Overview

Artificial Intelligence & Digital Ethics builds student understanding of responsible AI use in Grade 6 Computer Science under the General framework, with 6 ready-to-teach worksheets.

Students explore how AI systems work, why data privacy matters, and how bias can affect real outcomes. The set emphasizes evidence-based discussion so learners can explain decisions, identify potential risks, and practice responsible digital citizenship when interacting with AI tools.

Across the unit, students connect classroom ideas to everyday technology they may already use, including recommendation features, automated predictions, and data-driven services.

What's Included

• ✅ Responsible AI Use Worksheet Grade 6 | Computer Science Ethics – Students learn classroom rules for using AI responsibly and describe what “responsible” looks like in practice.
• ✅ Privacy and Data Use Worksheet | Grade 6 Computer Science – Learners examine how personal information can be collected, used, and protected when technology makes decisions.
• ✅ Bias in AI Systems Grade 6 Computer Science Digital Ethics Worksheet – Students identify bias sources and explain how unfair patterns can lead to inaccurate or harmful results.
• ✅ Recommendation Systems Worksheet | How AI Suggestions Work Grade 6 – Learners model how suggestions are generated and interpret why certain items appear as recommendations.
• ✅ Machine Learning and Training Data Worksheet | Grade 6 Computer Science – Students connect training data to predictions and practice reasoning about what data teaches a model.
• ✅ What Is Artificial Intelligence Worksheet | Grade 6 Computer Science – Students define AI, compare AI vs. non-AI systems, and classify examples from everyday life.

Skills & Standards

Aligned to a General framework, this bundle strengthens key crosscutting ideas in digital technology: systems thinking, responsible decision-making, and the role of data in outcomes. Students practice disciplinary core ideas such as how AI can be designed to recognize patterns, how training data influences model behavior, and how recommendation systems use inputs to generate outputs.

Throughout the worksheets, learners build modeling and analytical reasoning by describing what an AI system does, tracing cause-and-effect from data to predictions, and evaluating claims using examples. Students also develop evidence-based explanations by citing observations from scenarios, clarifying assumptions, and reflecting on impacts related to privacy, fairness, and safe use.

Specific unit skills include: distinguishing AI tasks from traditional programming; identifying privacy risks in common data uses; recognizing bias as a data/problem-related issue; explaining how recommendation logic shapes suggestions; and connecting machine learning training data to resulting behavior.

Perfect For

• Teachers seeking structured instruction
• Sub plans
• Homework or review
• Intervention or centers
• Assessment preparation

How to Use

Use this as a focused 1–3 week lesson sequence, depending on pacing. Day 1 can introduce AI basics, then follow with training data and machine learning ideas before moving into privacy and bias. Finish with recommendation systems and a culminating discussion on responsible AI use.

Try a short warm-up at the start of each session (example-based “AI or not?” sorting), followed by a brief guided practice on key vocabulary (data, bias, training, recommendations). For checks for understanding, use brief exit tickets tied to each worksheet’s scenario, and schedule an end-of-unit review where students match an AI concept to a real-world example and justify their reasoning.

Closing

With these worksheets, students practice responsible AI use by explaining AI systems, evaluating data-related risks, and connecting learning to real technology choices.