AI@AUP Learning about AI (curated resources)

"normalizing AI disclosure as a means of curbing the uncritical adoption of AI and restoring the trust between professors and students"

The increasing integration of AI tools into existing platforms raises new challenges, Zach Justus and Nik Janos write.

EDUCAUSE AI Literacy Programs for Faculty, Staff, and Students Working Group

Artificial Intelligence (AI) is a ubiquitous concept and tool already found across society and an integral part of everyday life. As such, basic under…

Both frameworks emphasize a human-centered mindset and AI ethics, as well as understanding of AI in order to use it in learning or teaching contexts

This chapter presents a roadmap for teacher educators to equip their graduates with the ability to recognize and mitigate the limitations and risks of using ChatGPT in educational settings. With the rise of artificial intelligence (AI), the incorporation of AI-powered technologies like ChatGPT has t...

Computers and Education: Artificial Intelligence (Fleckenstein et al.) – “Generative AI can simulate student essay writing in a way that is undetectable for teachers”

This study investigates the efficacy of six major Generative AI (GenAI) text detectors when confronted with machine-generated content modified to evade detection (n = 805). We compare these detectors to assess their reliability in identifying AI-generated text in educational settings, where they are increasingly used to address academic integrity concerns. Results show significant reductions in detector accuracy (17.4%) when faced with simple techniques to manipulate the AI generated content. The varying performances of GenAI tools and detectors indicate they cannot currently be recommended for determining academic integrity violations due to accuracy limitations and the potential for false accusation which undermines inclusive and fair assessment practices. However, these tools may support learning and academic integrity when used non-punitively. This study aims to guide educators and institutions in the critical implementation of AI text detectors in higher education, highlighting the importance of exploring alternatives to maintain inclusivity in the face of emerging technologies.

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.

(Florida International University Libraries)

MLA-CCCC Joint Task Force on Writing and AI

Demystifying AI is a series of short videos explaining algorithms and AI, answering questions including "what is an algorithm?", "what is AI bias?", and "how...

This post outlines how to create references for large language model AI tools like ChatGPT and how to present AI-generated text in a paper.

Learn more about MinnaLearn's and the University of Helsinki's AI course - no programming or complicated math required.

covers university-wide (staff) use of AI

international examples of institutional policies & guidelines

Torrey Trust facilitates the MYFest session held on 27 June 2024 with the full title: Thinking Machines? Or Lying, Cheating, and Stealing Machines? This inte...

It’s been over 12 months since the first blog post about the AI Assessment Scale, and a lot has changed, both with the technology and with our understandings of how it impacts assessments in …

partially crowdsourced but curated collection of links (with brief descriptions) to teaching resources and strategies

Learn about Artificial Intelligence (AI) and how it's transforming higher education. This guide explores what AI is, its applications, and how it's shaping careers.

(Torrey Trust)