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Class 6 · Artificial Intelligence
Chapter 3 Teaching Pack
Simple Pattern Recognition and Decision Making — a print-ready plan for moving from patterns to predictions, and from evidence to careful decisions.
Where this chapter sits
Class 6 carries 100 hours across the year: 40 hours Computational Thinking, 20 hours Artificial Intelligence, and 40 hours interdisciplinary projects. The AI component has four chapters; this is the third. Students have already met basic data concepts in Chapter 2. Chapter 4 turns next to ethics and digital responsibility.
| # | Chapter | Learning focus | Suggested |
|---|---|---|---|
| 1 | Introduction to AI and Everyday Examples | AI in daily life, AI and automation, human and machine abilities, ways machines learn | 6 periods |
| 2 | Basic Data Concepts | Types of data; collecting, arranging and representing it | 5 periods |
| 3 | Simple Pattern Recognition and Decision Making | Pattern rules and predictions, observations and conclusions, decisions from evidence | 5 periods |
| 4 | Ethics and Digital Responsibility | Responsible use, safety, privacy, passwords and digital footprints | 4 periods |
Lesson plan · 5 periods
Period 1 — A pattern needs a rule
Start with three board examples: 4, 7, 10, 13; a border made from circle–square–circle–square; and the class settling after the second bell on several days. Ask what connects them. Students usually call any repeated thing a pattern. Tighten that idea: a useful pattern has an order or relationship that can be stated as a rule.
Collect one number, one shape and one behaviour example from the room. For each, insist on a full sentence: “Add three each time”, not merely “It goes up.”
Period 2 — A rule earns a prediction
Model a prediction as two linked parts: what comes next and why. Use only sequences whose rule is stated or whose shortest repeating block is shown more than once. This prevents a guessing game in which several hidden rules could fit the same few terms.
Period 3 — Group first; observation is not conclusion
Write these found-object records on the board in the order collected: pencil, bottle, notebook, pencil, ruler, bottle, pencil, notebook. Ask pairs to group them by item type and count each group. The list preserves the collection order; the groups make two facts easier to see: pencils form the largest group, and the ruler appears only once. Have students name the shared characteristic they used. Make the purpose explicit: grouping similar records can expose a pattern that is hard to notice in an ungrouped list.
Write: “Nine of twelve students in this row brought lunch from home.” That is an observation: it reports the data. Then write: “Students prefer home food.” That is a conclusion: it interprets the observation. Ask what the second statement assumes and whether one row represents the class.
Students often add a cause that was never measured. Circle words such as because, prefer, always and better; they are signals to check the evidence, not words to ban.
Period 4 — “Not enough data” is a correct answer
Show three real daily totals: 18, 21, 24. State that no rule has been given. If asked for a tentative next value, most students will suggest 27 because both observed changes were +3; accept it as a reasonable possibility, not a certainty. The claim “the total will keep rising” is an unsupported generalisation from only three records. Reveal the fourth total, 17, and let the class revise both prediction and conclusion.
Period 5 — From evidence to a decision
Put the full chain on the board: data → pattern → prediction → decision. A decision must name both the action and the observation that justifies it. It must also stay open to change when new data arrives.
Connect this directly to AI: an AI system finds regularities in data, uses them to make a prediction or classification, and that output may guide a decision. Weak, narrow or too-small data can therefore lead to a weak decision. Use the unplugged activity in Section 05 to make that limit visible.
Worksheet
Name: Class & Section: Date:
A · Choose the best answer
B · State the rule and continue
C · Group similar records
Ten students named how they travelled to school, in the order their answers were collected: walk, bus, cycle, bus, walk, bus, van, cycle, bus, walk.
D · Read the data carefully
The librarian recorded book returns during one school week.
| Day | Monday | Tuesday | Wednesday | Thursday | Friday |
|---|---|---|---|---|---|
| Books returned | 18 | 22 | 22 | 25 | 19 |
(i) Thursday had 25 returns. (ii) Students prefer returning books near the end of the week.
