Skarp
From Hand-Coded to AI-Co-Created: Comparing Traditional and AI-Enhanced Programming Paradigms
This course explores how software development is changing as AI-assisted coding tools become mainstream. You will compare traditional programming workflows with emerging AI-enhanced paradigms, analyze their trade-offs, and learn how to reason about quality, security, and responsibility when humans and AI co-create code.
Course Content
8 modules · 2h total
Mapping the Landscape: Traditional vs AI-Enhanced Programming
Introduce the core idea of programming paradigms and contrast classic human-centric coding with today’s AI-assisted and agentic approaches.
Traditional Programming Paradigms and Workflows
Review core traditional paradigms (procedural, object-oriented, functional) and the standard tools and practices that shaped pre-AI development.
AI-Assisted Programming: Copilots, Code Generators, and Intelligent IDEs
Examine the current generation of AI coding assistants and how they embed into editors, terminals, and pipelines to augment traditional programming.
From Copilot to Co-Creator: AI-Enhanced Programming Paradigms
Explore how AI shifts programming from writing code line-by-line to steering systems, designing prompts, and orchestrating agents—creating new paradigms like AI-native and agent-first development.
Human–AI Collaboration Patterns in Coding
Analyze concrete collaboration patterns between developers and AI, including when to rely on AI, when to override it, and how to combine human strengths with machine strengths.
Quality, Security, and Trust: Comparing Risks in Traditional and AI-Generated Code
Compare how defects and vulnerabilities arise in traditional code versus AI-generated code, and examine emerging evidence on security, verification debt, and trust in AI outputs.
Verification, Testing, and Governance in AI-Enhanced Development
Focus on how testing, code review, and governance practices must evolve to safely incorporate AI-generated code into production systems.
Ethics, Responsibility, and the Future of Programming Work
Examine the broader ethical, professional, and societal implications of moving from traditional to AI-enhanced programming paradigms, including skills, jobs, and accountability.
Read the Textbook
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In software development, two ideas help us **map the landscape**:
1. **Programming paradigm** A programming paradigm is a *style or model of thinking about and structuring programs*. It influences: - How you decompose problems (e.g., objects vs functions vs data flows) - How you represent state and behavior - How you reason about correctness and performance
Classic examples (which you may already know): - **Imperative** (e.g., C, Python) – step-by-step instructions - **Object-oriented** (e.g., Java, C#) – objects with state and behavior - **Functional** (e.g., Haskell, modern JavaScript) – pure functions and immutability