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Explore the World of AI for Academic Settings

Artificial Intelligence is reshaping education, and SDSU's Academic Applications of Artificial Intelligence (AAAI) Micro-Credential helps ensure you stay ahead.

This fully online, self-paced program is designed to equip learners with the skills to use generative AI efficiently, ethically, and effectively. Offered free of charge through SDSU Global Campus, the program consists of five modules, blending theoretical insights with practical applications, and takes approximately 2-4 hours to complete.

While the course content is free, learners who wish to can earn a verified digital badge for a $25 fee.

What You'll Cover

Overview: How Does AI Work?

Ethics & Responsible Use

What Can AI Do?

Finding Apps

Prompt Engineering Activities

Copyright © 2025 SDSU Global Campus - All Rights Reserved

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Academic Applications of Artificial Intelligence Micro-Credential - SDSU Global Campus

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SDSU's Academic Applications of AI (AAAI) Micro-Credential prepares you to apply generative AI technology efficiently, effectively, and ethically to level up learning - which is at the heart of SDSU's mission.

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AI Human Rights Impact Assessment Tools | Conflict Management & Human Rights Office | University of Waterloo

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Human Rights: An Introduction Part 2 The Ontario Human Rights Code Waterloo policy

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Inside Higher Ed | Higher Education News, Events and Jobs

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I want artificial intelligence to plagiarize my work.

Let me clarify: I don't mean I want a student to copy and paste my latest law review article and pass it off as their own. Academic integrity matters. But when it comes to the burgeoning capabilities of generative AI, our fixation on traditional notions of authorship and plagiarism is becoming a significant barrier to realizing perhaps the greatest proliferation of knowledge generation and sharing this world has ever seen. In my research on how legal frameworks can accelerate the development and adoption of AI, I see this firsthand. We stand on the precipice of a revolution in thought and expression, yet we hesitate, clutching tightly to outdated norms centered on individual credit rather than collective progress.

AI's trajectory is already transforming how we interact with information. It can distill labyrinthine scientific papers into accessible summaries, translate complex policy debates across languages and cultural contexts, and empower individuals previously sidelined in public discourse to craft compelling arguments. Imagine community groups, such as Parent Teacher Associations, using AI to analyze proposed legislation and draft persuasive appeals to their representatives, tailored precisely to resonate with local concerns. Picture scientists in underresourced labs partnering with AI to rapidly survey existing literature, identify novel research avenues and author grant proposals. Think of citizens everywhere using AI tutors to grasp intricate subjects like the economic literature on tariffs, leveling the playing field for informed participation in democratic life.

This potential is particularly profound for scholars like me working on emerging issues of broad popular concern, such as the regulation of AI. Our core professional mandate is the creation and dissemination of knowledge. We spend years honing expertise, conducting research and crafting arguments, ideally to contribute to the broader understanding of our fields and the world. AI offers unprecedented tools to amplify this mission. It can synthesize vast data sets, identify subtle patterns across disciplines, help structure complex arguments, refine prose for clarity and even suggest novel lines of inquiry based on the existing corpus of human knowledge. It can be the most powerful research assistant, writing partner and knowledge amplifier ever conceived.

So, what holds us back? In academia, a significant part of the answer is ego, intertwined with our deeply ingrained obsession with attribution and the mortal fear of plagiarism. I encountered this resistance directly a few weeks ago at a well-known law school. I was presenting at a faculty workshop, soliciting feedback on a working paper, tentatively titled " Large Language Scholarship," that I co-authored with Alan Rozenshtein. The paper explores how generative AI can genuinely improve the often-torturous process of legal scholarship-streamlining literature reviews, assisting with complex doctrinal synthesis, even helping overcome writer's block to get crucial ideas onto the page faster and more clearly.

I laid out the potential benefits to my fellow academics: accelerating research, potentially democratizing scholarly contribution, allowing us to focus on higher-level analysis rather than rote summarization. The room was initially engaged, curious. But then the questions began. And almost immediately, the conversation snagged, then ground to a halt, fixated on one point: attribution.

The immense potential for enhanced knowledge creation, for tackling complex legal problems more efficiently and clearly, for making our often-esoteric work perhaps slightly more relevant-all of it evaporated against the specter of improper citation. We stumbled, repeatedly, against this brick wall. Despite my attempts to steer the conversation toward the opportunities and the need for new norms, several colleagues simply could not get past the ingrained fear that AI usage inherently equaled plagiarism, or at least created an unacceptable risk of it. The discussion became less about transforming scholarship and more about policing the boundaries that AI inherently challenges.

That experience crystallized the problem for me. We build careers on citation counts, on the meticulous tracking of who originated which precise turn of phrase or specific idea. We debate the ethics of AI "hallucinations" and worry about machines generating text based on our work without proper footnotes. But this fixation, as revealed in that workshop, often overshadows the core purpose of our work.

Let's be honest with ourselves. How many people actually read the average scholarly article from start to finish? A handful? And how many delve into the citations? Even fewer. While citation serves a crucial role, our anxiety often extends beyond practical functions into the realm of intellectual ownership and personal recognition. We worry that if an AI learns from our work, our individual contribution will be diluted.

This is where I say, please plagiarize me. Train your models on my articles, my analyses, my proposals for legal reform around AI-even on the ideas in "Large Language Scholarship." If an AI, having processed my research, helps a start-up navigate regulatory hurdles, assists a policymaker or simply explains a complex legal doctrine to a student, isn't that the ultimate fulfillment of my work's purpose?