E · Decide from evidence
| Route | Day 1 | Day 2 | Day 3 | Day 4 | Day 5 |
|---|---|---|---|---|---|
| A | 18 min | 35 min | 19 min | 20 min | 18 min |
| B | 23 min | 22 min | 24 min | 21 min | 23 min |
Answer key & teaching notes
| Q | Answer | What to watch for |
|---|---|---|
| 1 | (b) | A list is not automatically a pattern; students should look for an order or relationship. |
| 2 | (c), 22 | The stated rule is +4: 18 + 4 = 22. |
| 3 | (a) | The timing repeats under a school-day condition. One-off events do not establish a pattern. |
| 4 | (c) | 17 − 11 = 6. The other choices judge, generalise or recommend an action. |
| 5 | (c) | Most of the class will choose (a) first. Three consecutive days are observations, but they do not justify “always”. Ask how many more days and which kinds of days should be recorded. |
| 6 | 15; add 3 each time | Require both the term and the rule. “It increases” is too vague. |
| 7 | 162; multiply by 3 each time | A common error is 108 from adding 54. Check every transition, not only the last one. |
| 8 | 7:25 a.m. | The supporting observation is that it arrived at 7:25 on all five recorded school days. The prediction is conditional; do not accept “it must arrive then”. |
| 9 | Square | The shortest block is triangle–circle–square. It appears twice, then begins again with triangle–circle, so square completes the third block. |
| 10 | Walk: 3; bus: 4; cycle: 2; van: 1. | Groups must use the shared characteristic “travel mode”. Check that all ten records are counted once. |
| 11 | Bus was the largest group, with 4 students; van was the smallest, with 1. | Accept either comparison, or another accurate insight made clearer by the groups. Repeating the grouped counts without stating what became visible does not answer the question. |
| 12 | Example: Thursday had 7 more returns than Monday. | Accept any correct comparison from the table. It must report numbers or a directly visible relationship, not explain why. |
| 13 | No. | Thursday is highest in this one week, but one week cannot support “always”. This distinction is the learning target. |
| 14 | Returns for the same weekdays across several more school weeks. | “More data” alone is incomplete; look for a sensible comparison across Thursdays and other weekdays. Holidays may need to be noted. |
| 15 | (i) observation; (ii) conclusion | The second statement adds an interpretation about preference that the counts did not measure. |
| 16 | Do not make a permanent change yet; collect several weeks of returns first. | A temporary trial is also reasonable if the student says it should be checked against new data. A confident permanent change is not supported. |
| 17 | Stock more bananas than guavas for day five, then keep recording sales. | Banana sales were higher on each of the four recorded days. Accept another cautious decision tied to that observation; reject “bananas will always sell more”. |
| 18 | No; the data is too limited, and timing alone does not prove that rain caused the delay. | Students need more rainy and dry days and should consider traffic, breakdowns or departure time. Two matching events show an association, not a cause. |
| 19 | Answers vary; all three parts must connect. | Example: “I refill my bottle at lunch each school day; I predict I will need a refill tomorrow; I will carry enough water for the morning.” Check that the student states evidence, not a lucky guess. |
| 20 | Choose Route B. | All five Route B journeys were within 25 minutes; Route A exceeded the limit once. The table does not prove that B will always stay within 25 minutes or that B is always faster. Full credit requires the objective, exact evidence and one valid limit. |
Unplugged activity · “Reveal the Data”
Period 5 · 35 minutes · No devices, no internet and no special room required.
What you need
Forty small reused-paper slips, a pencil and the board. Mark 16 slips with a circle and 24 with a triangle. Arrange them before class in the reveal order below and keep the stack face down.
Before students enter
Make slips 1–3 circles. Among slips 4–10, place two circles and five triangles. Among slips 11–40, place eleven circles and nineteen triangles. The totals are therefore 16 circles and 24 triangles. The order is deliberate: early evidence points one way, then changes.
How it runs with 40+ students
- Split the class into eight groups of about five. Each group makes three headings in a notebook: Observation, Conclusion, Decision.
- Reveal the first three slips. Groups record exactly what is visible, decide whether they can name the majority in the whole stack, and hold up one paper marked C for circle, T for triangle or ? for not enough data.
- Reveal slips 4–10. Pause again. There are now five of each; the only supported response is “?”. Ask groups to correct any earlier certainty.
- Reveal the remaining slips and let groups total all 40. They may now decide that triangles are the majority in this stack, supported by 24 triangles against 16 circles.
- Each group reads one sentence that changed as the evidence grew. Finish by writing the AI chain: data → pattern → prediction → decision.
Project brief & rubric
The project checks the full reasoning chain. Retain a small range of marked samples rather than only the neatest work.
| Criterion | 4 — Exceeds | 3 — Meets | 2 — Approaching | 1 — Beginning |
|---|---|---|---|---|
| Data record | 10+ clear, dated observations with units or labels; method is consistent | 10 dated observations that can be understood | 6–9 observations or some labels missing | Fewer than 6 or record cannot be read |
| Pattern & prediction | Rule is precise; prediction follows it and includes a condition | Valid pattern and matching prediction, or a justified “no clear pattern” | Pattern is vague or prediction only partly follows | Pattern and prediction are unsupported |
| Observation, conclusion & limit | Separates all three and explains how the limit affects confidence | Correct observation, conclusion and one real data limit | Two are correct but one is confused or missing | Opinion replaces the evidence |
| Evidence-based decision | Practical decision cites exact evidence and says when to review it | Decision is supported by a named observation | Decision is related but evidence is vague | Decision does not follow from the data |
Suggested: 16 marks total. In a short viva, ask: “What can your data not tell us?” A polished chart with no honest limit has not met the central outcome.
Evidence record
Keep one completed record for the class with a few marked samples. This page is designed to print and file on its own.
| Field | Record |
|---|---|
| School | |
| Class & section | |
| Chapter taught | AI Ch. 3 — Simple Pattern Recognition and Decision Making |
| Periods used | |
| Dates | |
| Teacher | |
| Activity conducted | Reveal the Data (unplugged evidence-limit activity) |
| Assessment used | Project — “From Pattern to Decision”, rubric-scored |
| Students assessed | |
| Learning evidence noticed | ☐ States pattern rule ☐ Separates observation and conclusion ☐ Identifies limited data ☐ Justifies a decision |
| Samples retained | ☐ 3 marked projects ☐ Group activity notes ☐ Completed worksheets |
| Students needing follow-up | |
| Teacher’s note |