Weekend batches
October 2026
09:00–15:00 · flexible hours
$850per learner
24 Oct 2026
4 of 25 seats available
31 Oct 2026
10 of 25 seats available
Generative AI
GitHub Copilot for Developers in Kiribati
GitHub Copilot for Developers training in Kiribati provides practical skills to use AI-powered coding assistance for software development. Learn GitHub Copilot features, code generation, code completion, prompt engineering, debugging, refactoring, testing, documentation, and developer productivity. This course helps developers integrate AI into modern development workflows, improve coding efficiency, and build quality applications while understanding responsible AI practices and effective GitHub Copilot usage.
40 Jours/2 daysIntermediate4.3 · 1,000 reviews
Training calendar
Upcoming four-month course schedule
A new weekday and weekend batch starts every week. Each batch runs for 15 training days, based on the course duration of 40 Jours/2 days.
Weekend batches
November 2026
09:00–15:00 · flexible hours
$850per learner
07 Nov 2026
25 of 25 seats available
14 Nov 2026
25 of 25 seats available
21 Nov 2026
25 of 25 seats available
28 Nov 2026
25 of 25 seats available
Weekend batches
December 2026
09:00–15:00 · flexible hours
$850per learner
05 Dec 2026
25 of 25 seats available
12 Dec 2026
25 of 25 seats available
19 Dec 2026
25 of 25 seats available
26 Dec 2026
25 of 25 seats available
Weekend batches
January 2027
09:00–15:00 · flexible hours
$850per learner
02 Jan 2027
25 of 25 seats available
09 Jan 2027
25 of 25 seats available
16 Jan 2027
25 of 25 seats available
23 Jan 2027
25 of 25 seats available
30 Jan 2027
25 of 25 seats available
Course curriculum
What you’ll learn
40 Jours/2 daysIntermediate
Module 1: Introduction to GitHub Copilot
- Understanding GitHub Copilot – Learn the fundamentals, purpose, capabilities, and role of GitHub Copilot in modern software development in Kiribati.
- AI-Assisted Software Development – Explore how AI can support developers across coding, debugging, testing, documentation, and software delivery workflows in Kiribati.
- GitHub Copilot Plans and Access – Understand Copilot access, plans, supported environments, and organizational considerations in Kiribati.
Module 2: Setting Up GitHub Copilot
- GitHub Copilot Account Setup – Configure GitHub Copilot and understand the requirements for accessing its development features in Kiribati.
- IDE Integration – Set up GitHub Copilot in supported development environments and configure the developer workspace in Kiribati.
- Copilot Configuration – Explore settings, permissions, suggestions, and usage preferences for effective AI-assisted development in Kiribati.
Module 3: GitHub Copilot Code Suggestions
- Inline Code Completion – Learn how Copilot generates contextual coding suggestions while developers write code in Kiribati.
- Natural-Language Code Generation – Use comments and natural-language instructions to generate functions, classes, snippets, and application components in Kiribati.
- Next Edit Suggestions – Explore AI-assisted suggestions for potential changes and improvements during development in Kiribati.
Module 4: GitHub Copilot Chat
- Introduction to Copilot Chat – Learn how conversational AI can assist with programming questions, development tasks, and technical explanations in Kiribati.
- Code Explanation and Analysis – Use Copilot Chat to understand unfamiliar code, files, repositories, and programming concepts in Kiribati.
- Context-Aware Developer Assistance – Learn how to provide relevant context to obtain more useful coding responses in Kiribati.
Module 5: Prompt Engineering for Developers
- Developer Prompting Fundamentals – Learn how to create clear and specific prompts for generating reliable coding assistance in Kiribati.
- Context and Constraints – Understand how requirements, project context, coding standards, and constraints influence AI-generated responses in Kiribati.
- Iterative Prompting Techniques – Practice refining prompts and reviewing responses to improve development outcomes in Kiribati.
Module 6: AI-Assisted Programming
- Code Generation Techniques – Generate application logic, reusable functions, data structures, and programming components with GitHub Copilot in Kiribati.
- Multi-Language Development – Explore AI-assisted development across commonly used programming languages and frameworks in Kiribati.
- API and Query Assistance – Use Copilot to assist with API usage, database queries, and infrastructure-as-code development in Kiribati.
Module 7: Code Understanding and Refactoring
- Code Explanation – Analyze existing implementation logic and improve understanding of complex codebases in Kiribati.
- Refactoring Assistance – Use AI assistance to explore cleaner, more maintainable, and more readable code structures in Kiribati.