My goal as a scholar isn't personal renown. It's about the ideas themselves and their potential impact. It's about contributing to a body of knowledge that can lead to tangible improvements. If AI can act as a vector, carrying those ideas further, faster and to more people than I ever could alone-even if it remixes and integrates them without a footnote explicitly tracing back to "[Your Name], [Year]"-then it is serving the fundamental purpose of scholarship.

This doesn't mean abandoning intellectual honesty. It means recalibrating our perspective. We need to shift the focus from "Who gets the credit?" to "How can this knowledge best be used?" When AI acts as a tool for synthesis and creation, the traditional model of single authorship frays. Insisting on applying old norms to new technology risks stifling the very innovation we claim to champion, just as I saw happen in that workshop.

The potential benefits of AI-powered knowledge generation are too immense to be held hostage by academic vanity or anxieties about attribution. Let the AI learn from us. Let it synthesize, remix and build upon our work. Let it help translate our niche insights into broadly accessible wisdom. Let the ideas flow. My work isn't about me; it's about the knowledge. If AI using my work helps accelerate innovation or empowers someone, then I invite it: plagiarize away. The real reward isn't citation: It's impact.

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I Want AI to Plagiarize My Work (opinion)

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In an AI era, a preoccupation with issues of credit and citation limits the reach of our research, Kevin Frazier writes. I want artificial intelligence to plagiarize my work.

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Distant Writing: Literary Production in the Age of Artificial Intelligence

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Distant Writing: Literary Production in the Age of Artificial Intelligence

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Distant Writing: Literary Production in the Age of Artificial Intelligence
Revised version 5

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This article introduces the concept of “distant writing”, a novel literary practice in which authors act as designers, employing Artificial Intelligen

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Curated by Dr. Heather M. Brown, Instructional Designer – hbrown@tcc.edu Feel free to use, remix, and share this document! This work is licensed under a CC BY NC SA 4.0

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The State of AI in Higher Ed: A Synthesis of Policies, Guidelines, and Emerging Themes

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The State of AI in Higher Ed: A Synthesis of Policies, Guidelines, and Emerging Themes

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AI Tools for Education and Resources for Learning about AI

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AI Tools for Education and Resources for Learning about AI

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Shared by Brandon Lee Henry

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AI Tools for Education and Resources for Learning About AI

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A Big Picture Look at AI Detection Tools

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A Big Picture Look at AI Detection Tools

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A Big Picture Look at AI Detection Tools, with Christopher Ostro – Teaching in Higher Ed

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Christopher Ostro shares a big picture look at AI detection tools on episode 555 of the Teaching in Higher Ed podcast

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Turnitin - Overview of AI Detection Tools

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Turnitin - Overview of AI Detection Tools

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Turnitin - Overview of AI Detection Tools

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Practical Examples & Strategies

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AI and Assessments: Webinar Resources - Alchemy

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The following examples are designed to assist instructors in iterating their own assignments to align with desired levels of student AI use and accountability.

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I used to "teach" in my room at home. The stuffed animals and dolls were my students. My bedroom door had a sign on it that read #208. My "name" was "Miss Monroe," a tribute to the 1970s Charlie's Angels...
I have always wanted to teach.
Fast forward to today. I teach as a university professor at Vanguard University: a small, private, liberal arts university in Orange County, California. I'm also dean of teaching and learning, allowing me to collaborate with other faculty in improving all of our teaching and support efforts that allow students to thrive in their learning. My passion is in continually becoming more effective facilitating learning for my students. I'm also fortunate to get to coach faculty in my dean role and connect with faculty from all over the world through the Teaching in Higher Ed community.

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Teaching in Higher Ed with Bonni Stachowiak

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Teaching in Higher Ed is a faculty development space where we explore the art and science of being more effective at facilitating learning.

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Syllabi Policies for AI Generative Tools

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Syllabi Policies for AI Generative Tools

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EDUCAUSE

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Syllabi Policies for AI Generative Tools

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Google Scholar’s New AI Outline Tool Explained By Its Founder

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Google Scholar’s New AI Outline Tool Explained By Its Founder

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Google Scholar’s New AI Outline Tool Explained By Its Founder

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Google Scholar PDF reader uses Gemini AI to read research papers. The AI model creates direct links to the paper’s citations and a digital outline that summarizes the different sections of the paper.

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It’s been over 12 months since the first blog post about the AI Assessment Scale, and a lot has changed, both with the technology and with our understandings of how it impacts assessments in …

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Explore our market map and database featuring ~60 use cases for Generative AI in educational contexts and a database of over 300 AI tools for the Education Technology community.

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Great Podcast which summarizes AI Research & Latest AI News

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With Dan Bowen, Beth Worrall, and Lee Hickin from Microsoft Australia

It's a fortnightly chat about Artificial Intelligence in Education - what it is, how it works, and the different ways it is being used. It's not too serious, or too technical, and is intended to be a good conversation of background information.

Of course, as well as getting it here on the website, you can also just subscribe to your normal podcast service:

"This podcast is produced by Microsoft Australia & New Zealand employees, Lee Hickin, Dan Bowen, and Beth Worrall. The views and opinions expressed on this podcast are our own."

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Lee Hickin, Beth Worrall, and Dan Bowen work in the Microsoft Australia and New Zealand team, they talk about Artificial Intelligence in Education - what it is, how it works, and the different ways it is being used. It's not too serious, or too technical, and is intended to be a good conversation of background information.

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Podcast that has the latest ed tech news, including episodes on AI.

