AI is Here.
Now What?
Ashland University
August 21, 2025
Embedded Files
Handouts
Handouts
Facilitator: Lew Ludwig
Facilitator: Lew Ludwig
Dr. Ludwig is a Professor of Mathematics where he directed the Center for Learning and Teaching for five years. 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. He was a project leader on the MAA Instructional Practice Guide.
Padlets
Padlets
Pieces referenced in workshop
AI Assessment Scales
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
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
Unmasking AI tells the story of Joy Buolamwini’s journey from student researcher to founder of the Algorithmic Justice League, uncovering how racial and gender bias is built into AI systems—and what we must do to fight back.
November, 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 and such
Blogs to follow
Blogs to follow
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.
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.
Leon Furze
Leon Furze
Leon Furze helps educators understand and respond to generative AI. A former teacher and current PhD researcher, he writes and speaks widely about the future of writing, literacy, and education in an AI-powered world.
Pieces about Writing
Pieces about Writing
Tons of Resources
Tons of Resources
Academic Integrity
Academic Integrity
The Opposite of Cheating, by Tricia Bertram Gallant and David A. Rettinger, offers a timely, research-backed vision for academic integrity in the GenAI era—where learning, not policing, is the focus. With practical strategies for redesigning teaching and assessment, it helps educators foster honest, student-centered learning environments.
A Possible Way Forward: Course Redesign and Alternative Grading.
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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