AI Governance in Higher Education: A course design exploring regulatory, ethical and practical considerations
- URL: http://arxiv.org/abs/2509.06176v2
- Date: Tue, 16 Sep 2025 15:37:52 GMT
- Title: AI Governance in Higher Education: A course design exploring regulatory, ethical and practical considerations
- Authors: Raphaël Weuts, Johannes Bleher, Hannah Bleher, Rozanne Tuesday Flores, Guo Xuanyang, Paweł Pujszo, Zsolt Almási,
- Abstract summary: We propose a modular, interdisciplinary curriculum that integrates technical foundations with ethics, law and policy.<n>We highlight recurring operational failures in AI - bias, misspecified objectives, generalization errors, misuse and governance breakdowns.<n>The curriculum aims to prepare students to diagnose risks, navigate regulation and engage diverse stakeholders.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: As artificial intelligence (AI) systems permeate critical sectors, the need for professionals who can address ethical, legal and governance challenges has become urgent. Current AI ethics education remains fragmented, often siloed by discipline and disconnected from practice. This paper synthesizes literature and regulatory developments to propose a modular, interdisciplinary curriculum that integrates technical foundations with ethics, law and policy. We highlight recurring operational failures in AI - bias, misspecified objectives, generalization errors, misuse and governance breakdowns - and link them to pedagogical strategies for teaching AI governance. Drawing on perspectives from the EU, China and international frameworks, we outline a semester plan that emphasizes integrated ethics, stakeholder engagement and experiential learning. The curriculum aims to prepare students to diagnose risks, navigate regulation and engage diverse stakeholders, fostering adaptive and ethically grounded professionals for responsible AI governance.
Related papers
- Mirror: A Multi-Agent System for AI-Assisted Ethics Review [104.3684024153469]
Mirror is an agentic framework for AI-assisted ethical review.<n>It integrates ethical reasoning, structured rule interpretation, and multi-agent deliberation within a unified architecture.
arXiv Detail & Related papers (2026-02-09T03:38:55Z) - Trustworthiness of Legal Considerations for the Use of LLMs in Education [0.0]
This paper offers a comparative analysis of AI-related regulatory and ethical frameworks across key global regions.<n>It maps how core trustworthiness principles, such as transparency, fairness, accountability, data privacy, and human oversight are embedded in regional legislation and AI governance structures.<n>The paper contributes practical guidance for building legally sound, ethically grounded, and culturally sensitive AI systems in education.
arXiv Detail & Related papers (2025-08-05T07:44:33Z) - Media and responsible AI governance: a game-theoretic and LLM analysis [61.132523071109354]
This paper investigates the interplay between AI developers, regulators, users, and the media in fostering trustworthy AI systems.<n>Using evolutionary game theory and large language models (LLMs), we model the strategic interactions among these actors under different regulatory regimes.
arXiv Detail & Related papers (2025-03-12T21:39:38Z) - Balancing Innovation and Integrity: AI Integration in Liberal Arts College Administration [0.0]
It examines AI's opportunities and challenges in academic and student affairs, legal compliance, and accreditation processes.<n>Considering AI's value pluralism and potential allocative or representational harms caused by algorithmic bias, LACs must ensure AI aligns with its mission and principles.
arXiv Detail & Related papers (2025-02-20T18:16:11Z) - What is Ethical: AIHED Driving Humans or Human-Driven AIHED? A Conceptual Framework enabling the Ethos of AI-driven Higher education [0.6216023343793144]
This study introduces the Human-Driven AI in Higher Education (HD-AIHED) Framework to ensure compliance with UNESCO and OECD ethical standards.<n>The study applies a participatory co-system, Phased Human Intelligence, SWOC analysis, and AI ethical review boards to assess AI readiness and governance strategies for universities and HE institutions.
arXiv Detail & Related papers (2025-02-07T11:13:31Z) - Technology as uncharted territory: Contextual integrity and the notion of AI as new ethical ground [51.85131234265026]
I argue that efforts to promote responsible and ethical AI can inadvertently contribute to and seemingly legitimize this disregard for established contextual norms.<n>I question the current narrow prioritization in AI ethics of moral innovation over moral preservation.
arXiv Detail & Related papers (2024-12-06T15:36:13Z) - From Principles to Practice: A Deep Dive into AI Ethics and Regulations [13.753819576072127]
The article thoroughly analyzes the ground-breaking AI regulatory framework proposed by the European Union.<n>Considering the technical efforts and strategies undertaken by academics and industry to uphold these principles, we explore the synergies and conflicts among the five ethical principles.
arXiv Detail & Related papers (2024-12-06T00:46:20Z) - Developing and Deploying Industry Standards for Artificial Intelligence in Education (AIED): Challenges, Strategies, and Future Directions [22.65961106637345]
The adoption of Artificial Intelligence in Education (AIED) holds the promise of revolutionizing educational practices.
The lack of standardized practices in the development and deployment of AIED solutions has led to fragmented ecosystems.
This article aims to address the critical need to develop and implement industry standards in AIED.
arXiv Detail & Related papers (2024-03-13T22:38:08Z) - Towards Responsible AI in Banking: Addressing Bias for Fair
Decision-Making [69.44075077934914]
"Responsible AI" emphasizes the critical nature of addressing biases within the development of a corporate culture.
This thesis is structured around three fundamental pillars: understanding bias, mitigating bias, and accounting for bias.
In line with open-source principles, we have released Bias On Demand and FairView as accessible Python packages.
arXiv Detail & Related papers (2024-01-13T14:07:09Z) - Fairness in Agreement With European Values: An Interdisciplinary
Perspective on AI Regulation [61.77881142275982]
This interdisciplinary position paper considers various concerns surrounding fairness and discrimination in AI, and discusses how AI regulations address them.
We first look at AI and fairness through the lenses of law, (AI) industry, sociotechnology, and (moral) philosophy, and present various perspectives.
We identify and propose the roles AI Regulation should take to make the endeavor of the AI Act a success in terms of AI fairness concerns.
arXiv Detail & Related papers (2022-06-08T12:32:08Z) - An interdisciplinary conceptual study of Artificial Intelligence (AI)
for helping benefit-risk assessment practices: Towards a comprehensive
qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence.
The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
arXiv Detail & Related papers (2021-05-07T12:01:31Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.