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Ever feel like you’re the only one going through a certain experience in your career? You’re not alone. Join me, Fonz Mendoza, as I interview educators a…

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EdTech and AI with Jared Cooney Horvath (Recorded Live from ResearchED)

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EdTech and AI with Jared Cooney Horvath (Recorded Live from ResearchED)

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Interesting take on not using AI. Just to look at the other side of the coin. Many good points.

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Episode 86: EdTech and AI with Jared Cooney Horvath (Recorded Live from ResearchED) — The Learning Scientists

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This episode was funded by listeners like you. For more details on how to help support our podcast and gain access to exclusive content, please see our Patreon page . Show Notes: In episode 86, Cindy talks with Dr. Jared Cooney Horvath , neuroscientist, educator, and author of Stop Talk

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(created by CDiMicelli) SHARED -- Spring 2024 -- PA Academic Honesty & Integrity with AI

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Check out this US Letter (Landscape) designed by Christina DiMicelli.

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4 Tips for Designing AI-Resistant Assessments

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4 Tips for Designing AI-Resistant Assessments

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4 Tips for Designing AI-Resistant Assessments

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As AI continues to evolve, instructors must modify their approach by designing meaningful, rigorous assessments.

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Avoid detection on Turnitin amongst other AI detecting tools by processing your AI-generated content through Twixify.

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Michael Matsuda, Superintendent of SchoolsAnaheim Union High School District

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Generative AI for Educators - Grow with Google

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Learn AI skills with Google Generative AI for Educators. Save time, personalize instruction, and enhance lessons, no previous experience required.

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How to Use AI to Do Stuff: An Opinionated Guide

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How to Use AI to Do Stuff: An Opinionated Guide

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How to Use AI to Do Stuff: An Opinionated Guide

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Covering the state of play as of Summer, 2023

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AI Tool Demo: Creative Uses for ChatGPT in History and ELA

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AI Tool Demo: Creative Uses for ChatGPT in History and ELA

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AI Tool Demo: Creative Uses for ChatGPT in History and ELA

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Assistant editor Daniel Leonard shares a few interesting applications of ChatGPT that teachers have adopted to drive critical thinking and deeper analysis.

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This semester, Eugenia Novokshanova and I have started requiring our students to include links to their AI chat sessions when they submit certain exercises where we require the use of AI. For example, we have several "Paragraph Enhancement" exercises, where our students use a prompt that we have created, then upload a paragraph they have written, and the AI works like a personal tutor, asking them to rewrite and revise the paragraph sentence by sentence. When they are done, we require them to submit their original paragraph, the final paragraph, a link to their chat session, and a metacognitive reflection on the process. Last semester we didn't require the chat link (we didn't even think about it until Mike Kentz suggested it to me last summer), and we have found that link to be indispensable to our teaching! It is an essential aspect of the formative assessment of our writing students. When we use the chat link, we have a portal into the process of our students' writing:

We can see how they got from the original paragraph to the final paragraph. Some students struggle more than others, and they struggle with different aspects of the exercise. Looking at the chat link helps us understand the challenges that each individual student faces in writing that paragraph.

We can see when students misunderstand our directions. This is one way formative assessment helps us be better teachers, and it has led us to revise how we instruct our students. For example, in the exercise, when the AI asked students, "Please revise your topic sentence to provide more information about the source," some of our students repasted the entire paragraph with the revised sentence instead of simply writing the sentence. We made our directions much more explicit about how they were supposed to interact with the AI tutor.

We can see when students just don't want to work with the AI. When a student receives direction from the AI tutor, sometimes, instead of doing the work, they will say "Move on," or "I don't want to do that." Other times they will turn the work back on the AI by saying something like, "Write that for me." Often those are the same students who will report that they didn't get any real help from the AI. This helps us understand where those comments come from and helps us address any frustration students may be facing.

We can see if a student is pushing beyond our prompt to show especially ethical, responsible, or efficient use of AI. For example, the AI chatbot might start writing the paragraph for the student, and the student says, "You aren't supposed to write this. You are supposed to just help me-like a tutor." Or the student goes further than the AI chatbot to say something like, "I want to look harder at that quotation. Is there something I'm missing?" When this happens, we want to be able to reinforce the student in their use of AI and praise them for taking control.

The use of a chat link has even affected the way we approach teaching with AI in our classes, as it has been essential to our understanding of how our students understand and use AI in our writing classroom. We have become more aware of our responsibility to implicitly teach AI skills in our class. Before we were inferring those skills-teaching something with AI, but not directly teaching the student prompting or AI use. However, our understanding of the student interaction with AI, due to the use of chat links, has opened our eyes to the need to make our students aware of things that we didn't really notice before-like how our students don't have a clear understanding of how AI is different than Googling something, how you must take control of the chat session, and how they can direct AI outcomes through iteration.

As we design our courses for next semester, these are lessons we will be integrating into our coursework. More and more, every day, we are made aware of the fact that AI is not something you just "do." AI is a skill that needs to be carefully and thoughtfully taught throughout the semester in order for our students to begin to master it.

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Requiring a Chat Link has Changed Our Understanding of Student AI Use

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A free introductory series on using generative AI

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My Students Are Submitting AI Papers. Here’s What I Do

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My Students Are Submitting AI Papers. Here’s What I Do

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(Image credit: Getty Images)

Last summer it finally happened to me: While reading a student essay in an introductory online college course at one of the universities at which I teach, I began to suspect not only that this student hadn't written it but that no human mind had. In other words, it was the work of AI (artificial intelligence).

