Microwave Coding: How AI is Impacting Students' Deep Learning of Programming
In the fast-paced world of coding, AI offers quick solutions, but are students sacrificing true understanding for convenience? In this blog, we explore the difference between "microwave" learning and the slow, steady mastery of programming – and why the journey matters more than ever.
Mark Martin
10/5/20242 min read
Learning a new skill takes time and patience. It's never just about reaching the destination but about embracing the journey and the process. Think of it like cooking a Caribbean dish. If you microwave it for 10 minutes instead of slow-cooking it in the oven, the end result will be completely different in texture, flavour, and substance. The same applies to learning programming: rushing through the process won’t yield the same depth of understanding.
Good programming is an art, built on simplicity and readability. It’s about using:
Clear and meaningful variable, method, and class names
Code structures with a single, clear purpose
Consistent indentation and formatting
Minimal levels of code nesting for readability
And, of course, comments are there to tell a story, explaining what your code is doing and why it’s doing it.
Let’s get to the point: AI can offer quick, “microwave” solutions, and that can be a great help. When students are stuck or confused, AI can provide context and guidance. However, the real issue arises when students become overly reliant on these quick fixes, failing to consciously understand the underlying processes.
In programming – particularly in Python – we aim to teach students the classical or "pythonic" way of writing clean, efficient code. However, with the rise of AI tools, I’ve noticed that some bad coding habits have crept into AI-generated models, and students are copying these straight into their work, presenting it as their own.
As someone who’s been teaching programming for a while, I often feel like Inspector Columbo. It’s not difficult to spot when a student has used AI – their explanations are weak, and when I ask them to explain their work verbally, they often draw a blank.
Of course, there are the crafty ones who try to cover their tracks with slick prompts, but much like Columbo, I dig deeper. By asking complex questions, I can gauge whether they truly understand what they’ve written, or if they’ve just pieced it together from an AI response.
AI is here to stay, whether we like it or not. The real concern lies in whether students are taking a “microwave” or an “oven” approach to learning. Are they rushing through the concepts just to get to a solution, or are they taking the time to understand the fundamental principles of programming, such as data structures and algorithms?
There’s an old saying: “Knowledge is like building a house. Does AI give you sand or bricks?” When challenges come, like a storm in the form of complex coding problems, real world prorblems or technical interviews, we want our students to have built a strong foundation. We don’t want them to crumble because they relied on Ai solutions that they didn’t fully understand.
To wrap up this mini reflection post: Incorporating AI into learning isn’t inherently bad. In fact, it can be a powerful tool when used correctly. The key is balance. AI can act as a guide, offering useful hints and solutions when students are stuck, but it should never replace the deep work required to truly learn how to code. It’s important for students to not only use AI responsibly but also to challenge themselves to build their skills without over-reliance on it. Ultimately, learning programming is about understanding, not just completing coding tasks. While AI can help provide sand to fill in the gaps, it's up to the students to build their learning foundations with solid bricks of knowledge.