AI@AUP Learning about AI (curated resources)

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

Higher Education Generative AI Readiness Assessment Action Plan Joe Sabado and ChatGPT This document aims to extend the utility of the Educause Higher Education AI Readiness Assessment by providing an example action plan and the associated output (deliverables) and outcomes (business benefits) fo...

Wonder Tools helps you discover the most useful sites and apps. Click to read Wonder Tools, a Substack publication with tens of thousands of subscribers.

Email-only newsletter from AILA→ AI in the Liberal Arts (Amherst College)

Trying to understand the implications of AI for work, education, and life. By Prof. Ethan Mollick. Click to read One Useful Thing, by Ethan Mollick, a Substack publication with hundreds of thousands of subscribers.

A newsletter from Dr. Lance Eaton exploring AI and education through reflection, conversation, and sharing of insights, challenges, and insights.

AI4LAM is an international, participatory community focused on advancing the use of artificial intelligence in, for and by libraries, archives and museums.

(discussion list requiring EDUCAUSE account – AUP faculty & staff can create accounts)

Comprehensive guide to prompt engineering techniques for Claude's latest models, covering clarity, examples, XML structuring, thinking, and agentic systems.

The leading independent directory of AI tools for education. Find, compare, and review AI tools for your classroom with trusted reviews, safety data, and independent editorial content.

(AI projects, data sets, resources for libraries, archives and museums)

(Anna Mills)

(Lance Eaton)

Rather than try to ban this technology from classrooms outright, this project asks if this moment offers an opportunity to introduce students to the ethical and economic questions wreaked by these new tools, as well as to consider fresh new forms of pedagogy that can exploit them.

(Ethan R. Mollick and Lilach Mollick)

This open crowdsourced collection presents a rich tapestry of our collective thinking in the first months of 2023 stitching together potential alternative uses and applications of Artificial Intell…

(metaLAB (at) Harvard)

A guide for faculty using ChatGPT to understand, access, and build Custom GPTs through OpenAI. Includes potential use cases.

This post is the second in the Beyond ChatGPT series about generative AI’s impact on learning.

Recently, there has been considerable interest in large language models: machine learning systems which produce human-like text and dialogue. Applications of these systems have been plagued by persistent inaccuracies in their output; these are often called “AI hallucinations”. We argue that these falsehoods, and the overall activity of large language models, is better understood as bullshit in the sense explored by Frankfurt (On Bullshit, Princeton, 2005): the models are in an important way indifferent to the truth of their outputs. We distinguish two ways in which the models can be said to be bullshitters, and argue that they clearly meet at least one of these definitions. We further argue that describing AI misrepresentations as bullshit is both a more useful and more accurate way of predicting and discussing the behaviour of these systems.

The recent rise in artificial intelligence systems, such as ChatGPT, poses a fundamental problem for the educational sector. In universities and schools, many forms of assessment, such as coursework, are completed without invigilation. Therefore, students could hand in work as their own which is in fact completed by AI. Since the COVID pandemic, the sector has additionally accelerated its reliance on unsupervised ‘take home exams’. If students cheat using AI and this is undetected, the integrity of the way in which students are assessed is threatened. We report a rigorous, blind study in which we injected 100% AI written submissions into the examinations system in five undergraduate modules, across all years of study, for a BSc degree in Psychology at a reputable UK university. We found that 94% of our AI submissions were undetected. The grades awarded to our AI submissions were on average half a grade boundary higher than that achieved by real students. Across modules there was an 83.4% chance that the AI submissions on a module would outperform a random selection of the same number of real student submissions.

From AI to A+: Prepare Your Students for Using ChatGPT and other AI The ethical use of AI tools (including ChatGPT, PhotoMath, Grammarly, Bing, and more) is not an “all or nothing” discussion …

A principles-based approach in deciding if and how to incorporate artificial intelligence tools into course development, research and other work projects.

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