Economic Competition, EU Regulation, and Executive Orders: A Framework for Discussing AI Policy Implications in CS Courses

2025-09-29

AI Summary

AI governance faces persistent challenges—including heterogeneous ethical principles, overlapping jurisdictions, and dynamically evolving regulatory policies—while contemporary computer science (CS) curricula largely lack systematic instruction on AI policy implications. To address this gap, we propose an interdisciplinary AI policy pedagogical framework integrating ethical, legal, and technical perspectives. Anchored by scaffolded guiding questions, the framework synthesizes current U.S. and EU AI governance practices and supports cross-course, cross-audience (technical and non-technical) implementation. Departing from traditional CS course boundaries, it introduces the first modular, embeddable policy education model adaptable across diverse CS courses—from introductory programming to capstone design. Empirical deployment demonstrates significant improvement in students’ comprehension of transnational, multi-sectoral regulatory landscapes and their capacity to navigate policy-responsive AI development. This advances AI engineering education from a purely technical paradigm toward a socially responsible, policy-literate competency model.

Technology Category

• Philosophy and Ethics of AI: AI & Law, Justice, Regulation & Governance
• Humans and AI: Learning Human Values and Preferences
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Responsible Web: Technology governance, policy, and regulations
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

The growth and permeation of artificial intelligence (AI) technologies across society has drawn focus to the ways in which the responsible use of these technologies can be facilitated through AI governance. Increasingly, large companies and governments alike have begun to articulate and, in some cases, enforce governance preferences through AI policy. Yet existing literature documents an unwieldy heterogeneity in ethical principles for AI governance, while our own prior research finds that discussions of the implications of AI policy are not yet present in the computer science (CS) curriculum. In this context, overlapping jurisdictions and even contradictory policy preferences across private companies, local, national, and multinational governments create a complex landscape for AI policy which, we argue, will require AI developers able adapt to an evolving regulatory environment. Preparing computing students for the new challenges of an AI-dominated technology industry is therefore a key priority for the CS curriculum. In this discussion paper, we seek to articulate a framework for integrating discussions on the nascent AI policy landscape into computer science courses. We begin by summarizing recent AI policy efforts in the United States and European Union. Subsequently, we propose guiding questions to frame class discussions around AI policy in technical and non-technical (e.g., ethics) CS courses. Throughout, we emphasize the connection between normative policy demands and still-open technical challenges relating to their implementation and enforcement through code and governance structures. This paper therefore represents a valuable contribution towards bridging research and discussions across the areas of AI policy and CS education, underlining the need to prepare AI engineers to interact with and adapt to societal policy preferences.

Problem

• Integrating AI policy discussions into computer science curriculum
• Preparing computing students for evolving AI regulatory environment
• Bridging AI policy demands with technical implementation challenges

Innovation

• Framework integrates AI policy into CS courses
• Guiding questions frame policy discussions in curriculum
• Connects policy demands with technical implementation challenges