Note: This is the first post in an experiment at shared blogging by Punya Mishra,聽Nicole Oster聽and myself. Over the past months we have found ourselves engaged in some fascinating conversations around genAI, education, bias and more. This shared blogging experiment is an attempt to take some of these conversations and move them into this sharable […]
An important feature of a learning machine is that its teacher will often be very largely ignorant of quite what is going on inside. A. M. Turing, Computing Machinery and Intelligence, 1950 Last week at the SITE conference, I talked with Katrina Tour from Monash University about her work with refugees in Australia. She is […]
I thoroughly enjoyed attending the 2024 Society for Information Technology and Teacher Education conference in Las Vegas, Nevada last week. Great people, great ideas. New and old friends. A quick recap: Friends! The best part of this conference was meeting up with some great friends from Arizona State, New Mexico State, and Monash Universities. (including […]
Lately I’ve felt like I’m living in parallel universes. On one hand, I am trying to get the word out of the bias in GenAI, the problems that can cause, and the caution we should be using when these technologies are applied in educational contexts. For example, ChatGPT 3.5 provides lower average writing scores when […]
I have the honor of sharing some ideas about teaching, learning, and AI with New Mexican Educators Rising members at the 2024 Conference. Here are some concepts and resources that might be useful. Ideas for using LLMs in Learning Ideas for using LLMS in Teaching Additional Resources Videos Santa Fe Institute: The Future of Artificial […]
Access resources here! Bad Audio of All Presentations: Bias in LLMs: It’s Not What You Think Full Paper: Warr, Melissa and Oster, Nicole Jakubczyk and Isaac, Roger, Implicit Bias in Large Language Models: Experimental Proof and Implications for Education (November 6, 2023). Available at SSRN: https://ssrn.com/abstract=4625078 or http://dx.doi.org/10.2139/ssrn.4625078 Beyond Cheating: Using LLMs in Teaching and Learning Higher Ed […]
Image (including embedded bias) created by ChatGPT 4 and Dall-E 3. Last week I wrote a post about the bias in ChatGPT–specifically how it adjusts writing scores in response to student descriptions. I illustrated how ChatGPT 3.5 scored a writing passage higher when it was told the imaginary writer preferred classical music, and lower for […]
Sometimes my attempts to understand ChatGPT go down strange paths. While talking with a friend about bias in AI yesterday (yes, this occurs frequently in my life), college rivalries came up. And what could be cooler than having ChatGPT confirm my firm belief that Brigham Young University is superior to the University of Utah? In […]
Note: Cover image (including embedded racial bias) from Dall-E 3 via ChatGPT4.0 I’ve been doing some random tests with ChatGPT and other large-language models (LLMs) (some perhaps a bit more meaningful than last week’s Benford’s Law…). Today’s exploration: If I tell ChatGPT that something is written by a student who likes rap music–vs classical music–will […]