Generative AI Explained:
Benefits, Challenges, and Ethical Considerations
Granville Library
February 06, 2025
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Lew Ludwig
Lew Ludwig
Dr. Ludwig is a Professor of Mathematics and the director of the Center for Learning and Teaching. at Denison University. He is a nationally recognized speaker on generative AI, leading numerous webinars and workshops for the MAA, POD, and the GLCA. His work includes serving as a project leader on the MAA Instructional Practice Guide and presenting innovative teaching strategies at various national conferences.
Abstract
Abstract
Join us for an insightful exploration into generative artificial intelligence (AI), presented by Lew Ludwig, Professor of Mathematics and Director of Denison’s Center for Teaching and Learning. This session will provide a clear overview of how generative AI works and its transformative potential in education and beyond. We'll also discuss the crucial ethical considerations, including privacy and bias, highlighting AI's impact on society.
Are you new to generative AI?
Here are some useful resources to help you get started.
AI Pedagogy Project
AI Pedagogy Project
metaLAB (at) Harvard
metaLAB (at) Harvard
Focused on the essentials and written to be accessible to a newcomer, this interactive guide will give you the background you need to feel more confident with engaging in conversations about AI in your classroom.
Watch this video for an easy-to-understand guide on common AI terms, an overview of how the GPT model functions, a comparison between ChatGPT 3.5 and 4, and an insight into some of its limitations, presented by Lew Ludwig, August 2023.
Books on Understanding AI
Still the OG of AI books, An essential guide for educators on using AI as a transformative co-worker, co-teacher, and coach.
April, 2024
Authored by Arvind Narayanan and Sayash Kapoor, AI Snake Oil critically assesses AI's promises and pitfalls. It acknowledges the potential of generative AI, such as ChatGPT, while debunking myths and exposing misleading claims about the capabilities of other types of AI. This book provides essential insights into the use and misuse of AI across various sectors, helping readers navigate the benefits and challenges of AI. Includes accompanying website with tons of resources.
Septemember, 2024
AI and the Environment
DISCLAIMER: The environmental impact of large language models like ChatGPT is challenging to quantify accurately. These systems require substantial water for cooling during their training processes, and each model prompt consumes electricity. However, quantifying this usage is difficult as large tech companies often do not disclose detailed energy consumption data. The articles presented below attempt to address these issues. Please be aware that the claims and their validity may vary, decreasing from left to right. This variation is not due to any intentional misinformation but stems from the difficulty in obtaining precise data. The posts aim to do their best under these constraints. I hope they serve as a starting point for further discussion and exploration of this critical topic.
AI's Environmental Impact
AI's Environmental Impact
The podcast segment addresses the environmental impact of AI, focusing on its significant energy and water usage, tech companies' responses through renewable and nuclear energy investments, and the need for more transparency and efficiency.
AI Is Unavoidable, Not Inevitable
AI Is Unavoidable, Not Inevitable
The blog post explores the complexities of integrating generative AI into education, emphasizing the need for a comprehensive understanding and cautious engagement rather than simple resistance or adoption.
AI's impact on energy and water usage
AI's impact on energy and water usage
The webpage post is a work in progress and tries to give an overview of generative AI's energy and water usage, along with a call for more transparency from AI companies.
Using ChatGPT is not bad for the environment
Using ChatGPT is not bad for the environment
This blog post argues that using ChatGPT and other large language models (LLMs) has minimal environmental impact compared to other daily activities, and emphasizes focusing on systemic changes rather than individual actions for effective climate action.
Maha Bali
Maha Bali
This lesson plan uses the cake-making analogy to teach the ethical use of generative AI. It compares various methods of obtaining a cake—baking from scratch, using a box mix, or buying from a bakery or supermarket—with different degrees of AI reliance. Participants, including students and teachers, critically assess the implications of each approach, focusing on quality, time, cost, and personal effort. The discussion extends to generative AI's impact on learning outcomes, work quality, the learning process, and ethical considerations. The lesson concludes with a collaborative session to develop guidelines for responsible AI use across various contexts. This approach has effectively fostered meaningful discussions among students and educators about AI’s role in education.
Some useful blogs
These are some blogs I follow on Substack.
Ethan Mollick
Ethan Mollick
A professor at the Wharton School of the University of Pennsylvania, who studies entrepreneurship & innovation and AI. He is trying to understand what our new AI-haunted era means for work and education.
Marc Watkins
Marc Watkins
Assistant Director of Academic Innovation, Director of the Mississippi AI Institute, Lecturer of Writing and Rhetoric at the University of Mississippi. He trains faculty in AI literacy.
Lew Ludwig
Lew Ludwig
Lew Ludwig examines generative artificial intelligence in university mathematics and offers support to faculty grappling with its impacts in and outside the classroom.
Some resources on crafting prompts
Prompts are the questions or instructions we submit to generative AI, more complex than queries to a search engine. Effective prompting is a skill that takes time to craft and develop. These resources offer suggestions, many of which you can customize and adapt for your specific needs.
Mollick advocates for "good-enough prompting" as a user-friendly method for interacting with AI, treating it as a capable but peculiar coworker. Mollick emphasizes the importance of experimenting with AI to understand its capabilities and limitations, aiming to make it accessible and effective for diverse applications.
Authored by Kevin Yee, Erin Main, Laurie Uttich, and Liz Giltner from the University of Central Florida, 50+ AI Hacks for Educators (.PDF), explores the urgent need for educators to understand and integrate Generative AI into their teaching, offering practical insights and strategies to enhance engagement and ensure students are well-equipped to use AI effectively in their careers.
July, 2024
A collection of prompts by the Mollicks for instructors and students to help improve results from generative AI.
Working document
The mathematics behind LLMs
A MUST FOR ALL MATH STUDENTS!
This is a continuation of the Neural Networks series, which introduces the mathematics and deep learning involved in transformers and their prerequisites.
3Blue1Brown April, 2024
A visual walk-through of how this type of artificial intelligence work by Seán Clarke, Dan Milmo, and Garry Blight, The Guardian, November 1, 2023
Video and music generation used
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