# Early Adoption of Generative Artificial Intelligence in Computing Education: Emergent Student Use Cases and Perspectives in 2023

arXiv: https://arxiv.org/abs/2411.11166 · HTML: https://mlsanity.com/paper/2411.11166/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2024.

## Built on by (most cited first, 5 of 5)

- [The Evolving Usage of GenAI by Computing Students](https://mlsanity.com/paper/2412.16453/) (2024)
- [A Scaffolded GenAI Lab in Early Undergraduate CS: A Mixed-Methods, Multi-Course Evaluation](https://mlsanity.com/paper/2505.00100/) (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)
- [Revisiting the Performance of Generative Artificial Intelligence on Introductory Object-Oriented Programming Assessments: Insights from 2026](https://mlsanity.com/paper/2608.16318/) (2026)

## Abstract (arXiv)

Because of the rapid development and increasing public availability of Generative Artificial Intelligence (GenAI) models and tools, educational institutions and educators must immediately reckon with the impact of students using GenAI. There is limited prior research on computing students' use and perceptions of GenAI. In anticipation of future advances and evolutions of GenAI, we capture a snapshot of student attitudes towards and uses of yet emerging GenAI, in a period of time before university policies had reacted to these technologies. We surveyed all computer science majors in a small engineering-focused R1 university in order to: (1) capture a baseline assessment of how GenAI has been immediately adopted by aspiring computer scientists; (2) describe computing students' GenAI-related needs and concerns for their education and careers; and (3) discuss GenAI influences on CS pedagogy, curriculum, culture, and policy. We present an exploratory qualitative analysis of this data and discuss the impact of our findings on the emerging conversation around GenAI and education.