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Folks who’ve had “developer” on their job title for a few years will tell you: the hand-wringing over coding assistants feels awfully familiar. Intellisense, copy-paste… haven’t we been here before?

Not quite. This latest wave of tools brings as many cons as it does pros - and that’s exactly why the real question isn’t if we should use them, but how. How do we leverage AI coding assistants in ways that actually make us better engineers? Better problem solvers?

In this talk, I’ll share how we’ve been experimenting with AI inside the Umbraco CMS development team over the past year. Think mandatory Copilot-assisted sprints where devs are encouraged to ask “why” and “what if” instead of just “give me code.” I’ll walk through the practices that have turned coding assistants into learning assistants - and explain why AI can be such a powerful, low-stakes place to practice, explore, and even make mistakes.

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AI coding assistants present both opportunities and risks for developer learning. The key is using them as thinking partners rather than solution generators—sparring with AI to understand reasoning, asking critical questions like "why might this be bad?", and focusing on the learning process rather than just output. AI excels at removing cognitive exhaustion from grunt work (setup, boilerplate, regex), freeing mental capacity for deep problem-solving. However, effective learning requires struggle, mistakes, and critical reflection. Developers should use AI for tasks they don't care about improving at, while maintaining human collaboration for areas requiring growth. The tool works best when it scaffolds learning at the right difficulty level (zone of proximal development) and provides a judgment-free space for experimentation.

Learning not leaning: How developers can Accelerate their Learning with AI - Emma Burstow