# COURSE SUMMARY & ENROLLMENT PARAMETERS

**Course:** Introduction to Computational Logic & AI Prompt Engineering
**Duration:** 1 Semester
**Target Level:** Grades 9–10
**Credit Allocation:** 0.5 (semester credit)
**Prerequisite:** None

A foundational elective that introduces the entry-level architecture of computation — variables, loops, conditionals, and functions — without syntax pressure. The course teaches computational logic through the **Code Auditor** lens: students read, trace, and evaluate code rather than memorize it. It pairs digital literacy, cloud architecture awareness, and prompt-engineering frameworks for academic research, preparing students to use AI as a disciplined tool rather than a black box.

# INSTITUTIONAL RUNWAY & PACING STRATEGY

## The Four Thematic Modules (25% each)

| Module | Thematic Focus | Runtime Share |
|---|---|---|
| Module 1 | Logic Foundations — Variables, Loops & Conditionals | 25% |
| Module 2 | Functions, Structure & Cloud Architecture | 25% |
| Module 3 | Prompt Engineering for Academic Research | 25% |
| Module 4 | Digital Literacy & Responsible AI | 25% |

Each module consumes one quarter of the semester runway, independent of calendar dates. 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 portfolio assembly and project polish. Under compression, the Flex Zone yields first — never the core logic content.

# SYSTEMIC MODULE DECONSTRUCTIONS

## Module 1: Logic Foundations — Variables, Loops & Conditionals

- **Core Objectives:** Trace and predict how variables, loops, and conditionals control program flow.
- **Architectural Concepts:** Control flow, state, and the anatomy of an algorithm.
- **The AI-Assisted Classroom Workflow Strategy:** Students prompt AI to produce a small program, then trace its execution and audit its logic for errors.

## Module 2: Functions, Structure & Cloud Architecture

- **Core Objectives:** Decompose problems into reusable functions and understand how programs run in the cloud.
- **Architectural Concepts:** Functions, inputs/outputs, and a first look at servers, storage, and services.
- **The AI-Assisted Classroom Workflow Strategy:** Students refactor a sequence into functions with AI and map its cloud architecture.

## Module 3: Prompt Engineering for Academic Research

- **Core Objectives:** Write precise, iterative prompts and evaluate AI output for research and study.
- **Architectural Concepts:** Prompt structure, few-shot reasoning, and output verification.
- **The AI-Assisted Classroom Workflow Strategy:** Students build and test a research prompt library, auditing every output against reliable sources.

## Module 4: Digital Literacy & Responsible AI

- **Core Objectives:** Evaluate sources, spot misinformation, and apply responsible AI practices.
- **Architectural Concepts:** Source credibility, privacy, and bias detection.
- **The AI-Assisted Classroom Workflow Strategy:** Students fact-check AI output and audit a source for bias.

# PERFORMANCE EVALUATION & GRADING SCHEME

- **System Integration (30%):** Ability to connect logic, functions, and cloud concepts into coherent explanations.
- **Code Auditing Sheets (35%):** Structured trace-and-audit logs documenting how student logic was validated.
- **Capstone Portfolio (35%):** A research prompt toolkit and digital-literacy field guide.

# PROGRESSIVE ACADEMIC INTEGRITY & AI POLICY

AI is a **learning instrument to be audited**. Students log every AI interaction and verify outputs against sources. Un-audited AI text a student cannot explain is treated as plagiarism. *You may let the AI write the code, but you must be able to trace its logic.*