How Do Programming Students Use Generative AI?
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 23 of its 52 references that are in the map. First posted on arXiv 17 Jan 2025.
Builds on
23 of 52 references.
References · 23
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11 more
- 2024The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmerscited by 46
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Built on by
Citing papers per year: 2025: 4; 2026: 5.
9 papers.
- 2025Novice Developers' Perspectives on Adopting LLMs for Software Development: A Systematic Literature Reviewcited by 3
- 2025Experimental Analysis of Productive Interaction Strategy with ChatGPT: User Study on Function and Project-level Code Generation Taskscited by 0
- 2025Examining the Usage of Generative AI Models in Student Learning Activities for Software Programmingcited by 0
- 2025From Pilots to Practices: A Scoping Review of GenAI-Enabled Personalization in Computer Science Educationcited by 0
- 2026Chatbot-Based Assessment of Code Understanding in Automated Programming Assessment Systemscited by 0
- 2026From Overload to Insights: How AI Agents Can Support Scientists in Analyzing Complex Datacited by 0
- 2026How Well Can AI Generate Backlogs from App Mockups?cited by 0
- 2026Patterns of Learner-AI Interaction and Academic Performance in an Object-Oriented Programming Coursecited by 0
- 2026Do Not Copy/Paste: Soft Barriers for Copying in AI-Assisted Programmingcited by 0
- 2026Do Not Copy/Paste: Soft Barriers for Copying in AI-Assisted Programming23 Aug 2026
- 2026How Well Can AI Generate Backlogs from App Mockups?24 Jul 2026
- 2026From Overload to Insights: How AI Agents Can Support Scientists in Analyzing Complex Data18 Jul 2026
- 2026Patterns of Learner-AI Interaction and Academic Performance in an Object-Oriented Programming Course26 May 2026
- 2026Chatbot-Based Assessment of Code Understanding in Automated Programming Assessment Systems8 Apr 2026
- 2025From Pilots to Practices: A Scoping Review of GenAI-Enabled Personalization in Computer Science Education23 Dec 2025
- 2025Examining the Usage of Generative AI Models in Student Learning Activities for Software Programming17 Nov 2025
- 2025Experimental Analysis of Productive Interaction Strategy with ChatGPT: User Study on Function and Project-level Code Generation Tasks6 Aug 2025
- 2025Novice Developers' Perspectives on Adopting LLMs for Software Development: A Systematic Literature Review10 Mar 2025