Mapping LLM Misuse in Computing Education: A Survey-Based Risk Analysis of Faculty and Student Contexts

Document Type

Conference Proceeding

Source of Publication

International Conference on Evaluation of Novel Approaches to Software Engineering Enase Proceedings

Publication Date

1-1-2026

Abstract

Large Language Models (LLMs) have become deeply embedded in computing higher education, yet the misuse risks they introduce for faculty and students remain insufficiently understood from a cybersecurity and data privacy perspective. This paper presents an empirical study in which a structured survey of 105 participants at a computing college was used to identify and systematically risk-score thirteen LLM misuse cases across faculty and student contexts. Using a Likelihood × Impact scoring model, the resulting taxonomy classifies misuse cases as Critical, High, or Medium severity, with over-reliance and skill atrophy, academic integrity violations, and research integrity risks emerging as the highest-priority concerns. Targeted mitigation strategies addressing AI literacy development, institutional policy reform, and scaffolded LLM engagement are proposed in response. The findings contribute an empirically grounded, role-differentiated risk framework applicable to computing education institutions navigating responsible LLM integration.

ISBN

[9789897588280]

ISSN

2184-4895

Publisher

SCITEPRESS - Science and Technology Publications

Volume

1

First Page

587

Last Page

598

Disciplines

Computer Sciences

Keywords

Academic Integrity, AI Literacy, Computing Education, Cybersecurity Risk, Hallucination, Large Language Models, LLM Misuse, Risk Assessment

Scopus ID

105046605341

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Gold: This publication is openly available in an open access journal/series

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