Navigating AI in Education:
Making Informed Choices at UCSD
UC San Diego
January 23, 2025
Embedded Files
Facilitator
Facilitator
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
Are you overwhelmed by the buzz surrounding generative AI? Unsure how it fits into your teaching or research? This webinar will explore ways to boost productivity and support teaching practices with AI. You'll learn about responsible AI use and engage in meaningful discussions on how students can ethically leverage this technology. No prior experience with AI is necessary. By the end of this session, you will possess the knowledge and tools needed to make an informed decision about whether or not to integrate AI into your professional activities. Join us to examine AI's role in education and uncover its potential impact on you and your students.
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.
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.
Books on AI and Teaching
Offers educators a comprehensive and practical roadmap to effectively integrate AI into their teaching practices, addressing both the opportunities and challenges posed by AI, and equipping them with the tools to enhance learning, maintain academic integrity, and adapt to the rapidly evolving educational landscape.
April, 2024
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
Authored by Levy and Pérez Albertos, 'Teaching Effectively with ChatGPT' explores concrete examples of AI integration in education, offering practical strategies and insights to enhance teaching and prepare students for AI utilization.
July, 2024
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 with and for your students
Ryan Watkins provides an adaptive survey you can use with students to determine what AI use is permissible or not in your classes.
July 27, 2023.
Editable surveys for you and your students
Editable surveys for you and your students
Its important to talk to your students about appropriate AI usage. Below are two editable Google Form surveys you can use with your students to begin these conversations. You will need to be logged into a Google account. Don't like these examples, use AI to help generate new ones. Here is a prompt to help get you started.
Calculus: You can make an editable copy of this Google form to create a progressive scale of student AI usage on a related rates assignment. Here is a list of prompts for each scale level students can use.
Intro to proofs: You can make an editable copy of this Google form to create a progressive scale of student AI usage on an equivalence relation assignment. Here is a list of prompts for each scale level students can use.
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.
Phillipa Hardman
Phillipa Hardman
With a background as an affiliated scholar at Cambridge University and former Chief Learning Officer, this page offers a deep dive into integrating AI with educational practices.
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
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.
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
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 five part series from 2017 explaining neural networks, how they are trained and the concept of backpropagation. Requires a basic understadning of linear algerbra and mulit-variable calculus.
3Blue1Brown October, 2017
A Possible Way Forward: Alternative Grading and AI
In the age of generative AI, our educational practices are undergoing significant transformations. This technology compels us to reassess not just what and why we teach but also how we evaluate student learning. I believe one approach will be alternative grading methods, such as standards-based grading or contract grading, to better align with the evolving educational landscape. These approaches encourage a deeper, more personalized engagement with the material, creating a learning environment where students are motivated by curiosity and understanding rather than pursuing traditional grades.
This sample course created by Rachel Weir demonstrates how to create an alternate grading system in Canvas, aligning with the grading checklist provided in the syllabus. It also allows students to easily track their progress using the Learning Mastery gradebook.
Rachel Weir of Allegheny College has curated a comprehensive repository of resources for alternative grading in various math courses, ranging from developmental math to advanced analysis, and covering most levels in between.
edited by Susan D. Blum (2020) - This collection of essays by various educators discusses the drawbacks of traditional grading and provides practical advice on implementing ungrading practice.
by David Clark and Robert Talbert (2023) - This book critiques traditional grading systems and explores alternative grading methods like specifications grading and ungrading. It includes case studies and a workbook for designing alternative grading systems
by Linda B. Nilson (2014) - This book introduces specifications grading, a system that focuses on meeting specific criteria rather than accumulating points. It aims to restore rigor and motivate students while saving faculty time
Page updated
Report abuse