AI for all: Arm Education launches new learning pathway from novice to expert

In an era where artificial intelligence is rapidly transforming every sector—from healthcare through manufacturing to education—the imperative to equip learners with practical, high-quality AI and machine learning (ML) skills is clearer than ever.

At Arm Education, we have responded to this need by launching a structured AI learning pathway designed to meet learners where they are—and take them where they need to go. This curated suite of courses addresses a full spectrum of learning, from foundational AI and ML literacy to advanced deployment of generative AI workloads on Arm-based platforms.

This approach is grounded in the “cradle-to-cradle” philosophy of circular education as articulated by Dr. Khaled Benkrid, Arm’s Senior Director of Education and Research, in his blog post, A Lifelong Circular Model of Education: From Cradle to Cradle. I’ll be drawing on insights from that piece throughout this blog post, and I encourage you to read it for additional context.

Building AI Awareness and Confidence: Introduction to AI

Available on edX and Coursera platforms 

Target level: Entry / Level 3 (Skills for Life, School-Level, Conversion Pathways)

This course is designed to provide an accessible entry point into the world of AI and ML. Created with non-specialists in mind, the course introduces foundational AI concepts in a way that’s clear, engaging, and relevant. Learners explore essential questions such as: What is AI? How does it impact our lives? What ethical challenges does it pose?

Importantly, the course doesn’t assume prior experience. It’s well suited for UK Levels 3 or equivalent (typically 16-18 year old education level),and also serves as a valuable tool for educators seeking to bring AI into the classroom with minimal technical overhead.

By the end of this course, learners will be able to:

• Define what artificial intelligence (AI) is and explain its core concepts in accessible terms.
• Describe key components of AI, including algorithms, machine learning and neural networks.
• Identify everyday applications of AI across different industries and technologies.
• Recognize the ethical, societal, and environmental considerations surrounding the use of AI.
• Develop confidence in digital literacy and computational thinking skills.
• Explore the discussion around balancing power consumption and sustainability.
• Apply the skills and knowledge gained across the course to build, train, and run a ML classification model.
• Reflect on their own interaction with AI and its potential impact on future learning or career paths.

Bridging Theory and Practice: Machine Learning at the Edge on Arm – A Practical Introduction

Available on: edX and Coursera platforms

Target level: Level 5–6 (Skills for Life, Undergraduate)

Machine Learning at the Edge on Arm: A Practical Introduction enables learners to transition from basic AI knowledge and skills to applied embedded ML development, and is well suited to university students, fresh graduates, and engineers upskilling in edge deployment.

Learners will work with real-world datasets and deploy ML models using open-source frameworks like LiteRT for Microcontrollers—all within the constraints of low-power, resource-limited devices.

In alignment with the previously referenced “cradle-to-cradle” model, this course acts as an entry point for learners seeking to direct their path toward practical, industry-relevant expertise. It forms a “bridging” experience that supports circular movement through education—whether coming from academic study or going to a new industry role.

By the end of this course, learners will be able to:

• Understand the fundamentals of embedded machine learning and how it differs from cloud-based AI.
• Collect and prepare sensor data for training on microcontrollers.
• Train and evaluate simple ML models suitable for deployment on Arm Cortex-M processors.
• Optimize models through quantization and evaluate memory/performance trade-offs.
• Deploy ML models on physical devices using a structured workflow and open-source tools.

Exploring Possibilities: AI at the Edge on Arm

Available on: edX

Target Level: Level 5–6 (Undergraduate, Professional Upskilling)

Co-developed with the University of Cambridge, Professional and Continuing Education, AI at the Edge on Arm: Understanding and Deploying LLMs for Mobile Devices, explores one of the most dynamic frontiers in modern computing: deploying artificial intelligence directly on edge devices.

Rather than relying on cloud infrastructure, learners will investigate how to run AI workloads on-device—focusing on real-world examples running on mobile phones. The course introduces practical strategies for optimizing models for edge performance, including quantization, compression, and deployment using Arm-based platforms.

