Generative artificial intelligence (AI) is poised to revolutionize how humans work, and has already demonstrated promise in significantly improving human produc

Sarah Elaine Eaton facilitates the MYFest session held on 9 July 2024. In this session, Professor Eaton looks at the ethical implications of advancements in ...

The “queen of the internet” is full of something…

Anna Mills facilitates the MYFest session held on 2 July 2024. This interactive workshop surveys a range of AI-enabled research assistance apps that aim to help us find and analyze sources. We’ll look at general-purpose ones like Perplexity, ChatGPT4o, and Gemini as well as apps geared to academic research such as Elicit, Consensus, Keenious, ResearchRabbit, SciSpace, Scite_, and Undermind. In what ways do they facilitate source retrieval and analysis, and how can they also mislead us? What does wise use of these tools look like? Session slides here: https://bit.ly/SkepticalAIresearch

Jon Ippolito facilitates the MYFest session held on 27 June 2024 with the full title: Thinking Machines? Or Lying, Cheating, and Stealing Machines? This interactive workshop encourages participants to critically examine how and why generative AI tools were designed and what that means for their use in education. Topics of exploration include: Bias, Hallucinations, Exploitation of Human Labor, Data & Privacy, Digital Divide, Academic Integrity, and Intellectual Property Rights. As participants explore each of these topics, they will consider how to bring these critical issues into their practice and how to help prepare students to become critical AI users.

A new study shows that many people with autism embrace ChatGPT and similar AI tools for help and advice as they confront problems in their workplaces. But does that use of AI make sense?

My sincere thanks to those who helped support the creation of The Beyond ChatGPT series by subscribing to my newsletter. Your continued support has helped me carve out the time to research AI’s impact on skill development this summer. I’m committed to keeping the content of this series open to all. Moving forward, I’ll be revisiting each one of these past essays to explore ways educators can ethically use AI with students to help them learn or ways they can include intentional friction in the learning process to counter AI’s marketing promise of a frictionless learning experience.

Last week, Federico and I asked Robb Knight to do what he could to block web crawlers deployed by artificial intelligence companies from scraping MacStories. Robb had already updated his own site’s robots.txt file months ago, so that’s the first thing he did for MacStories. However, robots.txt only works if a company’s web crawler is

ChatGPT & Education Designed by Torrey Trust, Ph.D. College of Education University of Massachusetts Amherst @torreytrust | torrey@umass.edu This work is licensed under CC BY NC 4.0, meaning that you can freely use, remix, and share it as long as you give attribution and do not use it for commerc...

AI Text Detectors (aka AI Plagiarism Detectors & AI Content Detectors) Designed by Torrey Trust, Ph.D. College of Education University of Massachusetts Amherst @torreytrust | torrey@umass.edu This work is licensed under CC BY NC 4.0, meaning that you can freely use, remix, and share it as long as...

Why AI Detection Alone Will Fail Students

As a former secondary English teacher, senior examination assessor, and lecturer for initial teacher education, I understand the allure of using Generative AI (GenAI) for grading student work. We&#…

Days after former employees said the company was being too reckless with its technology, OpenAI released a research paper on a method for reverse engineering the workings of AI models.

Google’s AI feature bumped my article down on the results page, but the new AI Overview at the top still referenced it. What gives?

LibreCommons is a public library of open-access texts and Open Educational Resources. Find texts, explore reusable assessment modules, and collaborate with contributors.

From hundreds of internal projects to tinkering with the AI technology, Arizona State’s chief information officer reveals new details.

One university grapples with how to teach using large-language models.

In our essay, we discuss equity implications surrounding the usage of generative artificial intelligence (AI) in higher education. Specifically, we explore how the use of such technologies by students in higher education such as, but not limited to, multi-language learners, students from marginalized linguistic communities, students with disabilities, and low-income students has the potential to facilitate transformative learning. We describe how such tools, when accessible to learners, can help address barriers that prevent students from fully engaging in their learning. Additionally, we explain how the usage of generative AI has the potential to alter the lens through which students view their learning, countering assumptions and broadening what can be considered an “appropriate” use of assistive technologies to support learning for diverse students. We also address various limitations of generative AI with regards to equity such as the requirement to pay to access some of the applications, as well as linguistic and other biases within the outputs produced, reflective of the data used to train the tools. Throughout this piece, we share insights from a study of undergraduate students’ perspectives and usage of generative AI and potential future directions for the technologies. This essay aims to increase awareness of the opportunities and challenges around who benefits and who is excluded when generative AI is used within colleges and universities.

Algorithms and humans both contribute to bias in AI, but AI may also hold the power to correct or reverse inequities among humans.

In this informative guide we discuss how a student can use AI in an ethical, fair, and lawful manner to assist with learning and studying.