Recommendation systems and artificial intelligence are explored through real-world digital examples in this Grade 6 Computer Science worksheet. Students investigate how platforms suggest videos, songs, books, games, and products by analyzing previous choices, identifying patterns in data, and applying algorithms. They also examine why personalized suggestions can be useful without always being accurate or complete.

Rather than treating online recommendations as automatically trustworthy, this resource encourages students to consider how automated suggestions influence the content they see. Learners connect the technology behind personalized recommendations with practical digital decision-making, including exploring different subjects, comparing sources, and checking information independently.

What Students Explore

The lesson introduces the vocabulary and processes behind recommendation systems, helping students explain how digital platforms predict what a person might enjoy.

• Recommendation systems: Explore how digital platforms suggest content based on earlier choices and interests.
• Data and patterns: Identify how viewing history, ratings, searches, and other choices can reveal recurring interests.
• Algorithms and suggestions: Explain how algorithms follow rules to process information and generate personalized recommendations.
• Preferences and limitations: Examine why suggestions may be relevant but can become less useful when data is incomplete or interests change.
• Thoughtful digital choices: Evaluate how repeated recommendations can limit exposure to other subjects or viewpoints and identify ways to explore information more broadly.

Activities and Reading Practice

Students begin with a conversation-based activity and matching pairs before completing a vocabulary-focused cloze passage and a labeled diagram. The reading passage, How Digital Platforms Recommend Content, explains recommendation processes through examples involving space videos, music suggestions, viewing history, and similar users' interests.

Multiple-choice questions and true-or-false statements check comprehension of the reading. Short-answer prompts ask students to explain how earlier choices influence suggestions, describe a benefit and a limitation, and propose ways to explore content beyond repeated recommendations. An extended-response question brings these ideas together by asking students to explain how recommendation systems work, discuss their effects, and suggest responsible ways to use digital platforms.

Included Resource

• Grade 6 Computer Science worksheet on recommendation systems and personalized suggestions.
• Teacher Guide provided as a separate PDF, including learning objectives, teaching tips, discussion questions, and common mistakes.

Classroom Uses

Use this artificial intelligence worksheet during a Computer Science lesson on digital platforms, algorithmic decision-making, or personalized online content. The reading and structured activities provide opportunities for individual practice, reading comprehension, written explanations, and class discussion. Teachers can use the discussion questions in the guide to explore how recommendations shape online experiences and why students should evaluate suggestions rather than accept them automatically.

This resource is suitable for classroom instruction, independent practice, or a lesson review on recommendation systems. It uses a general curriculum framework rather than claiming alignment with a specific named standard, making the lesson focus clear for teachers selecting supplemental Grade 6 Computer Science materials.

Why This Topic Matters

Students encounter personalized suggestions across many digital services. Learning how data, preferences, patterns, and algorithms contribute to these suggestions gives them a foundation for evaluating automated content more thoughtfully. This resource connects the underlying technology to a practical question: how can students benefit from relevant recommendations while remaining curious, checking reliability, and making their own decisions?