Bias and fairness in artificial intelligence are essential concepts for Grade 7 Computer Science students to explore as they examine how AI systems make predictions and decisions. This worksheet focuses on how unbalanced training data can produce unfair outcomes, why representation matters, and how developers and users can evaluate AI systems for fairness and reliability.

Students investigate a realistic school scenario in which an AI tool recommends students for a science club. By examining how missing examples and limited representation can influence recommendations, they connect AI concepts to decisions that may affect real people.

Explore the Ideas Behind Fairer AI

The lesson develops essential vocabulary and reasoning skills through examples of artificial intelligence in use. Students work with the concepts of bias, fairness, representation, training data, outcomes, and human oversight. They consider why a system can produce biased results even when its developers do not intend to treat anyone unfairly.

Students also examine practical ways to investigate and improve AI systems, including gathering relevant examples from varied groups, checking data quality, comparing outcomes across groups, reviewing mistakes, and testing systems after changes. The reading emphasizes that fairness requires considering the purpose of a system, the people affected, and the consequences of automated decisions rather than simply collecting more data or expecting identical results.

What Students Work Through

• AI fairness dialogue that introduces the topic through discussion.
• Matching activity connecting relevant terms and concepts.
• Cloze passage reinforcing key vocabulary in context.
• Labeled diagram activity focused on the relationship between training data, AI processing, and outcomes for different groups.
• Reading comprehension passage explaining AI bias through a school science club recommendation example.
• Multiple-choice questions that assess definitions, interpretation, and practical responses to possible unfairness.
• True-or-false questions that check understanding of representation, evaluation, and human oversight.
• Short-answer responses asking students to investigate overlooked qualified students and suggest ways to improve fairness.
• Extended-response question requiring students to analyze a school leadership program scenario and explain how to investigate bias, improve fairness, and retain human oversight.

Included Resources

• Student worksheet covering the lesson activities and written responses.
• Separate teacher guide PDF containing learning objectives, teaching tips, discussion questions, and common mistakes.

The teacher guide provides practical support for introducing the concepts, guiding classroom discussion, and addressing common misunderstandings. The student activities move from vocabulary and reading comprehension toward evaluating evidence and explaining possible improvements.

Ways to Use This Lesson

Use this Grade 7 AI ethics worksheet during a computer science unit on artificial intelligence, as a guided reading and discussion lesson, or as independent practice after introducing training data and automated decision-making. The reading passage can provide a shared starting point for class discussion, while the short responses and extended-response task give students opportunities to justify their reasoning with examples.

This resource is suited to lessons that ask students to think critically about the limits of AI predictions and the importance of checking outcomes before relying on automated recommendations. It uses a general curriculum framework rather than claiming alignment to a specific named standard.