Learning to code when AI assistants already master the skill
The revelation came in the summer of 2023, when I took on a high school student as a summer intern. Their task was to develop a machine learning model to predict air quality in our city, using Jupyter notebooks, basic Python and scikit-learn.
One day, I was discussing the performance of the algorithm with my intern and asked them to change a graph: instead of plotting the predicted versus true values, I asked them to show the difference between the predicted and true values.
The student switched to another browser tab, prompted ChatGPT to “Calculate the difference between two arrays y1 and y2” and continued to copy and paste the answer “y1 — y2” into the notebook.
At first I was amused that they would ask the AI assistant for a line of code that is so simple, and certainly faster to write yourself than to prompt, wait, and copy and paste. But then I started thinking about the implications of AI assistants for the way we teach software development and the learning outcomes for students.
In what follows, I outline the implications of the rise of AI assistants for the teaching of coding skills, based on my personal experience as an undergraduate and graduate instructor. I argue for accepting AI assistants in the classroom, rather than trying to restrict their use. Assignments and exams should take into account the use of AI assistants and assess skills that are not — yet — covered by AI. However, students should be given the opportunity to develop their own coding skills, rather than relying on AI technology for every part of their learning journey.
How does learning actually work?
There is a famous quote attributed to the Chinese philosopher Confucius:
“I hear and I forget. I see and I remember. I do and I understand.”
Both in my own training and in teaching others, I have found this to be true. In education and psychology, the last part of the quote is known as transfer of learning [1]. Students progress through tasks of increasing complexity…
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