Writing

Rapid Prototyping AI Products with Claude Projects

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From Hypothesis to Working Prototype in Days

Developing AI-powered products traditionally requires months of development and significant technical resources. Using Claude Projects, we transformed this timeline from months to days, testing five critical AI capabilities and building a fully functional automated lesson plan generation system for South African teachers. This article explores how hypothesis-driven prototyping with Claude Projects enables rapid validation of AI product concepts before committing to full-scale development.

Introduction

The gap between AI capability and practical implementation remains a significant barrier in educational technology. While large language models demonstrate impressive potential, translating that potential into reliable, curriculum-aligned tools requires answering fundamental questions: Can AI reliably research curriculum documents? Can it follow complex multi-step processes? Can it integrate with existing systems? Can it produce professionally formatted outputs?

Rather than spending months building infrastructure to test these questions, we used Claude Projects as a rapid prototyping environment. Within one week, we moved from concept to a working system that generates comprehensive, curriculum-aligned lesson plans for the South African CAPS curriculum, complete with professional formatting, WordPress integration, and evaluation capabilities.

Why This Matters:

Educational technology development often fails not from lack of ambition but from building solutions before validating core assumptions. Rapid prototyping allows educators and developers to test AI capabilities quickly, identify limitations early, and make informed decisions about scaling production systems.

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Automating WordPress SEO Optimisation with MCP and Claude Code

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A Case Study in AI-Powered Site Management

This case study demonstrates how AI automation via the Model Context Protocol and Claude Code transformed a labour-intensive WordPress SEO optimisation project into a streamlined, chat-based workflow. What would typically require hours of manual WordPress administration was completed in two sessions without accessing the admin interface, addressing 11 critical errors, optimising 52 pages, and implementing strategic internal linking across 40 posts.

Note – I am not an SEO-bro or marketing guru, but have been running my own websites for years.

Where we started!

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Why I Built an AI Twin to Run My Life

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Building NiallOS with Claude Code 

“AI will save you so much time!” Silicon Valley promised. So I tried everything. ChatGPT for writing assistance. Claude for research summaries. Gemini for project management. Copilot for planning. Every morning started the same way: Ten minutes explaining my context to whichever AI I was using that day.

“I’m a Product Lead at Cambridge Education Futures…”

“I’m working on a Master’s…”

“I need this in British English for an internal audience…”

“Keep it practical, I’m building training programmes…”

Then I’d finally get to the actual work. AI was supposed to save time. Instead, I felt busier than ever.

I was drowning in AI tool overload while still doing everything manually.

The working laboratory is now NiallOS. The method behind it is expertise encoding.

Last reviewed: 31 August 2026.

The AI Promise vs. Reality

2023-2024: AI will transform knowledge work! Automate everything! Work 4 hours a week!

That was the promise. My reality as a Product Lead at Cambridge looked quite different. I was designing training programmes for education leaders working with organisations like UNICEF and KFAS, and managing a Master’s research project. Creating workshop materials and training curricula. Publishing reports, papers and research outputs. Tracking professional development and learning goals. Staying current on EdTech and AI in education.

AI was supposed to save time on routine tasks, help me focus on strategic work, make complex work manageable, and give me breathing room.

What actually happened? Every morning became an AI context-switching marathon. ChatGPT for brainstorming workshop activities. Claude for analysing research papers. Back to ChatGPT for LinkedIn post drafts. Gemini for curriculum outline suggestions. Each tool required a fresh start and re-explaining everything from scratch.

I calculated the daily context tax. I wasn’t using AI to work smarter. I was managing a portfolio of AI tools, each one requiring setup, context, and translation. It felt like having five junior assistants who all had amnesia every morning.

Claude Code stepping up to run my life

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Building Sovereign AI Infrastructure for Education: UNICEF Innovation Camp Workshop

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Part of: Sovereign AI for Education Systems: A Practical Guide for Ministries, a guide to the decisions, infrastructure options and roadmap for ministries.

In October 2025, I was invited by UNICEF to design and deliver an AI in Education workshop for their Learning Pioneers Programme, bringing together education ministry leaders from 14 countries at the Innovation Camp in Helsinki. The session tackled a critical challenge: how can resource-constrained education systems harness AI’s potential without compromising cultural sovereignty, breaking budgets, or replacing educators?

Niall McNulty at the UNICEF Innovation Camp, 2025

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Transforming Educational Publishing with AI

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A Practical Workshop Framework

As organisations across higher education and publishing confront the challenge of integrating artificial intelligence into their operations, the question is no longer whether to adopt AI, but how to do so effectively. At Cambridge, we have been running an internal training programme focused on what we term “AI-ifying the organisation” – helping teams understand, adopt, and implement AI tools in ways that enhance rather than replace human expertise.

This article documents our approach to training publishing professionals in AI capabilities, drawing from a comprehensive workshop delivered on AI and Educational Publishing. Rather than treating AI as a mysterious black box, we focus on practical techniques that publishing teams can implement immediately, whilst also addressing the critical change management and upskilling requirements that successful AI adoption demands.

The framework we developed serves dual purposes: demonstrating what is genuinely possible with current AI technology, and providing a replicable training structure that other organisations can adapt to their own contexts. Throughout this article, we share specific examples, prompting techniques, and workflow implementations that have proven effective in real-world publishing scenarios.

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AI Fundamentals for Educators: AI, Language Models, Capabilities and Ethics

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Part of: How to Design AI Professional Development That Changes Practice, a guide to AI training that lasts beyond the workshop.

This page brings together the four lessons of the AI Foundations module from the Cambridge AI Lunch and Learn series: what AI is, how large language models work, what they can and cannot do, and how to use them responsibly.

