A leading computer scientist says it’s “educability,” not intelligence, that matters most.

Every time we talk about AI, we get one big piece of feedback that I really want to dive into: how the lightning-fast explosion of AI tools affects the climate. AI takes a lot of energy, and there’s a huge unanswered question as to whether using all that juice for AI is actually worth it, both practically and morally. It’s messy and complicated and there are a bunch of apparent contradictions along the way — so it’s perfect for Decoder. Verge senior science reporter Justine Calma joins me to see if we can untangle this knot. Links: This startup wants to capture carbon and help data centers cool down | The Verge Google’s carbon footprint balloons in its Gemini AI era | The Verge Taking a closer look at AI’s supposed energy apocalypse | Ars Technica AI is exhausting the power grid. Tech firms are seeking a miracle | WaPo AI Is already wreaking havoc on global power systems | Bloomberg What do Google’s AI answers cost the environment? | Scientific American AI is an energy hog | MIT Tech Review Microsoft’s AI…

The Ethical Challenges in AI worksheet will help you work through ethical challenges using a design process.

This post is the third in the Beyond ChatGPT series about generative AI’s impact on learning. In the previous posts, I discussed how generative AI has moved beyond text generation and is starting to impact critical skills like reading and note-taking. In this post, I’ll cover how the technology is marketed to students and educators to automate feedback. The goal of this series is to explore AI beyond ChatGPT and consider how this emerging technology is transforming not simply writing, but many of the skills we associate with learning. Educators must shift our discourse away from ChatGPT’s disruption of assessments and begin to grapple with what generative AI means for teaching and learning.

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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...

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&#…

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

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.

Tim Fawns, Monash University

Updates for old friends, and for new followers some links back to where we've been

Kathryn Conrad[Critical AI 2.1 is a special issue, co-edited by Lauren M.E. Goodlad and Matthew Stone, collecting interdisciplinary essays and think pieces on a wide range of topics involvin

GenAI & Ethics: Investigating ChatGPT, Gemini, & Copilot Torrey Trust Professor of Learning Technology University of Massachusetts Amherst torrey@umass.edu | www.torreytrust.com

It is worthwhile spending some time on social media to see the relentless bombardment faced by students from influencers peddling AI tools that straddle the line between aiding study and blatantly enabling cheating. These influencers flourish is contradictions and promise the moon: complete your homework in five minutes flat, forget about ever attending another lecture, let AI take up the pen for you. In each pitch, the essence of learning is overshadowed by a pervasive call to save time. Welcome to the dizzying world of AI influencer culture, where the pursuit of profit drives companies to use influencers as direct conduits to push their products onto students.

Following my keynote to the Association for Learning Technologies (ALT) winter summit on AI and ethics

Since the release of ChatGPT, huge amounts of attention and funding have been directed toward chatbots. These A.I. systems are trained on copious amounts of human-generated data and designed to predict the next word in a given sentence. They are hilarious and eerie and at times dangerous. But what if, instead of building A.I. systems that mimic humans, we built those systems to solve some of the most vexing problems facing humanity? In 2020, Google DeepMind unveiled AlphaFold, an A.I. system that uses deep learning to solve one of the most important challenges in all of biology: the so-called protein-folding problem. The ability to predict the shape of proteins is essential for addressing numerous scientific challenges, from vaccine and drug development to curing genetic diseases. But in the 50-plus years since the protein-folding problem had been discovered, scientists had made frustratingly little progress. Enter AlphaFold. By 2022, the system had identified 200 million protein shapes, nearly all the…

World, meet Alex, Bill, and Mophat, three workers whose labor was essential to filtering violence and abuse out of ChatGPT.For the first time they’re ready to tell you who they are—and how the work unraveled their lives and their families.https://t.co/QXqCRAcOZX pic.twitter.com/8PjWjihMoD— Karen Hao 郝珂灵 (@_KarenHao) July 11, 2023

To unpack this, we should first maybe unpack crticial, AI, and literacy. By critical, I mean this in multiple senses of the word. One is critical as in critical thinking, as in skepticism and quest…