- Code Transformation – Apply Copilot to modify and adapt existing code according to development requirements in Kiribati.
Module 8: Debugging and Problem Solving
- Bug Identification – Use GitHub Copilot to investigate potential causes of coding errors and application issues in Kiribati.
- Error Analysis – Provide error messages, logs, and relevant context to support AI-assisted troubleshooting in Kiribati.
- Solution Validation – Review, test, and validate AI-generated fixes before applying them to production applications in Kiribati.
Module 9: AI-Assisted Testing
- Unit Test Generation – Use GitHub Copilot to create unit testing code for application components in Kiribati.
- Test Scenario Development – Generate test scenarios and edge cases based on application requirements in Kiribati.
- Test Improvement – Use AI assistance to enhance test coverage, readability, and maintainability while validating generated tests in Kiribati.
Module 10: Documentation with GitHub Copilot
- Code Documentation – Generate comments, explanations, and technical documentation for application code in Kiribati.
- README Development – Use Copilot to assist with project documentation and developer onboarding materials in Kiribati.
- Technical Content Generation – Create structured explanations and documentation while reviewing AI-generated content for accuracy in Kiribati.
Module 11: GitHub Copilot Agent Mode
- Understanding Agent Mode – Explore how Copilot can work through multi-step development tasks within supported IDE environments in Kiribati.
- Task Planning and Execution – Provide high-level requirements and allow Copilot to analyze files, propose changes, and work through development tasks in Kiribati.
- Reviewing Agent Changes – Learn how to inspect generated edits and proposed commands before accepting changes in Kiribati.
Module 12: Copilot Customization and Instructions
- Custom Instructions – Configure project-specific instructions to guide Copilot responses according to development standards in Kiribati.
- Repository Context – Provide relevant project information and conventions to improve AI-assisted development results in Kiribati.
- Reusable Prompts and Customization – Explore methods for tailoring Copilot to specific development workflows and project requirements in Kiribati.
Module 13: GitHub Copilot and Software Development Workflows
- Issue-Based Development – Explore how Copilot can assist developers in understanding and implementing tasks originating from GitHub issues in Kiribati.
- Pull Request Workflows – Learn how AI assistance can support pull request preparation, summaries, and development activities in Kiribati.
- AI-Assisted Software Lifecycle – Understand how Copilot can support development activities from planning through implementation and review in Kiribati.
Module 14: GitHub Copilot Code Review
- AI-Assisted Code Review – Learn how GitHub Copilot can review pull requests and provide suggestions for potential improvements in Kiribati.
- Reviewing Suggested Changes – Evaluate AI-generated review comments and determine which recommendations should be applied in Kiribati.
- Repository Review Instructions – Understand how repository-specific instructions can help guide Copilot code reviews according to project standards in Kiribati.
Module 15: GitHub Copilot CLI and Developer Tools
- Copilot CLI Fundamentals – Explore GitHub Copilot CLI capabilities for AI-assisted development tasks from the command line in Kiribati.
- Command Assistance – Learn how natural-language interaction can support command-line development workflows in Kiribati.
- Developer Workflow Integration – Apply Copilot-assisted command-line techniques to practical development scenarios in Kiribati.
Module 16: MCP and AI Tool Integration
- Model Context Protocol Fundamentals – Understand how MCP can extend AI coding workflows with external tools and contextual information in Kiribati.
- MCP Server Integration – Explore how supported MCP servers can provide additional capabilities to GitHub Copilot workflows in Kiribati.
- Tool-Enabled Development – Learn how AI agents can interact with external tools while maintaining developer oversight and review in Kiribati.
Module 17: Responsible and Secure AI-Assisted Development
- Reviewing AI-Generated Code – Learn why developers must validate generated code for correctness, quality, maintainability, and security before use in Kiribati.
- Security and Privacy Considerations – Understand important security, privacy, intellectual property, and organizational considerations when using AI coding tools in Kiribati.
- Responsible AI Practices – Apply responsible practices when incorporating AI-generated content and code into software development workflows in Kiribati.
Module 18: Hands-On GitHub Copilot Developer Project
- Project Planning – Define application requirements and create an AI-assisted development plan using GitHub Copilot in Kiribati.
- Application Development – Build application components using code suggestions, Copilot Chat, prompting, debugging, testing, and agent-assisted workflows in Kiribati.
- Testing and Code Review – Validate the application, review AI-generated code, improve implementation quality, and prepare the project for delivery in Kiribati.