With hands-on exercises and applied context, this course builds both conceptual understanding and practical skills—ideal for those looking to deepen their expertise in embedded AI.

By the end of the course, learners will be able to:

• Understand the fundamentals of edge AI, including its unique advantages and technical challenges
• Explore lightweight and quantized model architectures suited to resource-constrained environments
• Gain hands-on experience using industry-standard tools and workflows for deploying AI on Arm hardware
• Examine large language models (LLMs) in the context of edge computing
• Develop an informed view of deployment trade-offs, including performance, power, and privacy.

Empowering Educators: Teaching AI on the Edge

Available on: Coursera platform

Target level: Level 6–7 (Tutors/Academic/Professional Educators)

Educators are central to our mission to build a lifelong learning ecosystem. That’s why we’ve launched Teaching AI on the Edge—a dedicated professional development course for university and vocational instructors who want to bring edge AI into their own curricula.

Presented by Dr. Catherine Breslin, an AI consultant from Cambridge UK and co-founder of Kingfisher Labs, the course delves into key considerations for teaching AI in higher education. It blends essential theoretical foundations with practical project-based experiences, preparing participants to understand, build, and effectively teach AI systems optimized for edge devices

This course exemplifies what Dr Benkrid calls bridging content—a layer that connects student learning with professional practice, and educators with Arm’s broader technology ecosystem. It reflects Level 6–7 pedagogy while also offering modular reuse in other contexts.

High-performance learning: Optimizing Generative AI on Arm

Available on: edX

Target level: Level 7–8 (Advanced University & Developer Proficiency)

Rounding out the pathway is our Optimizing Generative AI on Arm Processors: from Edge to Cloud course, now available edX. This course caters to advanced learners and professional developers optimizing ML workloads for performance, efficiency and scalability.

Created with ML practitioners in mind, this course bridges the gap between theory and real-world deployment on Arm-based systems.

Learners will focus exclusively on advanced optimization techniques, including SIMD (SVE, Neon), low-bit quantization, and microkernel-accelerated inference, without revisiting foundational machine learning concepts.

To support efficient and focused learning, each module is structured as a concise, runnable jupyter notebook. Learners will benefit from narrated slides and hands-on exercises, tailored to different learning styles. Clear architectural examples are highlighted to show how to extract maximum performance from Arm platforms.

Throughout development, real-world application remained a guiding principle. As Oliver Grainge, AI researcher at the University of Southampton and course author, observed:

Additionally, learners are guided through the step-by-step process of running large language models (LLMs) on both edge devices  and cloud platforms, gaining hands-on experience with the trade-offs involved in performance, efficiency, and deployment. As Madhu Thomas, Principal Software Engineer and early reviewer comments:

“The course provides a detailed, step-by-step guide on how to run a large language model (LLM) on a CPU and cloud, making complex concepts more palatable. … Whether you're pursuing academic research or are an engineer looking to rapidly prototype AI concepts on Arm CPUs, this course is an invaluable resource.”

Creating a learning experience across levels

As mentioned in Dr. Benkrid’s blog post, technical education isn’t one-size-fits-all—it should be flexible, inclusive, and designed to support lifelong learning. This new AI and ML pathway reflects that approach, giving learners the freedom to start, return to, or shift their learning journey as their goals change. Whether you are a school student taking your first step, a university lecturer integrating practical labs, or a professional expanding into edge AI development, our materials are:

• Modular: start where it makes sense for you
• Stackable: build deeper understanding with each step
• Versatile: adapt for academic, vocational, or professional contexts

These courses are not final destinations, but stepping stones within a lifelong learning journey. Whether learners are just beginning or building on prior experience, the pathway equips them with the skills and confidence to grow, adapt, or begin anew. Inspired by Arm’s goal to democratize access to computing innovation, this learning pathway empowers individuals to build future-ready skills and take part in the AI transformation—regardless of where they begin.

Get involved

If you are an educator, developer, or just someone passionate about shaping the future of AI, we invite you to explore these courses and partner with us to bring AI education into your classrooms, curricula, or communities.

Reach out to us at education@arm.com to find out more.