🏷️ policy-maker
25 pages tagged with policy-maker(23 articles, 2 concepts)
📄 The Tragedy of the Cognitive Commons: How AI Could Disrupt the Regeneration of Professional Expertise
> **Nolan Lovett** — Human Resource Development Review (author accepted manuscript, 2026).…
📄 What Does the Credential Still Certify? Cognitive Stewardship for AI-Mediated Education
Generative AI undermines a basic premise of educational assessment: that submitted work reliably evidences the human capacities a credential certifies. This paper proposes *cognitive stewardship*, a f…
📄 Did Alice Do Wrong? Cross-Cultural Differences in Student Perceptions of Generative AI Use in University Computing Education
A scenario-based survey (Fall 2024) comparing how computing students at Canadian and South Korean universities judged the ethicality and policy compliance of AI-assisted coding practices. Despite func…
📄 A Longitudinal Analysis of Public Discourse on AI Ethics in Education Using Twitter Data
**Akriti Bagale, Nafisa Mehjabin, Ali Unlu, Aditya Johri, et al. (2026)** - George Mason University; University of Virginia. arXiv preprint. Bagale, A., Mehjabin, N., Unlu, A., Johri, A., et al. (2026…
📄 A Comparative Analysis of Institutional and Course Generative AI Policies within Higher Education: Implications for Instruction in Computing Education
> **Synthesis:** A comparative content analysis of institutional GenAI policies and computing-course syllabi in U.S. research-intensive universities, revealing a gap between broadly pro-use institutio…
📄 Programming Language Policy as an AI Literacy Equity Problem: A 15-Nation Comparative Analysis
Across 15 nations, the paper examines how secondary computer-science education embeds AI literacy into general-track subjects (Digital Literacy, ICT, TIC, SNT) rather than specialized tracks, creating…
📄 A bit of chaos and madness: The AI Assessment Scale and the work of assessment reform
📄 [PDF](https://arxiv.org/pdf/2606.26729) This study examines the implementation of the Artificial Intelligence Assessment Scale (AIAS), a structured framework for redesigning [[assessment|university…
📄 Structuring Transparency: Developing Domain-Specific Generative AI Declaration Frameworks in Higher Education
As [[generative-ai]] disrupts [[higher-ed]], institutions increasingly require students to declare AI use. However, generic binary declarations (e.g., "I used GenAI") fail to capture the nuanced appli…
📄 Knowing the Rules Is Not Enough: Student Regulatory Awareness and Use of GenAI in Higher Education
Bischof et al. investigate how students' awareness of [[generative-ai]] regulations relates to their perceived compliance and actual usage behavior in [[higher-ed]]. While previous research mainly exa…
📄 The Environmental Cost of LLMs in AIED: Reporting and Practices
> **Sabrina C. Eimler, Lukas Erle, Daniel Flood, Aditi Haiman, Luca Häckert, André Helgert, Lachlan McGinness, Büsra Yapici**…
📄 Report on CHIIR 2026 Workshop on Generative AI and Academic Search (GAI&AS)
**Yifan Liu, Jaime Arguello, Orland Hoeber, Chang Liu et al.** — cs.IR, cs.AI, cs.HC This report summarizes the CHIIR 2026 Workshop on Generative AI and Academic Search (GAI&AS), which examined how Ge…
🏷️ AI Plagiarism Detection
Technologies and methods for detecting AI-generated content in academic submissions, including classifier-based approaches, watermarking, and stylistic analysis. The effectiveness and reliability of t…
📄 Agentic Literacy Debt: A Structural Problem the AI Literacy Field Has Not Yet Named
**Agentic Literacy Debt** names a critical gap in the [[ai-literacy]] landscape that has become urgent with the rise of autonomous AI agents. Existing AI literacy frameworks assume humans evaluate AI …
📄 Mathematical Modelling of Ethical AI Use in Higher Education: A Coordination Game Framework for Future-Facing Learning
**Ethical AI Use in Higher Education: A Coordination Game Framework** provides a formal mechanism-level account of why policy statements alone fail to change student AI-use behavior. Reframing student…
📄 Position: Adopting AI in Practice Does Not Guarantee the Productivity Boost
This ICML 2026 position paper argues that adopting AI in organizational practice does not automatically yield productivity gains — human and environmental factors critically moderate the relationship.…
📄 Generative AI as a Design Variable: An Evidence-Centered Framework for Principled Governance in STEM Assessment
This paper proposes a principled framework grounded in Evidence-Centered Design (ECD) that treats [[generative-ai]] as a design variable within STEM assessment arguments rather than an external threat…
📄 Artificial Intelligence in Lifelong Learning: Opportunities and Challenges in Adult Education Policy
Theodora and Tselios (2026) provide a policy-oriented synthesis of AI's dual role in adult and [[lifelong-learning]] contexts — as both an enabler of personalized, scalable education and a source of s…
📄 What AI in Education Needs Next: Lessons from Youth Leaders Across Five Countries
> A global perspective on AI in education readiness, framed around the insight that the real bottleneck is human and institutional capacity, not technical access. Based on a WEF (2026) synthesis of yo…
📄 Higher Education Must Bridge the AI Gap
> A Science editorial by University of Illinois Chicago Chancellor Marie Lynn Miranda (April 2026) arguing that higher education has a narrow window to shape AI's distribution equitably. Proposes a th…
📄 The Impact of AI on Work in Higher Education
> A large-scale survey (n=1,960) by EDUCAUSE (2026) examining how AI is reshaping work in higher education institutions — attitudes, adoption patterns, institutional strategies, risks, and opportuniti…
2026-05-09 · higher-ed, faculty-development, administrator, market-analysis, faculty-development-genai
📄 A New Direction for Students in an AI World: Prosper, Prepare, Protect
> A yearlong global "premortem" by the Brookings Center for Universal Education (2026) examining generative AI's risks and benefits for students. Based on 500+ interviews across 50 countries, 400+ stu…
📄 How State Policy Can Help Teachers Use AI Well
> A NASBE/CRPE policy analysis (May 2026) examining how US states can shape conditions for effective teacher AI adoption — setting guardrails, providing resources, and building capacity without microm…
🏷️ AI from the Administrator Perspective
> Stub — pending source ingestion. AI adoption, strategy, and governance from the institutional administrator and leadership perspective.…
📄 The University AI Didn''t Replace: Rethinking Universities in the AI Era
> **Synthesis:** Rather than replacing universities, generative AI **redefines their essential functions** — this paper proposes a four-level framework of institutional AI adoption and argues that the…
📄 Principled AI in Education
> The framework rests on three interconnected anchors that must be addressed *before* selecting tools: > Rejecting the binary promise-vs-peril discourse and the rush to immediate implementation, Finke…