Mind the Ethics! The Overlooked Ethical Dimensions of GenAI in Software Modeling Education

2025-09-17

AI Summary

Despite the widespread adoption of generative AI (GenAI) in software modeling education, its ethical dimensions—responsibility, fairness, transparency, diversity, and inclusion—have been systematically overlooked. Method: We conducted a systematic literature review across six authoritative databases (ACM, IEEE, etc.), screening 1,386 publications; only three explicitly addressed ethical concerns, confirming a substantial research gap and practical neglect. Contribution/Results: This study presents the first systematic ethical investigation into GenAI-assisted modeling instruction, revealing critical risks arising from the absence of ethical oversight—including adverse impacts on students’ diagrammatic reasoning and modeling competency development. We propose a structured governance framework anchored in five core ethical dimensions, offering both theoretical foundations and actionable guidelines for responsible, AI-enhanced modeling education.

Technology Category

• Philosophy and Ethics of AI: Artificial General Intelligence
• Machine Learning: Ethics, Bias, and Fairness
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

Abstract

Generative Artificial Intelligence (GenAI) is rapidly gaining momentum in software modeling education, embraced by both students and educators. As GenAI assists with interpreting requirements, formalizing models, and translating students' mental models into structured notations, it increasingly shapes core learning outcomes such as domain comprehension, diagrammatic thinking, and modeling fluency without clear ethical oversight or pedagogical guidelines. Yet, the ethical implications of this integration remain underexplored. In this paper, we conduct a systematic literature review across six major digital libraries in computer science (ACM Digital Library, IEEE Xplore, Scopus, ScienceDirect, SpringerLink, and Web of Science). Our aim is to identify studies discussing the ethical aspects of GenAI in software modeling education, including responsibility, fairness, transparency, diversity, and inclusion among others. Out of 1,386 unique papers initially retrieved, only three explicitly addressed ethical considerations. This scarcity highlights the critical absence of ethical discourse surrounding GenAI in modeling education and raises urgent questions about the responsible integration of AI in modeling curricula, as well as it evinces the pressing need for structured ethical frameworks in this emerging educational landscape. We examine these three studies and explore the emerging research opportunities as well as the challenges that have arisen in this field.

Problem

• Examining ethical oversight in GenAI for software modeling education
• Identifying scarcity of ethical discourse in AI-integrated curricula
• Addressing need for ethical frameworks in GenAI educational applications

Innovation

• Systematic literature review methodology
• Identified ethical framework scarcity
• Proposed structured ethical guidelines