Beyond the Hype: Realities of Generative AI in Math Education
Columbus State Community College
March 01, 2025
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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 GLCA. In 2021, he was honored with 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.
Upcoming opportunities
MAA OPEN Math Workshop: May 12-16
MAA OPEN Math Workshop: May 12-16
Who’s Afraid of Generative AI: Promises and Challenges of AI for the Mathematics Classroom
Who’s Afraid of Generative AI: Promises and Challenges of AI for the Mathematics Classroom
with Feryâl Alayont, Gizem Karaali, Lew Ludwig, & Nick Shay
with Feryâl Alayont, Gizem Karaali, Lew Ludwig, & Nick Shay
Artificial intelligence, particularly generative AI like ChatGPT, is on everyone’s mind right now. There is a robust and ongoing discussion of its risks and benefits, its potential impacts on higher education, the philosophical and ethical questions it engenders, and the ways in which faculty and students might use it effectively. This workshop will support the participants as they dive into these conversations as informed contributors. After a brief but informative exploration of the mathematics that makes these new technologies work, we will engage with the pedagogical challenges and promises they bring to the foreground. Participants will have the opportunity to work hands-on with a selection of AI platforms, with particular emphasis on the most widely used large learning model, ChatGPT. They will develop assignments or lessons that encourage students to make use of these technologies in a competent and yet critical manner.
OHIO MAA Online Workshop: May 20-22
OHIO MAA Online Workshop: May 20-22
AI Essentials: Getting Up to Speed for Informed Decisions
AI Essentials: Getting Up to Speed for Informed Decisions
with Lew Ludwig
with Lew Ludwig
Overview: Are you hesitant about integrating AI into your courses? Uncertain how it can be utilized effectively in academia? You're not alone. Many educators feel overwhelmed by the rapid advancements in AI technology and its implications for teaching and research.
Workshop Details: Join us for a comprehensive three-day workshop, two hours each day, designed to demystify AI and empower you with the knowledge to make informed decisions about its use. We'll start with an introduction to Large Language Models, explaining how they work and the ethical considerations they entail.
Hands-On Experience: The workshop will quickly pivot to hands-on sessions with these AI models. You'll gain firsthand experience to understand their capabilities and limitations. This practical exposure is crucial for evaluating how AI can enhance your professional tasks—from developing course materials and improving annual reports to assisting in research tasks like literature reviews and grant proposal assessments.
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.
The "Cheat-Proof" Calculus Test
The article presents a comprehensive calculus exam question covering various topics like limits, derivatives, integrals, and theorems through a graphical approach, highlighting its effectiveness in assessing students' problem-solving abilities and deepening their understanding of calculus concepts.[1]
September, 2019
An innovative "take-home quiz" assessment approach for calculus courses where students create their own questions, graphs, and solutions over a week-long period, promoting higher-order thinking skills, retention through interleaving of concepts, and a sense of ownership while mitigating cheating concerns compared to traditional timed tests.
May, 2022
With the advent of generarive AI, explore one student's journey of attempting a cyborg approach to the "cheat-proof" calculus test.
May, 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
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 AI is affecting our students and colleagues
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.
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.
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
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.
Generative AI and Your Research
GenAI tools have transformed the landscape of research, providing new
methods for data collection, analysis, and interpretation. Here is a list of
cutting-edge GenAI applications designed to enhance efficiency and
creativity in research endeavors.
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
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.
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