Like many educators across the globe, I was thrust into a brave new world of modern teaching that I had not been trained for and to which an appropriate response was unclear.

While I was aware of many effective AI detection tools , I had also heard horror stories of false positives from these same tools. My institution had recently announced a policy forbidding instructors from accusing students of using AI in their work. This gave me some institutional framework in which to operate, but I also did not want to reward a student with a positive grade for using a machine to do their writing for them.

I've since encountered similar situations many times. My AI radar has improved over time, and my understanding of the thorny questions around AI submissions has evolved.

Here's how I handle these cases. Keep in mind that this is an ever-evolving process and appropriate responses are often institution, class, and student-specific.

1. Detecting AI: I Trust My Instincts to A Point

The first step in this process for me is to use common sense while reading student work. AI tools have gotten good at writing in one sense but tend to be bland - the written equivalent of white noise or smooth jazz. In addition, the student I first suspected submitted work that was markedly different from any they had submitted previously that term.

Since then, I've noticed a trend of my online students never submitting their own writing, so this change in tone and style is often unavailable as a comparison point.

On the flip side, I've become aware of a number of AI "tells" and have developed methods for detecting AI writing without technology. I've started using these more and more instead of AI writing detectors, which I've increasingly found to have too many false negatives, as there are now AI tools designed to read AI-written content and tweak it to throw off AI detectors.

But as good as I think my spidey sense for detecting AI writing is, it would definitely not stand up in a court of law, so I do not make AI accusations based on this. Instead, I use these suspicions as a conversation starter.

2. I Confer With Colleagues

I have written about a student who was falsely accused of using AI to generate papers, so I know I need to proceed cautiously to be fair to my students.

I typically reach out to a colleague in my department who oversees my work to share my concerns and any questionable work. If it still looks suspicious, we then bring in a superior for a third opinion and to discuss a course of action. This process helps protect the student by ensuring that I'm not misreading the situation somehow.

Talking with others also protects me as an untenured educator because it documents that I am acting in accordance with the university's AI's policies. This is especially important because at many institutions these policies are evolving and in some cases haven't been formalized yet.

Conferring with colleagues remains one of the most important steps with each and every AI paper I encounter. It can be time-consuming for me and them, Al but I think it's important, as my sense of what is and isn't AI writing is not perfect and students deserve the benefit of having this process be conducted with some oversight.

3. I Try to Speak With The Student Without Accusing Them of Using AI

My colleagues and I typically decide the best course of action is to try and speak with the student. During any conversation I ask how they conducted their research for the paper without mentioning AI or accusing them of anything.

I usually reach out via email and ask to talk with the student on the phone or via video call. If a student doesn't respond, I email specific questions about the content of the paper, and say that I need these to be answered before I can grade their work.

At the same time, I also send class-wide emails that I have encountered suspected AI-use in the classroom. These announcements remind students that using AI writing for essays is against course and university policies.

Many times, the student will not respond, and I give the paper a zero.

I've encountered several instances of suspected AI use and most play out pretty much the same way. None of my students have been penalized for violating academic standards but none receive credit for work that they did not produce, which is my primary concern.

It's a small, but I think important, victory for human writing.

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My Students Are Submitting AI Papers. Here's What I Do

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Like many educators, I've been thrust into a brave new world of AI and modern teaching that I had not been trained for and to which appropriate responses aren't always clear

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AI Instructional Framework Explorer :: AI Insights For Instructors

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AI Instructional Framework Explorer :: AI Insights For Instructors

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AI Instructional Framework Explorer :: AI Insights For Instructors

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AI Instructional Framework Explorer :: AI Insights For Instructors

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AI Instructional Framework Explorer :: AI Insights For Instructors

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Agreement Overview

By using this AI instructional framework, you agree to abide by the following terms and conditions to ensure ethical and responsible use of generative AI technologies in educational settings.

Terms and Conditions

Use AI tools and resources provided by this framework solely for educational purposes.

Respect the privacy and confidentiality of all users and their data.

Avoid using AI to generate harmful, misleading, or inappropriate content.

Ensure that AI-generated content is used to support and enhance learning, not to replace genuine student work.

Promote transparency in the use of AI, clearly indicating when and how AI tools are being used.

Adhere to all relevant institutional policies and guidelines regarding AI usage.

Continuously educate yourself and others about the ethical implications and potential biases of AI technologies.

Enforcement

Violation of these terms may result in the suspension or termination of access to the AI tool, as well as potential action by the institution or any other party with oversight.

Feedback and Updates

We encourage users to provide feedback on this agreement and any related issues. The agreement may be updated periodically to reflect new insights and developments in AI technology and its use in education.

1. AI Instructor Assistant & AI Class Assistant

Does not store any user-submitted data

Course names and AI-generated outputs are visible only to the user

2. Usage of Perplexity.ai

Perplexity.ai Service Levels

Users may choose to upgrade their Perplexity.ai service level for indepth results and collaboration within a department.

