# COURSE SUMMARY & ENROLLMENT PARAMETERS

**Course:** AI Ethics, Law, & Digital Citizenship
**Duration:** 1 Semester
**Target Level:** Grades 9–10
**Credit Allocation:** 0.5 (semester credit)
**Prerequisite:** None

A zero-code humanities/tech hybrid that explores the real-world impact of technology. Students debate AI copyright law, data privacy, algorithm bias, facial-recognition surveillance, and the social impact of automation. The course is the natural partner to *Introduction to Computational Logic*, extending the same **Code Auditor** mindset into law, ethics, and civic reasoning — auditing systems and claims rather than writing code.

# INSTITUTIONAL RUNWAY & PACING STRATEGY

## The Four Thematic Modules (25% each)

| Module | Thematic Focus | Runtime Share |
|---|---|---|
| Module 1 | Data Privacy & Algorithm Bias | 25% |
| Module 2 | AI Copyright & Intellectual Property | 25% |
| Module 3 | Surveillance, Facial Recognition & Civil Liberties | 25% |
| Module 4 | Automation & the Future of Work | 25% |

Each module consumes one quarter of the semester runway, independent of the calendar. This guarantees full coverage whether the semester runs 15 or 18 weeks.

## Systemic Buffer Block (The Flex Zone)

A **Systemic Buffer Block** closes the semester. On a 15–18 week schedule, each module runs ~3.5–4.5 weeks; the Flex Zone absorbs the residual 1–3 weeks for debate finales and portfolio assembly.

# SYSTEMIC MODULE DECONSTRUCTIONS

## Module 1: Data Privacy & Algorithm Bias

- **Core Objectives:** Analyze how personal data is collected and how algorithms encode bias.
- **Architectural Concepts:** Data lifecycles, algorithmic fairness, and privacy tradeoffs.
- **The AI-Assisted Classroom Workflow Strategy:** Students analyze a real algorithm for bias and debate its privacy implications, fact-checking every claim.

## Module 2: AI Copyright & Intellectual Property

- **Core Objectives:** Evaluate who owns AI-generated work and how copyright law is adapting.
- **Architectural Concepts:** Intellectual property, fair use, and generative-AI legal precedent.
- **The AI-Assisted Classroom Workflow Strategy:** Students debate an AI copyright case and draft a documented position paper.

## Module 3: Surveillance, Facial Recognition & Civil Liberties

- **Core Objectives:** Weigh security benefits against privacy and civil-liberties costs.
- **Architectural Concepts:** Biometric systems, surveillance architecture, and rights frameworks.
- **The AI-Assisted Classroom Workflow Strategy:** Students evaluate a surveillance scenario, mapping tradeoffs with AI-assisted research.

## Module 4: Automation & the Future of Work

- **Core Objectives:** Assess the social and economic impact of automation.
- **Architectural Concepts:** Labor economics, technological displacement, and policy response.
- **The AI-Assisted Classroom Workflow Strategy:** Students research an automation case and produce an evidence-backed impact report.

# PERFORMANCE EVALUATION & GRADING SCHEME

- **System Integration (30%):** Ability to connect legal, ethical, and technical concepts into coherent argumentation.
- **Code Auditing Sheets / Source Audits (35%):** Structured logs documenting how AI-assisted research was fact-checked.
- **Capstone Portfolio (35%):** Position papers and impact reports assessed on argument and evidence.

# PROGRESSIVE ACADEMIC INTEGRITY & AI POLICY

AI is a **research assistant to be audited**. Students log AI-assisted research and verify claims against primary sources. Un-audited AI text a student cannot defend is treated as plagiarism. *You may let the AI draft the argument, but you must own the reasoning and the evidence.*