Data and AI

Lesson Overview

This 60-minute session is the third in a six-lesson unit titled Introduction to Artificial Intelligence. It focuses on developing students’ understanding of how data underpins AI systems, aligning with the National Curriculum for Computing at Key Stage 3 (Years 9-10) in England. The lesson will engage students in practical data collection, cleaning, and analysis, emphasising the critical role of data quality and quantity in AI outcomes.

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National Curriculum Links

Computing – Key Stage 3 (Years 9-10)

• Programmes of Study: Computational thinking, algorithms and programming
  • “Use logical reasoning to explain how some simple algorithms work and to detect and correct errors.”
• Programmes of Study: Data representation and manipulation
  • “Understand how data of various types can be structured and designed to be processed by algorithms.”
• Programmes of Study: Impacts of digital technology
  • “Recognise the ways in which data is collected, stored, and analysed and the implications for individuals’ privacy.”

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Learning Objectives

By the end of this lesson, students will be able to:

1. Identify and categorise different types of data (structured, unstructured, quantitative, qualitative) used in AI systems.
2. Discuss why data quality (accuracy, completeness, relevance) and data quantity impact AI model performance.
3. Demonstrate practical skills in collecting, cleaning, and manipulating data using spreadsheets.

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Success Criteria

• Students can accurately classify given data examples into types relevant for AI.
• Students engage in discussion covering the importance of clean, sufficient data for AI accuracy and fairness.
• Students collaboratively collect survey data, identify errors or inconsistencies, and clean the dataset in Google Sheets.
• Students present their understanding clearly through group summaries.

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Learning Activities

Time	Activity	Description	Resources	Curriculum Links
0-10 min	Starter: Exploring Data Types	Brief teacher-led discussion introducing: - Structured vs. unstructured data - Quantitative vs. qualitative data - Examples from AI systems (e.g. image data, text data, sensor data)	Whiteboard or projector	Understanding data types
10-20 min	Group Survey Design and Data Collection	In groups of 5, students create a simple survey (e.g. favourite film genre, mode of transport to school). They collect data from peers or other groups, entering responses into a shared Google Sheet. This fosters real-time collaboration and data entry experience.	Paper templates for survey, Google Sheets	Data representation and manipulation
20-30 min	Data Cleaning Workshop	Groups review their raw data, guided by the teacher to: - Identify missing data - Detect inconsistent or invalid responses - Apply cleaning techniques (e.g. removing duplicates, correcting errors) using Google Sheets features (filtering, conditional formatting). Discuss how poor data quality could affect AI results.	Google Sheets, projector	Logical reasoning, error detection
30-45 min	Class Discussion: Why Data Matters in AI	Teacher-led dialogue expanding on key issues: - Importance of data quantity (sample size, bias) - Who collects data and ethical considerations - Impact of noisy or biased data on AI fairness and accuracy	Article on data in AI (printed or displayed)	Impacts of digital technology, ethical considerations
45-60 min	Group Presentations and Reflection	Each group presents: - Types of data collected - Cleaning steps taken - Why data quality and quantity matter for AI systems Discuss future lessons and reinforce key learning.	Presentation materials or verbal	Communication of technical concepts

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Assessment

• Formative: Observation of group collaboration during data collection and cleaning.
• Summative: Group presentations assessed against a simple rubric:
  • Correct identification of data types (5 marks)
  • Explanation of data cleaning methods and rationale (5 marks)
  • Discussion of data quality/quantity impact on AI (5 marks)
  • Quality and clarity of presentation (5 marks)

Rubric shared in advance to guide students’ work.

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Resources and Preparation

• Print or digital copy of a concise article explaining the role of data in AI (age-appropriate, jargon-light, approx. 1 page)
• Google accounts and devices for each group to access Google Sheets
• Projector and whiteboard for teacher instructions
• Survey templates (simple, printable)
• Lesson plan, assessment rubric

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Teacher Tips

• Use relatable, real-world examples of AI from students’ experiences (e.g. recommendation systems on streaming platforms, voice assistants).
• Encourage critical thinking about data privacy and bias as a bridge to later units covering AI ethics.
• Demonstrate simple Google Sheets functions before the cleaning activity to ensure all students can participate effectively.
• Incorporate questioning techniques to stimulate student-led discussion about data’s role beyond AI (e.g. in social media or healthcare).

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This lesson plan provides a rigorous yet approachable introduction to the critical role data plays in the function of AI, perfectly aligned with the demands and spirit of the National Curriculum for Computing in England. Teachers can confidently engage Year 9-10 students in hands-on, collaborative learning that builds foundational digital literacy skills while priming them for more advanced AI concepts.