Free Anonymous (Default)

No account required

No conversation history saved

This makes Perplexity a unique system with few barriers of use for users who are new to AI

Getting started with Perplexity

Free Account

User creates an account

User has access to some PRO searches and can customize responses

Retains conversation history

PRO License

Paid upgrade

Access to Claude and OpenAI engines

More in-depth responses

Enterprise PRO

Designed for departmental use

Allows instructors to collaborate on prompts

Enables shared research

More about Enterprise Pro

The use of this tool is provided on an "as-is" basis, without any warranties of any kind, either express or implied, including, but not limited to, implied warranties of merchantability and fitness for a particular purpose. The user acknowledges that no representation or warranty is made regarding the accuracy, reliability, or completeness of the tool. Accessibility compliance, security review, and content accuracy are controlled by external vendors, and no warranties are made regarding these aspects. The user agrees to indemnify, defend, and hold harmless the provider, its affiliates, and their respective officers, directors, employees, and agents from and against any and all claims, liabilities, damages, losses, or expenses, including reasonable attorneys' fees and costs, arising out of or in any way connected with the use of the tool, including any data or content transmitted or received by the user, and any breach of this agreement by the user.
AI Class Assistant & AI Instructor Assistant by Dr. Szymon Machajewski is licensed under Creative Commons Attribution 4.0 International

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Welcome to the AI Instructional Framework Explorer

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By using this AI instructional framework, you agree to abide by the following terms and conditions to ensure ethical and responsible use of generative AI technologies in educational settings. Use AI tools and resources provided by this framework solely for educational purposes. Respect the privacy and confidentiality of all users and their data.

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Is AI Finally a Way to Reduce Higher Ed Costs?

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Is AI Finally a Way to Reduce Higher Ed Costs?

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Inside Higher Ed | Higher Education News, Events and Jobs

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New technology has often changed work. The Industrial Revolution did not eliminate weavers, brewers and farmers, but it did change core aspects of their work while increasing productivity and efficiency. For those who could master the new machines, wages increased, and for those who owned the machines, costs went down and the volume of production went up. More recently, countless innovations-calculators, email, the internet, spellcheckers, spreadsheets, citation software and learning management systems-have all changed how higher education faculty and staff work, but they have done little to increase salaries or lower student costs. Supporting these technologies, of course, added costs which may have cancelled out any savings.

Today, professional jobs that were immune to earlier shifts in physical labor are already seeing disruptions from artificial intelligence (AI). Indeed, one new research study found that AI could already "match or exceed human accuracy in determining legal issues" (at a significantly lower cost), concluding "these results are not just statistics-they signal a seismic shift in legal practice." Similarly, a Google study has found that software bug fixes can now be identified and fixed more quickly using AI.

Jobs consist of groups of tasks. AI can't do everything better, but almost every job has that AI can already do better. The ability to outsource tedious tasks (either to others or machines, like a spellchecker) often makes us happier and more efficient, and it is no surprise the same has already been demonstrated when we use AI well.

Despite the promises and hype of previous technology, television, satellites, the internet, online courses and MOOCs all failed to lower higher education costs. All of them made it easier and faster to distribute content, but none of them helped us scale the feedback and relationships that form the core of higher education. Faculty labor has remained central and impossible to scale.

AI offers different possibilities.
1. AI can relieve faculty from some tedious tasks and do it at scale.
Here are some (perhaps tedious) tasks with (much abbreviated) prompts that might allow AI to give you back some time and even make you happier or better at your job. Recognize that you will get significantly better results with a more advanced (i.e., paid) AI (GPT-4, Claude 3 or Gemini Advanced) and that many of these require an AI connected to the internet (like the free Copilot or Perplexity).

Course Design

Provide me with three sample policies I might include on my syllabus regarding how AI might be used by students in my X course.

How could I make this syllabus/assignment more inclusive?

Content and Pedagogy

Find me five relevant videos appropriate for college students on A that are B minutes in length and give me a 75-word summary for each that includes its content, reliability and source.

Provide five ideas for how to introduce college students at X university to topic Y using examples or analogies they will find relevant.

Grading

Create a rubric in table form to assess the learning in this assignment using these learning outcomes. List criteria in the first column and then provide descriptions in subsequent columns for poor, fair, good and excellent performance.

Provide grades and feedback for these essays. Use my rubric, previously graded essays and samples of my feedback to calibrate your feedback to write and grade in my voice.

Assessment and Accreditation

Evaluate these essays using rubric A and assess what percent of essays meet the B standard.

Write my departmental accreditation report using these formats, guidelines and data.

Research

Create a literature search/bibliography/list of articles on topic A, using methodology B, with sample size C that disputes claim D. (Consensus and Elicit are examples of AI that search only the published articles in the Semantic Scholar database to give you verifiable results.)

Who are the other major figures in this field who might be potential reviewers of this article? What work of theirs should I be sure to cite?

Grant Writing

Read these emails/strategic goals and advise me how to make my funding request more compelling to my provost.

Transform this research brief into a compelling proposal for the X foundation using its format and guidelines.

Job Search

Pretend you are faculty member X on this search committee for a new dean. Read the uploaded position description, my cover letter and CV. How might the committee or X react to my materials? List missing elements and suggest ways for me to improve my application.

Act like professor Y on this search committee. Help me prepare for my interview by using the attached materials and ask me a series of potential questions that will challenge me. Include questions with inaccurate information and require me to correct you with real data.

Student Support

Write a reference letter for student A using these materials. Begin by reading my previous letters and describing the elements of my voice and form.

Act like student X using these details and context. Help me practice for a conversation with X about Z.

Administration

Produce three spreadsheets with three different versions of the department schedule using this data on faculty preferences, room availability, student demand and X.

Write a kind and personal 75-word letter of rejection for authors whose manuscript has been rejected by the Journal of X.

