# Students' Perceptions and Use of Generative AI Tools for Programming Across Different Computing Courses

arXiv: https://arxiv.org/abs/2410.06865 · HTML: https://mlsanity.com/paper/2410.06865/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 9 Oct 2024.

## Builds on (most cited first, 12 of 12)

- [Automatic Generation of Programming Exercises and Code Explanations using Large Language Models](https://mlsanity.com/paper/2206.11861/) (2022)
- [Programming Is Hard -- Or at Least It Used to Be: Educational Opportunities And Challenges of AI Code Generation](https://mlsanity.com/paper/2212.01020/) (2022)
- [Studying the effect of AI Code Generators on Supporting Novice Learners in Introductory Programming](https://mlsanity.com/paper/2302.07427/) (2023)
- [The Robots are Here: Navigating the Generative AI Revolution in Computing Education](https://mlsanity.com/paper/2310.00658/) (2023)
- ["It's Weird That it Knows What I Want": Usability and Interactions with Copilot for Novice Programmers](https://mlsanity.com/paper/2304.02491/) (2023)
- [The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmers](https://mlsanity.com/paper/2405.17739/) (2024)
- [The Effects of Generative AI on Computing Students' Help-Seeking Preferences](https://mlsanity.com/paper/2401.02262/) (2024)
- [Trust in Generative AI among students: An Exploratory Study](https://mlsanity.com/paper/2310.04631/) (2023)
- [Unveiling ChatGPT's Usage in Open Source Projects: A Mining-based Study](https://mlsanity.com/paper/2402.16480/) (2024)
- ["It's not like Jarvis, but it's pretty close!" -- Examining ChatGPT's Usage among Undergraduate Students in Computer Science](https://mlsanity.com/paper/2311.09651/) (2023)
- ["ChatGPT Is Here to Help, Not to Replace Anybody" -- An Evaluation of Students' Opinions On Integrating ChatGPT In CS Courses](https://mlsanity.com/paper/2404.17443/) (2024)
- [Experiences from Integrating Large Language Model Chatbots into the Classroom](https://mlsanity.com/paper/2406.04817/) (2024)

## Built on by (most cited first, 6 of 6)

- [Novice Developers' Perspectives on Adopting LLMs for Software Development: A Systematic Literature Review](https://mlsanity.com/paper/2503.07556/) (2025)
- [Evaluating Code Generation of LLMs in Advanced Computer Science Problems](https://mlsanity.com/paper/2504.14964/) (2025)
- [To Tab or Not to Tab: Measuring Critical Engagement in AI Code Completion Tools Using Behavioral Signals and Attention Checks](https://mlsanity.com/paper/2606.30549/) (2026)
- [Patterns of Learner-AI Interaction and Academic Performance in an Object-Oriented Programming Course](https://mlsanity.com/paper/2607.24755/) (2026)
- [When Who You Are Can Change the Code You Get: A Study of Persona-Induced Bias in LLM Code Generation](https://mlsanity.com/paper/2609.22102/) (2026)
- [Understanding Generative AI Use in Programming MOOCs: The Role of Course Context and Learner Characteristics](https://mlsanity.com/paper/2610.04447/) (2026)

## Abstract (arXiv)

Investigation of students' perceptions and opinions on the use of generative artificial intelligence (GenAI) in education is a topic gaining much interest. Studies addressing this are typically conducted with large heterogeneous groups, at one moment in time. However, how students perceive and use GenAI tools can potentially depend on many factors, including their background knowledge, familiarity with the tools, and the learning goals and policies of the courses they are taking. In this study we explore how students following computing courses use GenAI for programming-related tasks across different programs and courses: Bachelor and Master, in courses in which learning programming is the learning goal, courses that require programming as a means to achieve another goal, and in courses in which programming is optional, but can be useful. We are also interested in changes over time, since GenAI capabilities are changing at a fast pace, and users are adopting GenAI increasingly. We conducted three consecutive surveys (fall `23, winter `23, and spring `24) among students of all computing programs of a large European research university. We asked questions on the use in education, ethics, and job prospects, and we included specific questions on the (dis)allowed use of GenAI tools in the courses they were taking at the time. We received 264 responses, which we quantitatively and qualitatively analyzed, to find out how students have employed GenAI tools across 59 different computing courses, and whether the opinion of an average student about these tools evolves over time. Our study contributes to the emerging discussion of how to differentiate GenAI use across different courses, and how to align its use with the learning goals of a computing course.