Course title:
“Learn AI-powered Python programming”
What sparked the concept for the course?
Generative AI is really good at computer programming – to the purpose where the way in which we teach and assess students learning to code needs to alter.
We used to present students dozens or a whole bunch of small, focused programming tasks through which we learned every aspect of the syntax—the words and symbols—of programming. This worked well as a place to begin, except now generative AI tools can solve all of those problems. Educators can attempt to ban these tools (good luck with that!) or embrace them. We decided to incorporate them in our latest course, where students learn to code – supported by a generative AI assistant.
What might be examined within the course?
The course reimagines what learning to code means now that generative AI is out there to resolve more of the low-level syntax problems which have slowed and frustrated students up to now. The more students struggle with tricky syntax details, the less time and energy they’ve to attain their program-related goals, comparable to: B. starting a business, writing apps for social causes, or contributing to projects which can be meaningful to them.
Generative AI allows us to concentrate on more worthwhile, sophisticated skills that students must grow to be effective programmers. For example, generative AI struggles to resolve large problems; We still need humans to interrupt these problems down into bite-sized pieces – a process often called problem decomposition – and which AI can solve each well. People are still needed to check the code to be certain that it does what it was intended to do and to be certain that the code is designed to assist, not harm, society and its vulnerable groups.
Why is that this course relevant now?
Many skilled programmers have already introduced generative AI tools and are using them to make their day by day work more efficient. If the goal is to organize students for these tasks, teachers must train them to make use of these latest tools.
Perhaps more importantly, students’ options in introductory courses are changing. With a more powerful tool comes a capability to work at higher and more efficient levels. These tools save people time.
What is a very important lesson from the course?
An vital lesson is that while generative AI is impressive, it is usually fallible. You cannot just ask for code and assume the code you get is ideal. It may not do the appropriate thing. It can result in errors, or bugs. This may cause safety concerns. It can exclude underrepresented groups or discourses. You must critically examine the code you get through generative AI.
What materials does the course include?
The course is predicated on our latest book “Learn AI-powered Python programming.” The book reimagines an introductory course in programming within the context of generative AI tools.
The primary tool utilized in the book and our course is named GitHub Copilot, which is like ChatGPT for programmers. Students use Copilot from day one. They create complete apps: apps to automate tedious, error-prone tasks; computer games; even an app to guess who wrote a novel whose creator could also be unknown. To ensure students proceed to learn the fundamentals, the book teaches them to know the code that generative AI creates.
What does the course prepare students for?
Some students take an introductory programming course to start their computer science major. For these students, we proceed to show foundational skills like reading code and testing code, but now we’re also introducing the higher-level skill of problem decomposition so students can solve greater problems than ever before.
However, nearly all of students in this system study other disciplines comparable to sociology, psychology, economics, engineering and natural sciences. The course prepares these students to make use of generative AI to spice up their careers through programming.
This article was originally published at theconversation.com
