# Computational thinking & problem solving (including declarative, OO and AI)

> CIE A-Level Computer Science · 9618
> Source: https://www.owlsprep.com/study/cie-9618-u13-overview/
> Weight: n/a

This unit covers core computational thinking frameworks and three major problem-solving paradigms, from foundational decomposition to modern AI search techniques, forming the basis for all practical software development.

**Prerequisites:** Basic programming and algorithm fundamentals

## Learning objectives

- Apply core computational thinking principles to decompose and solve complex real-world problems
- Contrast object-oriented and declarative approaches to problem modelling and solution design
- Explain fundamental artificial intelligence concepts and implement common AI search techniques
- Evaluate which problem-solving paradigm is best suited for a given problem context

## Unit at a Glance

This unit follows a logical learning progression: we start with the four core pillars of computational thinking, then move to contrast two widely used problem-solving paradigms (object-oriented and declarative), before applying these core concepts to artificial intelligence problem solving. Each skill builds on the last: decomposition simplifies complex problems, abstraction removes unnecessary detail, pattern recognition speeds up solution design, and paradigms provide structured frameworks to implement working solutions.

This unit is split into the following sub-topics:
- [Problem decomposition](https://www.owlsprep.com/study/cie-9618-u13-problem-decomposition/) — Learn how to break large, complex problems into smaller, independently solvable sub-problems.
- [Abstraction](https://www.owlsprep.com/study/cie-9618-u13-abstraction/) — Understand how to hide irrelevant details to focus on the core requirements of a problem.
- [Pattern recognition](https://www.owlsprep.com/study/cie-9618-u13-pattern-recognition/) — Identify repeating patterns across problems to reuse existing, proven solution approaches.
- [Object-oriented problem solving](https://www.owlsprep.com/study/cie-9618-u13-object-oriented-problem-solving/) — Model real-world problems using objects, classes, inheritance, and encapsulation principles.
- [Declarative problem solving](https://www.owlsprep.com/study/cie-9618-u13-declarative-problem-solving/) — Contrast declarative (what to solve) and imperative (how to solve) approaches to problem modelling.
- [Artificial intelligence fundamentals](https://www.owlsprep.com/study/cie-9618-u13-artificial-intelligence-fundamentals/) — Learn core AI concepts including knowledge representation and intelligent agent design.
- [AI search techniques](https://www.owlsprep.com/study/cie-9618-u13-ai-search-techniques/) — Explore uninformed and informed search methods for solving state-space AI problems.

## Common pitfalls

- **Wrong:** Confusing abstraction with just deleting information from a problem
  - Why it fails: Abstraction is intentional: it hides irrelevant details for a specific use case, not just random information
  - Correct: Always document which details you are hiding and why they are not needed for your solution
- **Wrong:** Treating OOP and declarative approaches as interchangeable for all problem types
  - Why it fails: Each paradigm is optimized for specific problem structures, and using the wrong one leads to overcomplicated solutions
  - Correct: Evaluate the problem structure and requirements before selecting a problem-solving paradigm
- **Wrong:** Thinking computational thinking only applies to coding problems
  - Why it fails: Computational thinking is a general problem-solving framework used across many disciplines beyond software development
  - Correct: Practice applying decomposition and abstraction to non-coding problems to build your intuition

## Cheatsheet

| Concept | Key Summary |
| --- | --- |
| Problem Decomposition | Breaking a complex problem into smaller, independent solvable sub-problems |
| Abstraction | Hiding unnecessary details to reduce complexity and focus on core problem requirements |
| Pattern Recognition | Identifying shared characteristics across problems to reuse proven solution designs |
| Object-Oriented Paradigm | Model problems around entities (objects) that combine state (data) and behavior (methods) |
| Declarative Paradigm | Specify what problem to solve, not the step-by-step procedure to solve it |
| Uninformed Search | AI search that uses no problem-specific heuristics to guide path selection |
| Informed Search | AI search that uses heuristics to prioritize more promising solution paths |

## What's next

Start your learning with the foundational skill of problem decomposition, the first core pillar of computational thinking. Once you complete all sub-topics in this unit, you will be ready to apply these problem-solving principles to advanced algorithm design in the next unit of the syllabus.

- [Problem decomposition](https://www.owlsprep.com/study/cie-9618-u13-problem-decomposition/)
- [Abstraction](https://www.owlsprep.com/study/cie-9618-u13-abstraction/)
- [Pattern recognition](https://www.owlsprep.com/study/cie-9618-u13-pattern-recognition/)

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