sheet, and the answer key and marking notes.

Official documents

Every resource in this library is written to the AI Literacy Learning Framework for Primary and Secondary Schools and falls within the policy scope of the Blueprint for Digital Education Development in Primary and Secondary Schools.

This resource maps to:

• AI Literacy Learning Framework for Primary and Secondary Schools (Examples of Application Scenarios) (p.13): using a junior-secondary mathematics data and statistics topic as its example, it asks pupils to question the AI about its source of information and to check its output against the official published statistics.

Learning strand: reflection on the application of AI

Official documents page: https://www.edb.gov.hk/en/edu-system/primary-secondary/applicable-to-primary-secondary/it-in-edu/debp.html

This page is an independent Beets teaching resource, not an official EDB document.

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