- Project Presentation – Demonstrate the completed GitHub Copilot project and explain the development approach, AI-assisted techniques, and validation process in Kiribati.
Entry requirements and prerequisites
- Programming Fundamentals – Basic understanding of programming concepts, variables, functions, data structures, and software development is recommended for GitHub Copilot training in Kiribati.
- Programming Language Knowledge – Familiarity with at least one programming language such as Python, Java, JavaScript, C#, or TypeScript is helpful for effectively using GitHub Copilot in Kiribati.
- Software Development Basics – Understanding of application development, debugging, testing, and common software development practices will help learners get the most from the course in Kiribati.
- Git and GitHub Fundamentals – Basic knowledge of Git repositories, commits, branches, pull requests, and GitHub workflows is recommended for GitHub Copilot for Developers training in Kiribati.
- IDE Familiarity – Experience with a supported development environment such as Visual Studio Code, Visual Studio, JetBrains IDEs, or other compatible editors is beneficial in Kiribati.
- Basic Command-Line Knowledge – Familiarity with terminals and basic command-line operations can help learners work efficiently with development projects and GitHub repositories in Kiribati.
- AI and Generative AI Awareness – Basic understanding of artificial intelligence, generative AI, prompts, and AI-assisted development is useful but not mandatory for learners in Kiribati.
Career value
Reasons to choose this course
Learn AI-Assisted Software Development
Understand how GitHub Copilot can support developers throughout coding and software development workflows in Kiribati.
Gain Practical GitHub Copilot Experience
Practice using Copilot features in real development scenarios instead of relying only on theoretical concepts in Kiribati.
Work With Multiple Programming Languages
Learn how to apply AI coding assistance across commonly used programming languages and development projects in Kiribati.
Improve Code Understanding
Use GitHub Copilot to explain existing code, explore unfamiliar implementations, and support faster code comprehension in Kiribati.
Learn AI-Powered Code Generation
Develop effective techniques for generating functions, classes, snippets, and application components with AI assistance in Kiribati.
Strengthen Debugging and Problem Solving
Learn approaches for using GitHub Copilot to investigate coding issues and explore potential solutions in Kiribati.
Advantages
Turn learning into practical capability
Increase Development Productivity
Learn how to incorporate AI-assisted development into everyday programming workflows to reduce repetitive coding effort and focus more on solution design and implementation in Kiribati.
Support Faster Application Prototyping
Use GitHub Copilot to transform natural-language requirements and development ideas into initial code structures, helping developers explore application concepts more efficiently in Kiribati.
Improve Codebase Navigation and Understanding
Develop techniques for using AI assistance to understand unfamiliar files, repositories, functions, and implementation patterns, making it easier to work with complex codebases in Kiribati.
Enhance Software Development Collaboration
Learn how AI-assisted documentation, explanations, pull request support, and development workflows can help teams communicate technical information more effectively in Kiribati.
Strengthen Modern Development Workflow Skills
Gain exposure to AI-assisted approaches that support planning, implementation, code modification, validation, and review across the software development lifecycle in Kiribati.
Prepare for AI-Augmented Software Engineering
Build practical knowledge of emerging AI-assisted development practices, including agentic workflows and AI-supported code review, helping professionals adapt to evolving developer roles in Kiribati.
Exam format & assessment
- Exam Level: Intermediate – GitHub Copilot for Developers in Kiribati
- Exam Type: Online computer-based assessment covering GitHub Copilot, AI-assisted software development, prompting, code generation, debugging, testing, code review, responsible AI, and developer productivity in Kiribati.
- Number of Questions: 40 questions designed to evaluate practical knowledge and understanding of GitHub Copilot development workflows in Kiribati.
- Exam Duration: 90 minutes to complete the online assessment in Kiribati.
- Passing Score: 70% or 28 out of 40 questions must be answered correctly to pass the StepMerit course assessment.
- Question Format: Multiple-choice, scenario-based, and practical knowledge questions covering real-world GitHub Copilot development situations in Kiribati.
- Core Exam Topics: GitHub Copilot fundamentals, IDE integration, code generation, Copilot Chat, prompt engineering, debugging, testing, documentation, refactoring, agent mode, code review, customization, MCP, security, privacy, and responsible AI in Kiribati.
- Practical Assessment: Scenario-based questions evaluate the learner's ability to select appropriate GitHub Copilot features and apply AI-assisted development techniques to software engineering challenges in Kiribati.
- Assessment Delivery: The exam is conducted online, allowing learners to demonstrate their GitHub Copilot knowledge through a structured digital assessment in Kiribati.
