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Quality engineering team redesigning AI-assisted prevention testing diagnostics and governance workflows
SQA, Test Automation & SDET · Course #064

Modern Quality Engineering with AI

This course develops the practical reasoning needed to redesign quality-engineering workflows so AI assistance improves prevention, coverage, diagnostics and learning without weakening governance. Rather than treating modern quality engineering with ai as a list of terms, it links modern quality engineering and ai augmentation, value-stream mapping and quality-friction diagnosis and use cases across discovery, development, testing and operations to realistic product risks and team workflows. Learners map a quality value stream, prioritise use cases, define human and automated controls, evaluate outputs, integrate evidence and plan capability, policy and measurement changes across teams. Learners compare alternatives, document assumptions and check results against explicit expectations. The result is an adoption roadmap that links AI to measurable quality outcomes rather than tool activity.

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Overview

This course develops the practical reasoning needed to redesign quality-engineering workflows so AI assistance improves prevention, coverage, diagnostics and learning without weakening governance. Rather than treating modern quality engineering with ai as a list of terms, it links modern quality engineering and ai augmentation, value-stream mapping and quality-friction diagnosis and use cases across discovery, development, testing and operations to realistic product risks and team workflows. Learners map a quality value stream, prioritise use cases, define human and automated controls, evaluate outputs, integrate evidence and plan capability, policy and measurement changes across teams. Learners compare alternatives, document assumptions and check results against explicit expectations. The result is an adoption roadmap that links AI to measurable quality outcomes rather than tool activity.

Course Highlights

AI-enabled quality value stream

Use-case prioritisation by outcome

Human and automated controls

Integrated quality evidence

Capability and policy design

Modules & Curriculum

Modern quality engineering and AI augmentation

Value-stream mapping and quality-friction diagnosis

Use cases across discovery, development, testing and operations

Data, context and integration requirements

Human review, evaluation and risk controls

AI-assisted prevention, test design and automation

Diagnostics, defect intelligence and operational learning

Governance, policy, security and responsible use

Capability building, roles and change adoption

Metrics, pilot design and scaled roadmap

Learning Outcomes

  • Assess where AI can improve a quality-engineering value stream.
  • Prioritise use cases by value, feasibility and risk.
  • Design human review, evaluation and escalation controls.
  • Integrate AI-generated evidence with existing quality systems.
  • Define capability, policy and role changes for sustainable adoption.
  • Measure effects on coverage, feedback, reliability and maintenance.

Ready to Build Your Future-Ready Skills?

Create an AI-enabled quality system measured by outcomes, evidence and responsible control.

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