Modern Java using AI Coding Assistants
Description
This hands-on course introduces Java programming fundamentals while integrating AI-assisted development practices throughout the learning journey. Participants build core Java skills, while leveraging AI as a pair programming tool for code generation, refactoring, review, and validation. The program emphasizes critical thinking, code quality, and responsible AI usage in modern software development workflows.
Indicative Duration: 48 training hours
*Duration is adjusted based on the final scope and the target audience.
Scope
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1. Development Environment & AI-Assisted Coding
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• Java syntax • JVM • CLI projects AI Practice: AI Pair programming intro • Verifying AI outputGit basics, commits, branches • Maven project structure & dependencies • AI Practice: AI-assisted Git/Maven setup |
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2. Java Language Fundamentals
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• Primitives • References • Loops • Conditionals • AI Practice: Using AI to generate alternative implementations, Code Clarity • Classes • Constructors • Access modifiers • Immutability • AI Practice: Using to generate domain model suggestions, Critique Design |
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| 3. Object-Oriented Programming | • Inheritance • Interfaces • SOLID intro • Code smells • AI Practice: AI for code review, refactoring recommendations |
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| 4. Core Java APIs | • Collections • Generics • Streams API • Functional style • AI Practice: Using AI to Convert loops → streams, detect inefficiencies |
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| 5. Error Handling | • Exception handling: checked/unchecked • Custom exceptions • AI Practice: Using AI to generate edge-case handling & validation |
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| 6. I/O & Serialization | • File I/O • Manual JSON • Resource management • AI Practice: Using AI to optimize file ops, detect unsafe resource handling |
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| 7. Database Access with JDBC | • JDBC basics • Connections • CRUD operations • AI Practice: Using AI to generate queries, DAOs, verify AI SQL suggestions |
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| 8. Testing Without Frameworks | • Testing without frameworks • Assertions • Test harness • AI Practice: Using AI to generate unit tests, edge case coverage |
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| 9. Performance & Refactoring | • Simple benchmarking • Big-O awareness • AI Practice: AI suggests algorithm improvements |
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| 10. Capstone Project | • CLI system (domain + JDBC + I/O + tests) • AI Practice: Scaffold, generate tests, review code |
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Learning Objectives
Upon completion of the course participants will be able to:
- Develop structured Java applications using core language features, OOP principles, and modular project organization
- Use AI tools effectively for code generation, refactoring, debugging, and test creation
- Critically evaluate and verify AI-generated code for correctness, performance, and design quality
- Implement data persistence using file handling and JDBC-based database integration
- Apply testing, benchmarking, and code quality practices within an AI-assisted development workflow
Target Audience
- Roles: Software Engineers, Backend Developers, Java Developers
- Seniority: Junior to Mid Level Professionals
Prerequisite Knowledge
- Basic understanding of programming concepts
- No prior Java experience required (if positioned as foundation course)
Delivery Method
Sessions can be delivered via the following formats:
- Live Online – Interactive virtual sessions via video conferencing
- On-Site – At your organization’s premises
- In-Person – At Code.Hub’s training center
- Hybrid – A combination of online and in-person sessions