- Course Certification: Successful candidates receive a StepMerit Learning Course Completion Certificate for GitHub Copilot for Developers training in Kiribati.
- Official Microsoft Certification: The Microsoft GitHub Copilot certification (GH-300) is a separate official certification exam. It is currently an intermediate-level certification with a 100-minute exam duration and a passing score of 700 or higher.
Bring your learning together
Course conclusion
GitHub Copilot for Developers training in Kiribati provides practical knowledge to integrate AI-powered coding assistance into modern software development workflows. Learners gain hands-on skills in code generation, Copilot Chat, prompt engineering, debugging, testing, refactoring, documentation, code review, agent-assisted development, and AI tool integration. The course helps developers use GitHub Copilot effectively while understanding the importance of reviewing AI-generated code, security, privacy, and responsible AI practices. With practical exercises and real-world development scenarios, GitHub Copilot training can help professionals improve developer productivity, strengthen AI-assisted programming skills, and prepare for evolving software engineering opportunities in Kiribati.
Target audience
- Software Developers
- Software Engineers
- Full Stack Developers
- Frontend and Backend Developers
- AI Application Developers
- DevOps and Cloud Developers
- Application Developers
- Web Developers
- Technical Leads and Development Team Members
- GitHub and Git Users
- AI-Assisted Development Professionals
- AI Enthusiasts and Career Switchers
Verified learning evidence
Expertise, recognition and learner outcomes
Course-specific expertise
- Professional & Training Experience: 5+ Years
- Course Training Experience: 120+ Hours of training focused on GitHub Copilot, AI-assisted software development, code generation, code completion, prompt engineering, code explanation, debugging, testing, refactoring, documentation, and developer productivity.
- Candidates Trained: 180+ candidates, including software developers, programmers, software engineers, IT professionals, application developers, DevOps professionals, technical leads, students, and AI/technology professionals.
- Training Delivery Experience: Experienced in instructor-led online and classroom training with live demonstrations, hands-on coding labs, interactive exercises, assignments, coding challenges, real-world development scenarios, and project-based learning.
- GitHub Copilot Expertise: Practical experience using GitHub Copilot for AI-assisted coding, code completion, code generation, code explanation, documentation, debugging, refactoring, unit testing, and improving software development productivity.
- AI-Assisted Code Development: Hands-on experience demonstrating how developers can use GitHub Copilot to generate application code, functions, classes, scripts, API components, database-related code, and repetitive programming tasks using natural-language instructions.
- Prompt & Context Optimization: Experience designing effective prompts and providing appropriate code context, project requirements, coding standards, and instructions to improve the relevance, accuracy, and quality of Copilot-generated suggestions.
- Code Explanation & Documentation: Practical experience using GitHub Copilot to explain existing code, document functions and classes, generate comments, prepare technical documentation, and help developers understand unfamiliar codebases.
- Debugging & Error Resolution: Experience using GitHub Copilot to identify potential coding issues, analyse errors, suggest fixes, troubleshoot programming problems, and support developers during debugging activities.
- Software Testing: Training and project experience using GitHub Copilot to generate unit tests, test cases, test scenarios, test data, and testing-related code while emphasizing developer review and validation.
- Code Refactoring & Optimization: Practical knowledge of using Copilot to improve existing code through refactoring, simplification, readability improvements, maintainability enhancements, and identification of potential coding improvements.
- Application & API Development: Experience applying GitHub Copilot to application development scenarios involving functions, APIs, backend services, frontend components, automation scripts, integrations, and other software development requirements.
- Developer Productivity: Training experience demonstrating how GitHub Copilot can reduce repetitive coding effort, accelerate development tasks, support learning, improve documentation, assist with testing, and enhance overall developer productivity.
- Secure & Responsible AI-Assisted Development: Training experience covering secure coding practices, validation of AI-generated code, protection of sensitive information, dependency and code review considerations, responsible AI usage, intellectual-property awareness, and developer accountability.
- Hands-On Project Guidance: Experience guiding learners through end-to-end software development projects involving GitHub Copilot for requirements analysis, code generation, testing, debugging, refactoring, documentation, code review, and final implementation.
- Assessment Experience: Experienced in evaluating learners through practical coding labs, assignments, quizzes, scenario-based exercises, Copilot-assisted development tasks, debugging challenges, testing activities, code reviews, demonstrations, and project assessments.
