How WBS CODING SCHOOL Teaches Students in the Age of Vibe Coding

Vibe coding is changing how developers write code, but where’s the line between learning and letting AI do the work? At WBS CODING SCHOOL, instructors like Garrett Lander are helping students navigate that question. Garrett explains how WBS CODING SCHOOL integrates “vibe coding” – a term coined by OpenAI co-founder Andrej Karpathy – into its web development curriculum, teaching students how to use tools like GitHub Copilot and ChatGPT responsibly while still mastering the fundamentals of coding, logic, and problem-solving.

Key Takeaways

• Vibe coding means using natural language prompts to generate code – but it’s not a replacement for learning programming fundamentals.

• Students should learn syntax, logic, and debugging first before layering in AI tools.

• AI-assisted coding tools like GitHub Copilot, ChatGPT, and Lovable can speed up workflows – but only if you understand what’s happening under the hood.

• WBS CODING SCHOOL takes a “harm reduction” approach: students learn how to use AI responsibly, with guidance rather than restriction.

• Hands-on coding remains essential – AI can’t teach problem-solving or logical thinking the way direct practice can.

How do you define ‘vibe coding’ for WBS CODING SCHOOL students?

Vibe coding was coined by Andrej Karpathy, co-founder of OpenAI, in a tweet where he described ‘letting the vibes take over’ and allowing LLMs to handle the coding. Instead of writing code in a programming language, you use human language to describe what you want to build, and the LLMs handle the actual coding. In short, you describe it, and it magically appears.

What does the vibe coding workflow look like in practice, and what tools do you recommend?

Vibe coding exists on a spectrum. You can go full vibe coding with tools like Lovable, where you use a chat interface to describe what you want and let AI handle everything – including debugging. Or you can write most of the code and use generative AI as an assistant.

The typical workflow includes a chat interface (like ChatGPT or Gemini), alongside a preview of your UI for frontend work, with the ability to toggle between the preview and actual code. Lovable is great for non-technical folks who don’t need to edit code directly. At WBS CODING SCHOOL, we primarily use GitHub Copilot because it integrates seamlessly with VS Code, which is already part of our development environment.

When should developers use vibe coding, and when should they avoid it?

Vibe coding is perfect for weekend projects where you’re building something for fun with no real stakes – exactly the use case Karpathy described in his original tweet. It’s fantastic for quick prototyping, allowing you to go from idea to prototype in minutes instead of hours. It’s also useful for repetitive tasks where you’re already really confident and you can easily review the generated code and output.

However, avoid vibe coding when stakes are involved. You need professional human review if you’re expecting more than 10 users, handling actual user data, or processing monetary transactions. The stakes are too high to trust to vibes alone when real data or money is on the line.

How does WBS CODING SCHOOL incorporate vibe coding into its curriculum?

Honestly, our focus remains on students writing code and understanding fundamentals. We offer a Vibe Coding Workshop that introduces the concept, covers pros and cons, and teaches responsible usage. We introduce tools like Lovable, Fireship, and GitHub Copilot in VS Code.

This vibe coding content was intentionally placed at the end of our course in the last week. We also provide workshops on prompt engineering and practical tool usage. We plan to expand this content, which will remain supplementary to core programming skills.

What role does hands-on coding (without AI assistance) play in your curriculum?

Hands-on coding is still the bulk of our curriculum. The idea that AI or vibe coding will replace engineers is maybe a far-off dream, but the current reality is that you can’t trust LLM output when the stakes are involved. There are security risks, outdated code issues, and quality problems – LLMs are only as good as the data they’re trained on, and there’s a lot of really bad code out there.

Our focus remains absolutely on students writing code themselves because in order to properly leverage vibe coding or GenAI-assisted tools, you must be able to read and write code yourself first.

What foundational concepts should a student master before using AI coding tools?

Before diving into AI tools, students first need to understand the fundamentals – things like syntax, loops, and basic programming principles. But honestly, the most important skill isn’t technical – it’s logical thinking.

It’s about learning how to take an abstract idea in your head and translate it into actual code. That kind of problem-solving and theoretical thinking is often the hardest part for beginners. I hear it all the time: “When someone explains the code line by line, it makes sense – but when I try to go from project requirements to writing the code myself, I get stuck.”

