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Explore how AI coding tools like GitHub Copilot can accelerate your journey to learn new programming languages.
The days of the single-language developer are fading. While companies like Shutterstock built empires on a single language (Perl in their case), the landscape has shifted. Today’s developers are expected to navigate a diverse technological landscape and start building projects that require proficiency in a range of languages and frameworks. This increased demand for versatility can feel quite overwhelming.
Thankfully, AI coding tools like GitHub Copilot, cursor.sh, and phind are emerging to empower developers in their learning journeys. These broadly available and adaptable tools offer real-time assistance and personalized guidance, making learning new languages faster and more efficient for developers of all experience levels.
In this post, we will:
Meet Kedasha Kerr and Alessio Fiorentino. Kedasha is one of GitHub’s own developer advocates, and Alessio is a DevOps architect and GitHub user.
As a seasoned JavaScript developer, Kedasha embarked on a machine learning course to deepen her understanding of AI. However, the course curriculum required her to learn Python, a completely new language for her. To combat the learning curve, Kedasha turned to GitHub Copilot’s chat functionality. This feature allowed her to interact with the AI in a conversational manner to ask questions about Python syntax, best practices, and even seek help with specific coding challenges she had for coursework.
“I had dabbled with Python, but I had never used it seriously or built anything with it. So, I used Copilot to help me, especially with conditionals. I would see an example from the professor, then I would go to Copilot and ask for it to explain conditionals to me as if I was a high school student with zero coding experience. I even asked it to draw a diagram to show me how data flows in Python, and Copilot coupled with Mermaid literally walked me through the explanation,” Kedasha explains.
“I used these tools to explain context and help me visualize the things that I was trying to learn. And if I was ever totally confused, I would just pop problems into the chat interface and ask it to break things down for me step by step. Honestly, it’s been a great learning buddy,” she adds.
Similarly, Alessio sought out AI coding tools to help him learn Rust for a new project he was working on. Here’s what Alessio had to say about using AI to assist his learning journey:
 “One of the key benefits of using AI is that it helps me learn and write better Rust code. Rust is a powerful language that provides full control over the execution flow, but it has many nuances and requires a different way of thinking, especially for those who started with Python or JavaScript. AI assists me in navigating these complexities and ensures that I write efficient and idiomatic Rust code. One of the standout features of AI is its ability to help me get straight to the problem without the need for trial and error in finding the right search terms. By providing context through prompts, AI delivers focused and relevant results. In addition to Rust, AI aids me in working with frameworks that I’m less familiar with. For example, it provides in-depth guidance when I’m using FastAPI for backend development or Svelte for frontend development. This saves me a lot of time and effort in understanding and implementing these frameworks effectively. While I believe in the importance of reading official documentation to gain a solid foundation, AI coding tools become incredibly valuable when tackling more complex and nuanced problems. It’s like a ‘training on the job 2.0’ experience, where you start with a little initial knowledge but are rapidly accelerated in becoming more productive with the assistance of AI.” |
Though we acknowledge that these are individual experiences, they showcase the power of AI coding tools in language acquisition. AI coding tools helped both Kedasha and Alessio by acting as a personalized learning companion, while offering contextual guidance and reducing the time spent on tedious tasks. This anecdotal evidence hopefully can serve as inspiration for other developers, as well as pave the way for further research into the measurable impact of AI on the learning process.
We gathered a few valuable tips for you to keep in your back pocket as you start learning with AI, but before we jump into those, we want you to keep this in mind:
AI tools are assistants, not replacements. They can suggest code, catch errors, and provide explanations, but you still need to understand the core concepts of the language. Don’t solely rely on AI-generated code—sometimes the suggestions are wrong. It’s important to always analyze outputs, understand why it works, and learn the underlying principles of the specific language.
Optimize your learning environment. This begins with exploring different AI coding tools and finding one that suits your learning style and the language you want to learn. It’s also important to supplement these tools with traditional learning practices, such as online tutorials, textbooks, or video courses. These can provide a more structured learning path, as well as more in-depth explanations for certain concepts.
💡 Did you know?
Copilot supports most programming languages. Since it was trained on billions of lines of public code and natural language, Copilot could be a good option for you. 👀
Don’t be afraid to experiment! Use the AI as a safety net—try different approaches to problems and see how the AI reacts so you can learn both from successes and errors. For example, let the AI suggest code snippets, but try to actively think about the suggestions and why they will (or won’t) work. You can also practice with error correction by letting the AI highlight errors and using them as learning opportunities to identify and rectify mistakes in your code.
Be specific and give context. When you’re learning a new programming language with AI coding tools, providing context is crucial for two main reasons:
It’s best to treat prompts as discrete and atomized tasks to get to an end result. Not only will this help you build better prompts for the AI, but it will also help you better articulate what you are trying to achieve.
Reach out to developers in the community. Developers who are actively experimenting with AI or are fluent in the language you’re trying to learn will provide more help than this blog post ever could. GitHub Community discussions are a great place to find folks with similar interests or answers to questions on numerous topics. For example, you could check out the Copilot discussion to learn more about Copilot to see if it’s the right fit for your toolkit to learn a new language!
💡Interested in 25 more tips and tricks for using Copilot in your IDE?
Check out this blog post, where Kedasha shares her insider knowledge of some Copilot best practices. Or watch this awesome video where she gives you a full demo on how to use Copilot for your data science projects.
Now that you’ve explored how to use AI to learn a new programming language, here are some of the benefits you can expect to take advantage of:
Scenario: you’re a beginner learning Python and you want to write a simple script to calculate the area of a rectangle. Initial stage:
As you progress:
Focus on best practices: the AI might highlight areas for improvement, suggesting ways to make your code more efficient or readable. |
The landscape of software development is evolving, and AI coding tools are at the forefront of this transformation. With these tools, you can explore your own programming language interests, streamline your skill acquisition journey, and ultimately feel empowered to stay competitive in your career. Plus, the opportunities they offer to democratize access to programming knowledge and accelerate the growth of skilled developers everywhere is pretty exciting.
Check out this blog post to learn more exciting ways you can use Copilot with your projects.
If you’d like to try out GitHub Copilot as an aid to your learning journey, start your free trial here.
