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

2025-10-05

AI Summary

This study systematically examines risks arising from generative AI (GenAI) in computer science education, including academic integrity violations, cognitive skill atrophy, and human-AI trust imbalances. Employing a systematic literature review, we analyzed publications from ACM Digital Library, IEEE Xplore, and Scopus (2022–2025), applying multi-stage screening and four-way independent coding to identify 224 high-quality studies from an initial pool of 1,677. We propose the first comprehensive risk taxonomy for GenAI in educational contexts, revealing critical gaps—particularly uneven student demographic coverage and insufficient empirical investigation into subtle harms such as implicit cognitive dependency and assessment invalidation. The resulting risk cognition map provides educators with an actionable, evidence-grounded framework. By foregrounding methodological rigor and empirical validation, this work advances the field beyond anecdotal discourse toward robust, intervention-ready scholarship and informs evidence-based policy and pedagogical design.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content

Abstract

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.

Problem

• Systematically reviews harms and unintended consequences of generative AI in computing education
• Identifies academic integrity, cognitive effects, and trust issues as key concerns
• Analyzes patterns of GenAI harms across educational contexts and student populations

Innovation

• Conducted systematic literature review on GenAI harms
• Coded papers for harm categories like academic integrity
• Identified methodological gaps and under-explored student populations