AI@AUP Learning about AI (curated resources)

Knowing when to use AI and when to leave it the hell alone. Click to read Slow AI, by Dr Sam Illingworth, a Substack publication with tens of thousands of subscribers.

White paper extending Selber's Functional/Critical/Rhetorical literacy framework to AI

Forman Christian College

Join AMICAL colleagues for an event on developing campus-level responses to generative AI.

University of Toronto

Initiative at Amherst College

Discussino forum for members of the AMICAL Consortium (including AUP faculty and staff)

(Joe Sabado)

(metaLAB (at) Harvard)

(Lance Eaton)

Collection of university policies and websites. Questions? Contact [email protected]

(Joe Sabado)

(ARL)

This article addresses the ethical challenges posed by generative artificial intelligence (AI) tools in higher education and explores the first responses of universities to these challenges globally. Drawing on five key international documents from the UN, EU, and OECD, the study used content analysis to identify key ethical dimensions related to the use of generative AI in academia, such as accountability, human oversight, transparency, or inclusiveness. Empirical evidence was compiled from 30 leading universities ranked among the top 500 in the Shanghai Ranking list from May to July 2023, covering those institutions that already had publicly available responses to these dimensions in the form of policy documents or guidelines. The paper identifies the central ethical imperative that student assignments must reflect individual knowledge acquired during their education, with human individuals retaining moral and legal responsibility for AI-related wrongdoings. This top-down requirement aligns with a bottom-up approach, allowing instructors flexibility in determining how they utilize generative AI especially large language models in their own courses. Regarding human oversight, the typical response identified by the study involves a blend of preventive measures (e.g., course assessment modifications) and soft, dialogue-based sanctioning procedures. The challenge of transparency induced the good practice of clear communication of AI use in course syllabi in the first university responses examined by this study.

This study aims to develop an AI education policy for higher education by examining the perceptions and implications of text generative AI technologies. Data was collected from 457 students and 180 teachers and staff across various disciplines in Hong Kong universities, using both quantitative and qualitative research methods. Based on the findings, the study proposes an AI Ecological Education Policy Framework to address the multifaceted implications of AI integration in university teaching and learning. This framework is organized into three dimensions: Pedagogical, Governance, and Operational. The Pedagogical dimension concentrates on using AI to improve teaching and learning outcomes, while the Governance dimension tackles issues related to privacy, security, and accountability. The Operational dimension addresses matters concerning infrastructure and training. The framework fosters a nuanced understanding of the implications of AI integration in academic settings, ensuring that stakeholders are aware of their responsibilities and can take appropriate actions accordingly.

ChatGPT recently turned one and what a wild, first year it has been. Over the last twelve months, institutions have scrambled to not only better understand generative Artificial Intelligence (AI) and its impact on teaching and learning, but also to determine the best ways to provide guardrails and guidance for faculty, staff, and students. Many […]

Particularly for new technologies that disrupt long-standing practices and cultural beliefs, the work of carefully and intentionally developing effect

Concilier le développement de systèmes d’IA avec les enjeux de protection de la vie privée De nombreux acteurs ont fait part à la CNIL de questionnements concernant l’application du règlement général sur la protection des données (RGPD) à l’intelligence artificielle (IA), en particulier depuis l’émergence de systèmes d’IA génératives (« Generative AI systems »).

(MIT strategy guide for addressing AI at higher ed institutions)

Our expert panel examines the preferred future of GenAI in higher education and provides an action plan to help you get there.

Particularly for new technologies that disrupt long-standing practices and cultural beliefs, the work of carefully and intentionally developing effect

The Higher Education Generative AI Readiness Assessment is designed to provide a sense of your institution’s preparedness for strategic AI initiatives