Navigating the AI Storm: Strategies for Educators
Chicago Symposium
April 18, 2025
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Facilitators
Facilitators
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 GLCA. In 2021, he earned the POD Innovation Award for a pioneering faculty development program. His work includes serving as a project leader on the MAA Instructional Practice Guide and presenting innovative teaching strategies at various national conferences.
Recent thoughts on AI
Marc Watkins’ article, "Adopt or Resist? Beyond the AI Culture Wars," explores how educators can navigate the polarizing debate surrounding generative AI in academia. Watkins argues against the binary of uncritical adoption versus outright resistance, urging educators instead to thoughtfully engage with AI. He emphasizes developing sustainable AI literacy by critically evaluating the technology's ethical implications and practical applications across disciplines. The piece calls for nuanced discussions about AI’s inevitable role in education, highlighting the need for informed, balanced policies and classroom practices.
How AI is affecting our students and colleagues
My MAA Column on AI
by Lew Ludwig
My aim in authoring this column is dual-purpose. Firstly, I want to engage my students with generative AI, exploring its applications and implications in our math class and the broader world. Secondly, in alignment with the MAA's core values of community and inclusivity, I invite you to join me on this journey. This is an opportunity for you to pose questions, exchange experiences, or propose topics for upcoming posts. By collaborating, we can collectively harness this emerging technology to enhance learning for ourselves and our students.
How LLMs work
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
Some Technical Things to Consider with Generative AI
The current issue of the Bulletin explores the impact of artificial intelligence on mathematical research, asking whether AI will revolutionize the field, change research methodologies, and address the concerns and potential of using machine learning in mathematical proofs and beyond.
April, 2024
Harvard Business School
Harvard Business School
The article explores how AI like GPT-4 can boost or trip up the work, depending on the task at hand. It's a deep dive into when AI is a help and when it's not!
September, 2023
MIT Technology Review
MIT Technology Review
The article explores the remarkable and puzzling capabilities of large language models, which can perform impressive feats of generalization and reasoning that defy traditional statistical understanding, leaving AI researchers scratching their heads about the underlying mechanisms that drive these models' success.
March, 2024
How I write with AI
Top Layer (Human): You write your thoughts and ideas (quickly)
Middle Layer (AI): Use “copy edit” to improve your writing (one paragraph at a time, 4o vs. 4.0)
Bottom Layer (Human): You review, refine, and finalize the AI-generated output - be the human in the loop
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 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
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.
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.
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
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.
A collection of prompts by the Mollicks for instructors and students to help improve results from generative AI.
Working document
A Possible Way Forward: Alternative Grading and AI
In this era of generative AI, the way we teach—and assess—is shifting. It’s not just about what or why we teach anymore, but how we understand and support student learning. One promising path forward is rethinking our grading practices. Alternatives like standards-based grading or contract grading may offer better alignment with today’s educational realities. These approaches invite students to engage more personally and meaningfully with their learning, guided by curiosity and understanding rather than the chase for points.
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
AI and the Environment
DISCLAIMER: The environmental impact of large language models like ChatGPT is real, but not easy to pin down. Training these systems uses a significant amount of water for cooling and each prompt consumes electricity. That said, getting accurate numbers is tough—many tech companies don’t share detailed energy data.
The articles below do their best to shed light on these concerns. Their claims may vary, not because of bad intentions, but because the data is hard to come by. Still, they offer a valuable entry point into an important conversation. I hope they spark curiosity and lead to deeper exploration of this complex issue.
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'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.
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