More context, process and iteration will greatly improve the results of these prompts. For example, a larger set of essays with your previous feedback is critical for better results and for training AI (and for greater privacy and security, you can download an opensource AI to run and fine-tune on your laptop). There are a rapidly increasing host of bots that will do specialized tasks and these functions are already creeping into your LMS. But what will you do with the time that you were spending grading or writing reports?
2. AI can improve relationships.
AI presents opportunities not just to reduce costs or improve efficiency. Consider this scenario:

A student has come into your office needing an approval to take an overload. Assuming you remember your password and which system to use, you can probably complete this task, but do you remember the student's hometown? Now imagine, you have an AI assistant that recognizes the student and immediately organizes the relevant information and prompts you to ask about the student's dog. The AI listens to your conversation and prompts you that the student will need Statistics 101 to complete her major or might like the new basketball club. These are among the opportunities that are now ahead of us.

In emerging studies regarding AI as an assistant, it is the least experienced who benefited the most from AI support. That is hardly a surprise, but neither is the complaint that faculty, whose job requirement entails five years of mostly solitary confinement, are not responsive enough, or trained enough, to build the relationships we now know are so vital for student success. As one researcher put it: AI is "like Grammarly for empathy."

AI can now transcribe, organize and fact-check meeting notes while also showing who talks or interrupts the most (a feature now in Teams and Zoom!). An AI companion gets all of your references but can also determine when a student is in distress. This strange and creepy new world is here, but who better than faculty to help us understand and test where it can be useful or harmful? Further, AI is already reducing costs in a plethora of sectors, and faculty need to be on the front lines of discovering if and how AI might help us combat high tuition.

There are many tasks where AI will never be able to replace humans, and we should focus on them. Faculty have rushed to say that human teaching will never be replaced with AI. True-and the need for human faculty will still be the primary driver of costs in higher education. Still, AI may finally be the technology that gives faculty more time and more assistance for the most important educational and relational tasks.

For now, try some of the tasks and prompts above, but when you don't get results you like, don't assume AI is useless. Try to ask a better question with more detail, complexity and specification (and perhaps pay for a significantly better AI).

The abacus, thesaurus and computers have all provided aids to human thinking, but none of them replaced the educational priorities of questioning assumptions and asking better questions. AI will change how humans think and has the potential to strengthen the ways we interact. Like the internet, it will change some of the skills we need, but it won't change the need for thinking with this new technology.

José Antonio Bowen and C. Edward Watson are the authors of Teaching with AI: A Practical Guide to a New Era of Human Learning, forthcoming from Johns Hopkins University Press. Bowen is the former president of Goucher College and Watson is the vice president for digital innovation at the American Association of Colleges and Universities.

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Is AI finally a way to reduce higher ed costs? (opinion)

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AI could free up faculty time to focus on the teaching and relationship-building that matter most, José Antonio Bowen and C. Edward Watson write. New technology has often changed work. The Industrial Revolution did not eliminate weavers, brewers and farmers, but it did change core aspects of their work while increasing productivity and efficiency.

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Teaching Writing With AI Without Replacing Thinking: 4 Tips

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Teaching Writing With AI Without Replacing Thinking: 4 Tips

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Steve Graham believes AI can help students learn to write but he doesn't want it to replace the writing process.

"Writing can be a powerful tool, and we have evidence that it will help you understand the material better and remember it better," says Graham, a professor at Arizona State University, who has studied effective writing instruction and how AI does in writing assessments.

But that only happens if students actually write. "If we move to a point where something like chat GPT is writing for us, then we lose the advantage of thinking about the material we're writing about, which can increase our understanding," he says.

Reconciling these two goals -- having AI help students learn to write more efficiently without hijacking the cognitive benefits of writing -- should be a key goal of educators. Finding the ideal balance will require more work from both researchers and classroom educators, but Graham shares some initial tips for doing this currently.

1. Have Students Brainstrom Without AI

Many talk about how they use AI as a brainstorming partner and to generate ideas. Graham isn't certain that's the best use for students, at least not initially.

Instead he'd like to see the students get their ideas down on paper before working with AI. "You're still thinking about it, then you're bringing in other ideas," he says.

2. Have Students Write A First Draft Without AI

Once a student has the ideas for a paper, Graham advises having them write a first draft. "Then the polishing of the material may be a great place for AI to help us," he says. This might include sentence construction, changes to tone, and other aspects of writing improvement that are generally separate from the part of writing involved in thinking about a topic.

3. Have Students Go Deeper Than The First AI Response

AI tends to offer advice so confidently students might be tempted to take it whether it improves their writing or not. To overcome this, Graham suggests writing a sentence and then having AI suggest multiple versions of that sentence, and then rewriting it with the class making it clear why the final sentence you chose is the best.

"Just as we might teach students how to form more complex sentences from simpler sentences with something like sentence combining, we can have activities where we help students make those kinds of decisions so that they use the feedback from AI to improve their sentence skills and make informed decisions," Graham says.

4. Emphasize Thinking In All AI Writing Tasks

Graham says that teachers want to have students focus on the idea that "thinking" goes along with writing as they explore utilizing AI for writing help. Even parts of the revision process should be centered on student thinking. Bolstering digital citizenship is also important in reducing the use of AI to cheat and shortchange learning.

"We want to be sure that at each point that we use this where you could potentially take thinking out of the mix, that we make sure that we structure the situation so that that's not going to happen," he says. "We want to really emphasize ownership of what's written and responsibility. Ultimately, if AI helps you in some way, you need to be transparent about that. And if there's errors, they're your errors, because it's your responsibility to check on that."

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Teaching Writing With AI Without Replacing Thinking: 4 Tips

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AI has a lot of potential for writing students, but we can't let it replace the thinking parts of writing, says writing professor Steve Graham

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A.I. Is Getting Better Fast. Can You Tell What’s Real Now?

