# Beyond the Benefits: A Systematic Review of the Harms and Consequences of Generative AI in Computing Education

arXiv: https://arxiv.org/abs/2510.04443 · HTML: https://mlsanity.com/paper/2510.04443/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2025.

## Built on by (most cited first, 4 of 4)

- [When Looks Do Not Lie: Discourse Structure Guided In-Context Learning for Faithful Diagram Generation](https://mlsanity.com/paper/2601.20476/) (2026)
- [Exploring the Value of Diverse LLM Explanations in Introductory Programming](https://mlsanity.com/paper/2606.28882/) (2026)
- [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)
- [Flawed but Memorable: Student Critical Reception of Interest-Personalized GenAI Analogies in Computing Education](https://mlsanity.com/paper/2609.06095/) (2026)

## Abstract (arXiv)

Generative artificial intelligence (GenAI) has already had a big impact on computing education with prior research identifying many benefits. However, recent studies have also identified potential risks and harms. To continue maximizing AI benefits while addressing the harms and unintended consequences, we conducted a systematic literature review of research focusing on the risks, harms, and unintended consequences of GenAI in computing education. Our search of ACM DL, IEEE Xplore, and Scopus (2022-2025) resulted in 1,677 papers, which were then filtered to 224 based on our inclusion and exclusion criteria. Guided by best practices for systematic reviews, four reviewers independently extracted publication year, learner population, research method, contribution type, GenAI technology, and educational task information from each paper. We then coded each paper for concrete harm categories such as academic integrity, cognitive effects, and trust issues. Our analysis shows patterns in how and where harms appear, highlights methodological gaps and opportunities for more rigorous evidence, and identifies under-explored harms and student populations. By synthesizing these insights, we intend to equip educators, computing students, researchers, and developers with a clear picture of the harms associated with GenAI in computing education.