Documented learner outcomes
- Problem Statement – A healthcare organisation's development teams work on applications involving patient portals, appointment systems, healthcare data management, clinical-support applications, APIs, and internal business platforms. Developers may spend significant time writing repetitive code, creating test cases, debugging issues, preparing documentation, and understanding existing codebases. Traditional development approaches can increase development effort and make it challenging to maintain consistent code quality across projects. The challenge is to effectively use GitHub Copilot as an AI-powered development assistant to improve developer productivity, accelerate software development tasks, and support code quality while maintaining healthcare data privacy, security, coding standards, and human review.
- How We Overcame the Problem – Learners analysed healthcare application-development requirements and identified suitable tasks for GitHub Copilot. They used natural-language prompts and contextual instructions to generate code suggestions, explain existing code, create functions, develop unit tests, identify potential issues, refactor code, and generate technical documentation. Learners reviewed and validated Copilot-generated code, refined prompts and instructions, and applied secure coding practices to ensure that AI-assisted development remained aligned with healthcare application requirements.
- Overall Implementation – Learners implemented an AI-assisted healthcare software-development workflow covering development requirement → task analysis → prompt/instruction → GitHub Copilot suggestion → code generation → developer review → testing → debugging/refactoring → security validation → documentation → final implementation.
- Healthcare Application Development – Learners used GitHub Copilot to support development tasks for healthcare applications such as patient portals, appointment management systems, healthcare dashboards, information-management applications, and internal support tools.
- AI-Assisted Code Generation – Learners generated functions, classes, API components, data-processing logic, validation routines, and other application code using natural-language instructions and contextual coding requirements.
- Code Explanation and Understanding – Learners used GitHub Copilot to explain existing healthcare application code, identify the purpose of functions and components, and improve understanding of unfamiliar codebases.
- API Development Support – Learners used Copilot to assist with developing and documenting APIs for healthcare applications, including request handling, response structures, validation logic, and error handling.
- Unit Test Generation – Learners generated test cases and unit-test scenarios for application components, validated the suggested tests, and modified them according to project requirements.
- Debugging and Error Resolution – Learners used GitHub Copilot to analyse coding errors, interpret error messages, identify potential causes, and suggest possible solutions that were subsequently reviewed and tested by developers.
- Code Refactoring – Learners applied Copilot to identify opportunities for improving code structure, readability, maintainability, and reusability while ensuring that refactored code continued to meet application requirements.
- Healthcare Data Handling – Learners considered appropriate approaches for handling healthcare-related information and avoided exposing sensitive or confidential data unnecessarily when using AI-assisted development tools.
- Secure Coding Practices – Learners used Copilot to identify potential security issues and improve coding practices related to input validation, authentication, authorisation, error handling, data protection, and secure application development.
- Documentation Generation – Learners used GitHub Copilot to create code comments, function descriptions, API documentation, technical explanations, and development notes to improve maintainability and knowledge sharing.
- Prompt and Context Optimization – Learners developed clear and specific instructions by providing appropriate requirements, programming context, expected behaviour, constraints, and output formats to improve the quality of Copilot suggestions.
- Code Review and Validation – Learners reviewed AI-generated code for correctness, security, performance, maintainability, coding standards, and healthcare application requirements before integrating it into the project.
- Developer Productivity Evaluation – Learners compared traditional development tasks with AI-assisted workflows and evaluated improvements in coding speed, testing, debugging, documentation, and repetitive development activities.
- Responsible AI and Developer Oversight – Learners addressed risks associated with AI-generated code, including incorrect implementations, security vulnerabilities, outdated approaches, licensing considerations, hallucinated APIs, and inappropriate use of sensitive information. Human developer review remained an essential part of the workflow.
- Evidence of Learning – Learners produced AI-assisted code samples, GitHub Copilot prompts and instructions, API implementations, unit tests, debugging examples, refactored code, documentation, security-validation results, before-and-after development comparisons, test results, and project documentation as evidence of their practical GitHub Copilot development capabilities.
- Overall Learner Outcome – By completing the implementation, learners demonstrated the ability to use GitHub Copilot for AI-assisted code generation, code explanation, API development, testing, debugging, refactoring, documentation, and productivity improvement; provide effective development context; evaluate and validate AI-generated code; apply secure coding practices; and integrate responsible AI-assisted development workflows for realistic healthcare software-development scenarios.
Helpful answers for planning your training
Frequently asked questions
GitHub Copilot is an AI-powered coding assistant that helps developers generate, understand, modify, and review code in Kiribati.
Software developers, engineers, programmers, and AI-focused professionals can benefit from GitHub Copilot training in Kiribati.
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