That’s exactly what AI tools can rob you of early on – the struggle that helps you grow. So at the beginning, we discourage students from relying too much on generative AI. You need to write the code yourself and work through the frustration, because that’s where real learning happens.

What’s your philosophy on when students shouldn’t use AI tools while they’re still learning – and how do you encourage that restraint?

I see AI tools as a trade-off – you’re sacrificing learning for productivity. In a work environment, that trade-off can make sense, especially for repetitive tasks you already understand. But in an educational setting, the focus should be on learning, not speed or efficiency.

The more you invest in actually learning the programming concepts, the more those productivity gains will multiply later. If you use a “vibe coding” tool with zero technical knowledge, your output will be limited. But if you understand the fundamentals, your productivity increases exponentially – you can prompt the model more effectively, give detailed instructions, and interpret the output correctly.

That deeper understanding also helps when the model hits a wall and can’t “vibe” its way out of an error. With a solid foundation, you can step in, debug, and get it back on track. That’s why we encourage students to hold off on AI tools early on – build the foundation first, then layer AI on top.

How do you structure pair programming or collaborative work in an environment where students still have access to AI tools?

Collaborative work is a huge part of our curriculum. Most weeks, students work in groups of two to four on projects. We emphasize keeping a human in the loop.

In programming, there are countless ways to solve the same problem. Our curriculum is intentionally opinionated – we teach specific approaches, sometimes several – but if you ask an AI model for help, it might take you down a completely different path. You can often tell when something was written by AI because the code looks stylistically different from what we’ve covered.

That’s why I always remind students to bring another person into the process – whether it’s a teammate or, ideally, an instructor. They can help you spot when you’re going off track and guide you out of those AI rabbit holes before they become bigger problems.

How does WBS CODING SCHOOL ensure students develop a deep mental model of software rather than defaulting to AI-generated code?

We use a ‘harm reduction model’ – we recognize AI tools are out there, so we won’t tell students not to use them. Instead, we guide them toward assisted learning modes in ChatGPT and Gemini that require engagement rather than spoon-feeding answers.

We strongly discourage letting AI write code for you. AI can be helpful for debugging, but unless you’re at the end of the course, you should write as much code yourself as possible. We teach techniques like pseudocode (breaking big tasks into comments to build that logical bridge). For example, if you’re building a CRUD app, start by breaking that down into the smallest possible tasks.

Have you noticed students becoming demotivated by thinking, ‘AI can do this, so why should I learn it?’ How do you address this mindset?

It’s less about students thinking, ‘AI can do this, why learn it?’ and more about the temptation to reach for this magic box with all the answers. Discomfort is where growth happens, but it’s uncomfortable – so AI becomes very tempting.

I tell my students: you won’t finish 100% of every project, and I’d much rather see a partially completed project you wrote yourself than a finished one written by AI. These courses are fast-paced, so I try to reduce the pressure for perfection, which is especially prevalent in Western cultures. We’re process-oriented, not results-oriented. We don’t care if the end product doesn’t look great – we care about the steps you took along the way.

Any other advice to discern whether AI-generated code is correct and appropriate?

For anything factual: verify, verify, verify. When using AI, always ask for links to resources. I advocate for going straight to official documentation – reading documentation is a skill that improves with practice.

You cannot trust factual information from LLMs because hallucinations happen with the same confidence as accurate information. If you’re unsure about anything, check it. We provide many internal resources through our learning management system, external links to documentation, and carefully vetted tutorial articles. This gives students a large body of verified information to reference without relying on unverified AI output.

Are your students prepared for an AI-enhanced workplace when they graduate?

We teach prompt engineering, a skill that dramatically affects LLM output quality. But the focus remains on writing as much code as possible – the gains from learning programming concepts multiply when you eventually incorporate LLMs.

We also emphasize reading code, which is similar to but distinct from writing code. As students progress, we provide increasingly complete starter repos to train them in navigating existing codebases. This skill is crucial for vibe coding since you do code reviews when working with AI assistants.

The sequence matters: we teach coding first, then provide guidance on integrating AI tools. Adding AI tools to your workflow is much easier than learning programming from scratch. Students need both skills – writing and reading code – to succeed in modern AI-enhanced workplaces.

Find out more and read WBS CODING SCHOOL reviews on Course Report. This article was produced by the Course Report team in partnership with WBS CODING SCHOOL.