# COURSE SUMMARY & ENROLLMENT PARAMETERS

**Course:** AP Computer Science Principles (AI-Focused)
**Duration:** 1 Academic Year
**Target Level:** Grades 11–12
**Credit Allocation:** 1.0 (full-year credit)
**Prerequisite:** None (foundational computing literacy recommended)

AP Computer Science Principles is the College Board's flagship introductory computing course, taught here through the lens of the **Code Auditor** model. Syntax is treated as a minor implementation detail delegated to generative AI; the intellectual work of the course lives in system architecture, algorithmic reasoning, data interpretation, and the societal impact of computing. The course aligns with the 2027–28 AP CSP redesign, integrating the new AI Design Project and preparing students for the language-neutral pseudocode exam. Students leave with a portfolio of audited artifacts and the ability to reason about — rather than memorize — computational systems.

# INSTITUTIONAL RUNWAY & PACING STRATEGY

## The Four Thematic Modules (25% each)

| Module | Thematic Focus | Runtime Share |
|---|---|---|
| Module 1 | Computational Thinking & Algorithmic Logic | 25% |
| Module 2 | Data, Information & the Internet | 25% |
| Module 3 | AI, Machine Learning & the AI Design Project | 25% |
| Module 4 | Networks, Internet Safety & Societal Impact | 25% |

Each module consumes exactly one quarter of the total instructional runway, independent of calendar dates or term length. This guarantees the full scope of the College Board framework is covered regardless of school holidays, snow days, or exam-window shifts.

## Systemic Buffer Block (The Flex Zone)

The final segment of the year is reserved as a **Systemic Buffer Block**. On a standard 32–36 week year, each thematic module runs 8–9 weeks; the Flex Zone absorbs the residual 0–4 weeks for portfolio finalization, create-task rehearsal, and targeted review. On compressed schedules, modules compress proportionally and the Flex Zone is the first to yield — never the core content.

# SYSTEMIC MODULE DECONSTRUCTIONS

## Module 1: Computational Thinking & Algorithmic Logic

- **Core Objectives:** Decompose problems, recognize patterns, and evaluate algorithms for correctness and efficiency — without hand-typing syntax.
- **Architectural Concepts:** Abstraction, decomposition, algorithmic validation, and the anatomy of a control-flow structure.
- **The AI-Assisted Classroom Workflow Strategy:** Students prompt a generative model to produce an algorithm, then audit it against a rubric: trace inputs, identify edge cases, and flag where the logic fails. Daily in-class labs are structured as **code audits**, not code authoring.

## Module 2: Data, Information & the Internet

- **Core Objectives:** Interpret how data represents information and how it moves across networks.
- **Architectural Concepts:** Data encoding, structured vs. unstructured data, and the request/response architecture of the web.
- **The AI-Assisted Classroom Workflow Strategy:** Students use AI-assisted tools to clean and visualize a dataset, then verify every derived claim against the source — producing a **data audit sheet** rather than a raw visualization.

## Module 3: AI, Machine Learning & the AI Design Project

- **Core Objectives:** Design an AI solution for a real problem and document the design process.
- **Architectural Concepts:** Supervised vs. unsupervised learning, model inputs/outputs, and bias as an architectural failure mode.
- **The AI-Assisted Classroom Workflow Strategy:** Students prototype with no-code/low-code AI tooling, then write a design justification that audits the model's decisions, limitations, and bias surface.

## Module 4: Networks, Internet Safety & Societal Impact

- **Core Objectives:** Evaluate privacy, bias, and the societal consequences of computing.
- **Architectural Concepts:** Network topology, threat modeling, and the legal/ethical surface of automation.
- **The AI-Assisted Classroom Workflow Strategy:** Students audit AI output for bias and assemble an evidence-backed impact case study, treating the model's claims as hypotheses to verify.

# PERFORMANCE EVALUATION & GRADING SCHEME

Grading is **competency-based**, never memory-based.

- **System Integration (30%):** Ability to connect components into a coherent architecture, demonstrated via diagrams and written rationale.
- **Code Auditing Sheets (35%):** Structured logs where students document what an AI-generated solution does, where it breaks, and how it was validated.
- **Capstone Portfolio (35%):** The Create Task + AI Design Project, assessed on design quality, iteration evidence, and defense.

# PROGRESSIVE ACADEMIC INTEGRITY & AI POLICY

AI is a **required professional tool**, not a shortcut. Students must transparently document every AI interaction (prompt, output, and audit) in a **generation log**. Un-audited, copy-pasted AI output — where the student cannot explain the logic — is treated as plagiarism. The standard is simple: *you may let the AI write the code, but you must be able to defend the architecture.*