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Module 7 of 7

Teaching AI ethics and data

Build practical judgement about data quality, bias, privacy, transparency, contestability, and human impact.

Module outcome: Help pupils evaluate AI-supported decisions from the perspective of people affected—not only technical performance.

3 lessons 67 min totalSelf-paced
Demo class context: the recommendation below is based on fictional data.
Module learning sequence

Lessons and classroom practice

0 of 3 complete
Your focus summary

Prioritise these sections

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Key idea

Responsible data use asks whether data should be used, not only whether it can be accessed.

  • Trace where data came from and why it was collected
  • Distinguish consent from mere availability
  • Apply data minimisation to classroom scenarios
  • Publicly visible data is free to reuse for any purpose.
  • Anonymous means impossible to identify.
  • Consent removes all responsibility from the organisation.

Follow the data journey

Identify who created the data, the original purpose, how it was labelled, who can access it, how long it remains, and whether people could meaningfully refuse.

Collect less

Data minimisation means using the least sensitive information needed for a clear purpose. Future usefulness is not an unlimited justification.

Consent has conditions

Valid consent should be informed, specific, freely given, and reversible. Power differences in schools mean consent alone may not make a practice fair or appropriate.

Data journey map

30 minutes
Prepare: An age-appropriate app scenario · Data journey template
  1. List every requested data item.
  2. Map collection, storage, sharing, and deletion.
  3. Classify each item as essential, helpful, or unnecessary.
  4. Redesign the service using less data and a clearer consent moment.
Younger or less experienced

Use picture icons for name, voice, location, and photo.

Older or more experienced

Add retention periods and consider whether combined fields could re-identify someone.

Evidence to collect

A redesigned data flow with a stated purpose and minimisation decisions.

Check your understanding

Answer each prompt to yourself, then mark whether you can explain it confidently. These are reflective checks, not scored questions.

  1. What exact purpose requires each field?
  2. Could the goal be met with less sensitive data?
  3. Can a person understand and reverse the choice?
Reflect

Which classroom data practices have become routine without being revisited?

Try next

Audit one digital activity with the question: ‘What is the minimum data this learning purpose needs?’

Ready to put it into practice?

Choose a classroom action above, then mark the lesson complete.

Rozum for Educators · Computational Thinking & AI readiness, aligned to the CBSE CT-AI curriculum.