15
Live Sessions
90 mins
Session Length
Mastery
Level
Professionals
Suitable For
Live Online
Delivery
UK Time
Time Zone
LEARNING OBJECTIVE
By the end of this course, you will be able to
Configure Claude and GPT to interact with a Playwright project through MCP server integration, connecting the model directly to your codebase.
Build an AI-augmented workflow that generates page objects, test cases and selectors from natural language prompts.
Integrate AI-assisted debugging into your process to diagnose flaky test failures from LLM analysis of trace files.
Justify which AI-generated test code to merge, reviewing it for correctness and security before it reaches your framework.
Deploy a documented AI-augmented automation workflow that shows a measurable productivity gain across the test lifecycle.
TARGET LEARNER
Who this course is for
For automation engineers ready to stop writing every test by hand and start generating them with AI through MCP.
Automation engineers who already know Playwright and want to bring AI tools into their daily workflow.
QA leads evaluating how AI can reduce test maintenance overhead and speed up test creation.
Senior testers who want to stay ahead as AI reshapes the automation landscape, not catch up later.
Developers who write their own tests and want to use LLMs to improve coverage and speed.
If you see yourself in one of these, this course is built for you.
Prerequisites
What you need
Prior Playwright and JavaScript or TypeScript experience is required before joining.
- Working experience with Playwright including page objects, fixtures, and running test suites is needed.
- Basic JavaScript or TypeScript knowledge is expected — the course does not teach these from scratch.
- A laptop with Node.js, VS Code, and a modern browser installed is required.
- Familiarity with using ChatGPT or Claude at a basic prompt level is helpful but not essential.
- No prior MCP or AI framework integration experience is required — that is exactly what this course covers.
COURSE OVERVIEW
What this course is about
AI-Powered Test Automation with Playwright MCP + Claude/GPT is an instructor-led programme for automation engineers with prior Playwright experience who want to bring AI directly into their testing workflow. The course covers Model Context Protocol integration, AI-generated page objects and test cases, and AI-assisted debugging in depth, combining live Playwright code with Claude and GPT integration throughout every session.
The programme runs across 15 ninety-minute live sessions, progressing through MCP fundamentals, AI-generated page objects, and AI-generated test cases before finishing with AI-assisted debugging, CI integration, and a documented portfolio workflow. Every topic connects the AI model directly to a real, running Playwright codebase.
A working Playwright project and access to Claude and GPT are used throughout the course. AI-generated code is reviewed and integrated into a live framework from session one so learners are building genuine AI-augmented automation throughout, not just experimenting with prompts.
What the course covers:
MCP Fundamentals: connecting AI models to your codebase through Model Context Protocol.
AI-Generated Code: page objects and test cases produced from natural language prompts.
AI-Assisted Debugging: diagnosing flaky test failures using LLM analysis of trace files.
CI Integration: reporting and reviewing AI-generated code before it reaches a shared framework.
Portfolio: a documented AI-augmented Playwright workflow.
CIRRICULUM
7 Modules. 15 live Sessions. Hundreds of small wins.
The curriculum starts with MCP fundamentals, then moves through AI-generated page objects and test cases before finishing with AI-assisted debugging, CI integration, and a documented portfolio workflow.
01
MCP Fundamentals — Connecting AI to Your Codebase
Understand what Model Context Protocol is, how it connects AI models to external tools and codebases, and how to set up Claude Desktop and a local MCP server pointing at a Playwright project.
02
Playwright Codebase Context for AI
Structure a Playwright project for AI readability and learn how to provide effective codebase context so Claude or GPT can generate accurate, framework-consistent output from the first prompt.
03
AI-Generated Page Objects
Use Claude via MCP to generate Page Object classes from live URLs and existing codebase context, then apply a structured review process to evaluate, correct, and integrate the AI output.
04
AI-Generated Test Cases
Generate Playwright test scenarios from user stories and acceptance criteria, expand test coverage with AI-suggested edge cases, and use LLMs to produce parametrised tests from data sets.
05
AI-Assisted Debugging and Flaky Test Analysis
Build a structured workflow for feeding Playwright failure traces and error logs to Claude or GPT, using AI to diagnose root causes, suggest fixes, and build a reusable prompt library for common failure modes.