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A.I. Is Getting Better Fast. Can You Tell What’s Real Now?

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10 Ways to Detect AI Writing Without Technology

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10 Ways to Detect AI Writing Without Technology

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10 Ways to Detect AI Writing Without Technology

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Over the summer, I began seeing the first suspected cases of AI use in the introductory college writing courses that I teach online. Since then, AI-generated essays have become a more common element of these classes.

Thankfully, I've gotten much better at instantly spotting AI papers thanks to some tell-tale signs of ChatGPT's and other AI-generators' writing styles. I discuss the techniques I've learned to detect AI writing in my classes below.

Before we get to these strategies, however, it's important to remember that suspected AI use isn't immediate grounds for disciplinary action. These cases should be used as conversation starters with students and even - forgive the cliché - as a teachable moment to explain the problems with using AI-generated work.

To that end, I've written previously about how I handled these suspected AI cases, the troubling limitations and discriminatory tendencies of existing AI detectors, and about what happens when educators incorrectly accuse students of using AI.

With those caveats firmly in place, here are the signs I look for to detect AI use from my students.

1. How to Detect AI Writing: The Submission is Too Long

When an assignment asks students for one paragraph and a student turns in more than a page, my spidey sense goes off.

Almost every class does have one overachieving student who will do this without AI, but that student usually sends 14 emails the first week and submits every assignment early, and most importantly, while too long, their assignment is often truly well written. A student who suddenly overproduces raises a red flag.

2. The Answer Misses The Mark While Also Being Too Long

Being long in and of itself isn't enough to identify AI use, but it's often overlong assignments that have additional strange features that can make it suspicious.

For instance, the assignment might be four times the required length yet doesn't include the required citations or cover page. Or it goes on and on about something related to the topic but doesn't quite get at the specifics of the actual question asked.

3. AI Writing is Emotionless Even When Describing Emotions

If ChatGPT was a musician it would be Kenny G or Muzak. As it stands now, AI writing is the equivalent of verbal smooth jazz or grey noise. ChatGPT, for instance, has this very peppy positive vibe that somehow doesn't convey actual emotion.

One assignment I have asks students to reflect on important memories or favorite hobbies. You immediately sense the hollowness of ChatGPT's response to this kind of prompt. For example, I just told ChatGPT I loved skateboarding as a kid and asked it for an essay describing that. Here's how ChatGPT started:
As a kid, there was nothing more exhilarating than the feeling of cruising on my skateboard. The rhythmic sound of wheels against pavement, the wind rushing through my hair, and the freedom to explore the world on four wheels - skateboarding was not just a hobby; it was a source of unbridled joy.
You get the point. It's like an extended elevator jazz sax solo but with words.

4. Excessive Use of Lists and Bullet Points

Here are some reasons that I suspect students are using AI if their papers have many lists or bullet points:

1. ChatGPT and other AI generators frequently present information in list form even though human authors generally know that's not an effective way to write an essay.

2. Most human writers will not inherently write this way, especially new writers who often struggle with organizing information.

3. While lists can be a good way to organize information, presenting more complex ideas in this manner can be ....

4 ... annoying.

5. Do you see what I mean?

6. (Yes, I know, it's ironic that I'm complaining about this here given that this story is also a list.)

5. It's Mistake-Free

I've criticized ChatGPT's writing here yet in fairness it does produce very clean prose that is, on average, more error-free than what is submitted by many of my students. Even experienced writers miss commas, have long and awkward sentences, and make little mistakes - which is why we have editors. ChatGPT's writing isn't too "perfect" but it's too clean.

6. The Writing Doesn't Match The Student's Other Work

Writing instructors know this inherently and have long been on the lookout for changes in voice that could be an indicator that a student is plagiarizing work.

AI writing doesn't really change that. When a student submits new work that is wildly different from previous work, or when their discussion board comments are riddled with errors not found in their formal assignments, it's time to take a closer look.

7. Something Is Just . . . Off

The boundaries between these different AI writing tells blur together and sometimes it's a combination of a few things that gets me to suspect a piece of writing. Other times it's harder to tell what is off about the writing, and I just get the sense that a human didn't do the work in front of me.

I've learned to trust these gut instincts to a point. When confronted with these more subtle cases, I will often ask a fellow instructor or my department chair to take a quick look (I eliminate identifying student information when necessary). Getting a second opinion helps ensure I've not gone down a paranoid "my students are all robots and nothing I read is real" rabbit hole. Once a colleague agrees something is likely up, I'm comfortable going forward with my AI hypothesis based on suspicion alone, in part, because as mentioned previously, I use suspected cases of AI as conversation starters rather than to make accusations.

Again, it is difficult to prove students are using AI and accusing them of doing so is problematic. Even ChatGPT knows that. When I asked it why it is bad to accuse students of using AI to write papers, the chatbot answered: "Accusing students of using AI without proper evidence or understanding can be problematic for several reasons."

Then it launched into a list.

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7 Ways to Detect AI Writing Without Technology

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As more of my students have submitted AI-generated work, I've gotten better at recognizing it.

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Centaurs and Cyborgs on the Jagged Frontier

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Centaurs and Cyborgs on the Jagged Frontier

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"...consultants using ChatGPT-4 outperformed those who did not, by a lot. On every dimension. Every way we measured performance."

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A lot of people have been asking if AI is really a big deal for the future of work. We have a new paper that strongly suggests the answer is YES.

