How Do Programming Students Use Generative AI?

TL;DR

This study investigates how programming students use ChatGPT, revealing prevalent strategies like seeking knowledge and generating solutions, and highlights concerns about overreliance impacting learning and critical thinking.

Contribution

It provides empirical insights into students' actual usage patterns of generative AI in programming tasks and discusses implications for education and future research.

Findings

Students often prompted ChatGPT to generate full solutions.

Two main usage strategies identified: seeking knowledge and generating solutions.

Overreliance may reduce students' critical thinking and problem-solving skills.

Abstract

Programming students have a widespread access to powerful Generative AI tools like ChatGPT. While this can help understand the learning material and assist with exercises, educators are voicing more and more concerns about an overreliance on generated outputs and lack of critical thinking skills. It is thus important to understand how students actually use generative AI and what impact this could have on their learning behavior. To this end, we conducted a study including an exploratory experiment with 37 programming students, giving them monitored access to ChatGPT while solving a code authoring exercise. The task was not directly solvable by ChatGPT and required code comprehension and reasoning. While only 23 of the students actually opted to use the chatbot, the majority of those eventually prompted it to simply generate a full solution. We observed two prevalent usage strategies: to seek knowledge about general concepts and to directly generate solutions. Instead of using the bot to comprehend the code and their own mistakes, students often got trapped in a vicious cycle of submitting wrong generated code and then asking the bot for a fix. Those who self-reported using generative AI regularly were more likely to prompt the bot to generate a solution. Our findings indicate that concerns about potential decrease in programmers' agency and productivity with Generative AI are justified. We discuss how researchers and educators can respond to the potential risk of students uncritically over-relying on Generative AI. We also discuss potential modifications to our study design for large-scale replications.