06
CI Integration and AI Test Reporting
Integrate the AI-augmented Playwright framework into a GitHub Actions pipeline, use LLMs to generate plain-English test summaries, and measure the productivity impact of AI across the test lifecycle.
07
Portfolio — AI-Augmented Playwright Workflow
Build and document a complete AI-augmented Playwright framework for a demo site, covering AI-generated page objects, test cases, a debugging log, productivity metrics, and a GitHub README.
Course Types
We offer three structured learning paths based on your goals:
Crash Course (Fast-Track)
Quick, intensive courses designed to teach specific skills efficiently. Ideal for those upskilling fast or preparing for certifications.
DeepDive Program (Full Mastery)
Comprehensive, step-by-step learning for full mastery. For beginners and professionals seeking long-term, deep expertise.
MentorConnect (One-on-One)
Personalised mentorship with real-world guidance. Best for those who thrive with direct, expert-to-learner coaching.
Learning Approaches
We recognize that everyone learns differently, so we offer flexible learning formats to fit your needs:
One-on-One Training
Personalized, instructor-led coaching tailored to your learning speed.
Best for career-specific coaching or specialized training needs.
Small Batch Classes (2-5 learners)
Interactive, discussion-based learning in small groups.
Encourages collaboration, teamwork, and peer-to-peer engagement.
FAQ
1
What is Model Context Protocol and why does it matter for QA?
Model Context Protocol (MCP) is an open standard that lets AI models like Claude connect directly to external tools, codebases, and services. For QA automation, this means Claude can read your actual Playwright project files, understand your framework conventions, and generate test code that fits your existing structure, rather than producing generic boilerplate.
2
Do I need to know Playwright before joining?
Yes, working Playwright experience is required. The course does not teach Playwright fundamentals. You should already be comfortable writing page objects, fixtures, and test suites. The ITLearnner Playwright with TypeScript course is the ideal preparation if you need to build that foundation first.
3
Is this course just about prompting ChatGPT for test code?
No. This course builds a structured, repeatable AI-augmented workflow using Model Context Protocol, which gives AI models contextual access to your actual codebase rather than a blank slate. The difference in output quality between an MCP-connected AI and a generic ChatGPT prompt is significant, and the course teaches you to work with the former.
4
Which AI tools are covered?
The course covers Claude (via Anthropic's MCP implementation and Claude Desktop) and GPT (via OpenAI's function calling and API). Both are used throughout the course so you understand the differences and can choose the right tool for a given task in your own organisation.
5
Will I still need to write test code manually?
Yes. AI is a productivity multiplier, not a full replacement. The course teaches you to review, evaluate, and correct AI-generated code critically. Understanding what the AI produced and why is essential for maintaining a professional framework, and the course dedicates significant time to the review and integration skills that separate good AI-augmented engineers from those who create tech debt.
6
How does AI-assisted debugging work in practice?
Module 5 covers structured prompting patterns for feeding Playwright failure traces, error logs, and stack traces to Claude or GPT. You will build a library of reusable debugging prompts for the most common Playwright failure modes (stale elements, timing issues, selector drift) and learn how to evaluate the AI's suggested fixes before applying them.
7
Will the course cover AI and CI pipelines?
Yes. Module 6 integrates the AI-augmented framework into GitHub Actions, covering how to run AI-assisted tests in a pipeline, generate plain-English AI test summary reports automatically, and measure the productivity impact of AI across the test lifecycle.
8
What does the portfolio project look like?
The Module 7 portfolio project is a documented GitHub repository containing an AI-augmented Playwright framework for a demo site, a debugging log showing AI-assisted diagnosis of real failures, and a productivity comparison with before-and-after metrics. This gives you concrete evidence of AI automation skills to show in interviews.
9
Is this course relevant if my team uses Selenium rather than Playwright?
The MCP and LLM integration principles covered in this course transfer across frameworks. However, the practical implementation is Playwright-specific. If your team uses Selenium, the AI for Automation Testers course on ITLearnner covers AI augmentation at the framework-agnostic level and is a better fit.
10
How quickly is this area evolving and will the course stay current?
This is one of the fastest-moving areas in software engineering. ITLearnner reviews and updates this course regularly as MCP evolves, new AI tools emerge, and best practices solidify. Enrolled learners receive update notifications and access to revised content as it is released.
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