Lesson 1, AI Foundations Module

In our first foundational session, we demystified artificial intelligence by exploring what it actually is—and what it isn’t. The key insight? AI doesn’t follow rules like traditional software; it recognizes patterns learned from vast amounts of data, much like how you learned to recognize cats through examples rather than explicit instructions.

We distinguished between different types of AI (Large Language Models, Computer Vision, Predictive AI, and Generative AI) and discovered that AI is already embedded in our daily lives – from email spam filters to smartphone facial recognition. Through practical exercises, participants experienced how AI can adjust its communication style for different audiences and solve multi-constraint problems like planning office pizza orders with dietary restrictions.

The takeaway: AI is a powerful tool for augmentation, not replacement, and like any tool, it requires understanding its capabilities and limitations before we can use it
effectively.

AI_Lunch_9_OctoberDownload

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Adapting AI to tackle education inequality in Africa

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Originally published on Biz Community

The opportunities that artificial intelligence (AI) offer African teachers and students are immense; the AI education market in the Middle East and Africa is projected to hit $1.7bn by 2030. Yet in sub-Saharan Africa, where student–teacher ratios can reach 50:1 and many children still lack access to quality learning resources, the need for innovative solutions is urgent.

What excites me most about AI in African education is the potential to address persistent inequalities in ways that haven’t been possible before.

For too long, students in under-resourced schools have had fewer opportunities simply because their teachers lacked access to support, materials, or professional development. AI can change this dynamic fundamentally, making world-class support accessible even in the most remote classrooms.

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Designing a 24-Week AI Training Programme for Professional Development

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Part of: How to Design AI Professional Development That Changes Practice, a guide to AI training that lasts beyond the workshop.

Lessons from Cambridge

Developing organisational AI capability requires more than ad-hoc workshops. This article documents the design and implementation of a comprehensive 24-week AI training programme at Cambridge University Press and Assessment, demonstrating how structured, bite-sized learning can transform busy professionals from “AI curious” to “AI confident” whilst maintaining their daily work commitments.

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10 Simple Prompts for Cambridge Teachers to Use with AI Chatbots

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Ready-to-use prompts for lesson planning, activities and classroom resources

I’ve created 10 easy prompts that give Cambridge teachers immediate access to syllabus and assessment content, suggestions for effective teaching delivery, ideas for engaging classroom activities, and resources you can use in class or share directly with students. The beauty of these AI chatbots is their accessibility; they work on mobile devices and require no special setup or additional data inputs.

I’ve tested these prompts across ChatGPT, Claude and Gemini, and here’s what I’ve found: they’re remarkably similar in terms of capabilities, so use whichever one you are familiar with or have access to. For the demos, I’ve pretended to be an IGCSE Business Studies teacher, but you can adapt the prompts for your subject and level.

The key insight? If you’re a thoughtful teacher who knows how to check and verify outputs, these tools can genuinely increase your productivity and potentially improve your teaching practice. I’ve included all the prompts in the slides so you can test them out yourself, get ideas for lessons, find suggestions for activities, and create content for both your lessons and students. When the model writes questions, check the cognitive level against a stem bank rather than trusting the verb. I keep Bloom’s Taxonomy question stems beside the output.

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When AI Meets Human Potential: Insights from Azerbaijan’s Digital Pioneer

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A fascinating conversation about reshaping education and workforce development in the age of artificial intelligence

I had the privilege of moderating a thought-provoking fireside chat with Fariz Jafarov at Baku ID, and it left me with plenty to think about regarding the future of work and learning. Fariz brings a unique perspective to these conversations – he’s not just theorising about digital transformation; he’s been orchestrating it across Azerbaijan for over a decade.

As Executive Director of C4IR Azerbaijan, part of the World Economic Forum’s global network focusing on the Fourth Industrial Revolution, Fariz is at the coalface of national AI strategy. His previous role as Director General of the e-Government Development Center saw him develop transformative platforms like ASAN Visa and myGov, fundamentally changing how millions of citizens interact with government services. This earned him Azerbaijan’s State Medal of Progress, which speaks volumes about the real-world impact of his work.

Niall McNulty with Fariz Jafarov

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Vibe Coding: How South Africans Can Build Software by Simply Describing What They Want

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An approach to programming that could enhance digital skills development across the country

I’m a big fan of Cursor for coding with AI, and this movement now has a name – “vibe coding” – which allows people to create programmes by describing what they want in natural language and letting AI handle much of the actual coding. As South Africa grapples with developing digital skills and implementing new coding curricula in schools, vibe coding represents a potentially transformative approach that could democratise software development across the country.

The timing couldn’t be better. With the Department of Basic Education rolling out coding and robotics as part of the new curriculum framework, and the country’s ongoing push to develop digital literacy, vibe coding offers a bridge between traditional programming education and practical software creation. Rather than spending months learning syntax and programming languages, South Africans can now focus on problem-solving and creative thinking while AI handles the technical heavy lifting.

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Herrington’s Authentic Learning Framework Meets Generative AI

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Jan Herrington’s authentic learning framework asks for tasks that use the same thinking as real professional work. Generative AI can support that, or it can short-circuit the struggle the framework needs. This page holds those two in view.

Last reviewed: 31 August 2026. Source: Herrington, Reeves and Oliver, A Guide to Authentic e-Learning.

Authentic learning is not a realistic-looking worksheet. It is cognitive realism: the same kinds of problem, collaboration and unfinishedness you would meet at work. Generative AI belongs in that picture only when students still have to plan, check and own the result.

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