"The teachers are confused as well": A Multiple-Stakeholder Ethics Discussion on Large Language Models in Computing Education

TL;DR

This study investigates ethical concerns of large language models in computing education by interviewing 20 CS education stakeholders. It identifies three mental models for LLM usage—Writing Tool, Coding Tool, and Information Tool—and documents concerns around inaccuracy, hallucinations, bias, privacy, and academic integrity. The authors argue for contextual, non-bans governance and proactive digital-literacy education to prepare students and faculty for responsible LLM use. The findings inform policy planning, curricula redesign, and governance strategies that balance innovation with learning integrity and privacy protections.

Abstract

Large Language Models (LLMs) are advancing quickly and impacting people's lives for better or worse. In higher education, concerns have emerged such as students' misuse of LLMs and degraded education outcomes. To unpack the ethical concerns of LLMs for higher education, we conducted a case study consisting of stakeholder interviews (n=20) in higher education computer science. We found that students use several distinct mental models to interact with LLMs - LLMs serve as a tool for (a) writing, (b) coding, and (c) information retrieval, which differ somewhat in ethical considerations. Students and teachers brought up ethical issues that directly impact them, such as inaccurate LLM responses, hallucinations, biases, privacy leakage, and academic integrity issues. Participants emphasized the necessity of guidance and rules for the use of LLMs in higher education, including teaching digital literacy, rethinking education, and having cautious and contextual policies. We reflect on the ethical challenges and propose solutions.

Figures (1)

• Figure 1: LLM user mental models in CS education.