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AI-Assisted Embedded Driver Engineering

Packt via Coursera

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Overview

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Develop a disciplined AI-assisted driver workflow that turns peripheral requirements into reviewable patches, verified harnesses, documentation, and release-ready embedded code. AI can support embedded development most effectively when it works within a disciplined engineering process. This course presents an AI-assisted workflow for building peripheral drivers that are specified, reviewed, verified, and packaged for release rather than produced as unvalidated snippets. The curriculum starts with CubeIDE setup, VS Code, project structure, build baselines, and golden reference drivers for GPIO and UART. Learners align an AI assistant to project standards, add lightweight logging, and use patch-based development while creating driver interfaces and implementations for timers, ADC, and external interrupts. Emphasis stays on requirements, invariants, reviewable diffs, target proof harnesses, documentation, examples, and evidence artifacts. Learners gain a repeatable process for working with AI without giving up engineering control; by the end of this course, they can use AI support to ship embedded drivers they can explain, verify, and defend. Embedded developers, firmware engineers, and technical leads who want a structured AI-assisted workflow for peripheral driver creation. Useful for STM32 developers building GPIO, UART, timer, ADC, or EXTI drivers and for teams that need reviewable, evidence-backed firmware. The course treats AI as a peer programmer within clear engineering boundaries. Learners establish standards, baselines, and reference drivers before requesting patches. Verification harnesses and evidence artifacts reinforce every driver workflow. This course is based on AI-Assisted Embedded Driver Engineering, by Israel Gbati. Copyright © 2026 Israel Gbati All rights reserved. No part of this course may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews. Packt is one of the world's most prolific publishers of cutting-edge technical content. For over two decades we've made it our mission to curate and publish the knowledge of only the very best technical experts. We focus on real-world courses that help our customers get the job done, with coverage that extends across a wide range of established and cutting-edge technical topics. If you're an individual or an organisation that embraces learning by doing, Packt is the perfect fit for you.

Syllabus

  • Introduction
    • This module provides an overview of the course structure, key learning goals, and the practical importance of AI in embedded driver engineering. It helps learners understand how AI can enhance system development and efficiency in real-world applications.
  • Setting Up the IDE
    • This module guides learners through the essential steps of setting up an integrated development environment (IDE) for embedded systems. It covers downloading, installing, and configuring development tools, along with using technical documentation to support firmware implementation. Learners will gain practical skills in preparing a development workspace for hands-on projects.
  • Setting Up the AI-Assisted Development Environment
    • This module guides learners through the setup of an AI-assisted development environment, focusing on configuring tools like VSCode, enabling an AI peer programmer, and integrating AI capabilities for coding. It equips students with the foundational skills needed to work effectively in an AI-enhanced coding workflow.
  • Project Structure and Build Baseline
    • This module explores the foundational structure of a software project and its build process, focusing on key components such as the project tree, platform files, and header files. Learners will gain practical skills in analyzing and implementing structured firmware components within an AI-assisted embedded driver engineering workflow.
  • Golden Reference Driver: GPIO
    • This module explores the development and implementation of a GPIO driver for embedded systems, focusing on key components such as clock access, mode configuration, and initialization functions. Learners will gain hands-on experience in creating structured firmware solutions and validating their functionality. The course also emphasizes integrating these elements into a broader AI-assisted embedded driver engineering workflow.
  • Golden Reference Driver: UART
    • This module focuses on configuring and implementing UART communication in embedded systems, including setting up timebases, helper functions, and testing the final design. Learners will gain hands-on experience with firmware development and AI-assisted embedded driver engineering workflows.
  • Lightweight Logging for Proof and Verification
    • This module covers the implementation and testing of a lightweight logger for proof and verification in embedded systems. Learners will explore how to define logging interfaces, implement log functions, and validate system behavior in a firmware context. The focus is on building reliable logging mechanisms that support AI-assisted embedded driver development.
  • Aligning the AI Assistant to Your Codebase Standard
    • This module explores how to align AI assistants with codebase standards, focusing on practical techniques like scanning reference materials and using automated tools to enhance embedded system development.
  • AI-Assisted Driver Engineering: Timers
    • This module explores how to integrate AI-assisted tools into the development of embedded driver systems, focusing on firmware implementation, API proposals, and test validation. Learners will gain practical skills in instructing AI assistants to build and verify module files for IoT and embedded systems. The course emphasizes structured workflows for timer-based features in real-world applications.
  • AI-Assisted Driver Engineering: ADC
    • This module explores the practical implementation of AI-assisted driver engineering, focusing on executing session contracts, proposing and implementing API patches, and validating driver behavior through testing and documentation. Learners will gain insights into the workflow of developing robust, AI-enhanced embedded systems.
  • AI-Assisted Driver Engineering: External Interrupts (EXTI)
    • This module explores the integration of AI in embedded driver engineering, focusing on external interrupts, API implementation, and event handling. Learners will gain hands-on experience with firmware development, interrupt management, and real-time system coordination.

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