For the last several months, I been part of a team of social scientists working with Boston Consulting Group, turning their offices into the largest pre-registered experiment on the future of professional work in our AI-haunted age. Our first working paper is out today. There is a ton of important and useful nuance in the paper but let me tell you the headline first: for 18 different tasks selected to be realistic samples of the kinds of work done at an elite consulting company, consultants using ChatGPT-4 outperformed those who did not, by a lot. On every dimension. Every way we measured performance.

Consultants using AI finished 12.2% more tasks on average, completed tasks 25.1% more quickly, and produced 40% higher quality results than those without. Those are some very big impacts. Now, let's add in the nuance.

First, it is important to know that this effort was multidisciplinary, involving multiple types of experiments and hundreds of interviews, conducted by a great team, including the Harvard social scientists Fabrizio Dell'Acqua, Edward McFowland III, and Karim Lakhani; Hila Lifshitz-Assaf from Warwick Business School and Katherine Kellogg of MIT (plus myself). Saran Rajendran, Lisa Krayer, and François Candelon ran the experiment on the BCG side, using a full 7% of its consulting force (758 consultants). They all did a lot of very careful work that goes far, far beyond the post. So, please look at the paper to make sure you get all the details - especially if you have questions about numbers or methods. I need to simplify a lot to fit 58 pages of findings into a post, and any mistakes are mine, not my co-authors. Also, while we pre-registered these experiments, this is still a new working paper, so there might be errors or mistakes, and the paper is not yet peer-reviewed. With that in mind, let's get to the details...

AI is weird. No one actually knows the full range of capabilities of the most advanced Large Language Models, like GPT-4. No one really knows the best ways to use them, or the conditions under which they fail. There is no instruction manual. On some tasks AI is immensely powerful, and on others it fails completely or subtly. And, unless you use AI a lot, you won't know which is which.

The result is what we call the "Jagged Frontier" of AI. Imagine a fortress wall, with some towers and battlements jutting out into the countryside, while others fold back towards the center of the castle. That wall is the capability of AI, and the further from the center, the harder the task. Everything inside the wall can be done by the AI, everything outside is hard for the AI to do. The problem is that the wall is invisible, so some tasks that might logically seem to be the same distance away from the center, and therefore equally difficult - say, writing a sonnet and an exactly 50 word poem - are actually on different sides of the wall. The AI is great at the sonnet, but, because of how it conceptualizes the world in tokens, rather than words, it consistently produces poems of more or less than 50 words. Similarly, some unexpected tasks ( like idea generation) are easy for AIs while other tasks that seem to be easy for machines to do (like basic math) are challenges for LLMs.

I asked the ChatGPT with Code Interpreter to visualize this for you:

To test the true impact of AI on knowledge work, we took hundreds of consultants and randomized whether they were allowed to use AI. We gave those who were allowed to use AI access to GPT-4, the same model everyone in 169 countries can access for free with Bing, or by paying $20 a month to OpenAI. No special fine-tuning or prompting, just GPT-4 through the API.

We then did a lot of pre-testing and surveying to establish baselines, and asked consultants to do a wide variety of work for a fictional shoe company, work that the BCG team had selected to accurately represent what consultants do. There were creative tasks ("Propose at least 10 ideas for a new shoe targeting an underserved market or sport."), analytical tasks ("Segment the footwear industry market based on users."), writing and marketing tasks ("Draft a press release marketing copy for your product."), and persuasiveness tasks ("Pen an inspirational memo to employees detailing why your product would outshine competitors."). We even checked with a shoe company executive to ensure that this work was realistic - they were. And, knowing AI, these are tasks that we might expect to be inside the frontier.

In line with our theories, and as we have discussed, we found that the consultants with AI access did significantly better, whether we briefly introduced them to AI first (the "overview" group in the diagram) or did not. This was true for every measurement, whether the time it took to complete tasks, the number of tasks completed overall (we gave them an overall time limit) or the quality of the outputs. We rated that quality using both human and AI graders, who agreed with each other (itself an interesting finding).

We also found something else interesting, an effect that is increasingly apparent in other studies of AI: it works as a skill leveler. The consultants who scored the worst when we assessed them at the start of the experiment had the biggest jump in their performance, 43%, when they got to use AI. The top consultants still got a boost, but less of one. Looking at these results, I do not think enough people are considering what it means when a technology raises all workers to the top tiers of performance. It may be like how it used to matter whether miners were good or bad at digging through rock... until the steam shovel was invented and now differences in digging ability do not matter anymore. AI is not quite at that level of change, but skill levelling is going to have a big impact.

But there is more to the story. BCG designed one more task, this one carefully selected to ensure that the AI couldn't come to a correct answer. This wasn't easy. As we say in the paper "since AI proved surprisingly capable, it was difficult to design a task in this experiment outside the AI's frontier where humans with high human capital doing their job would consistently outperform AI." But we identified a task that used the blind spots of AI to ensure it would give a wrong, but convincing, answer to a problem that humans would be able to solve. Indeed, human consultants got the problem right 84% of the time without AI help, but when consultants used the AI, they did worse - only getting it right 60-70% of the time. What happened?

In a different paper than the one we worked on together, Fabrizio Dell'Acqua shows why relying too much on AI can backfire. In an experiment, he found that recruiters who used high-quality AI became lazy, careless, and less skilled in their own judgment. They missed out on some brilliant applicants and made worse decisions than recruiters who used low-quality AI or no AI at all. When the AI is very good, humans have no reason to work hard and pay attention. They let the AI take over, instead of using it as a tool. He called this "falling asleep at the wheel", and it can hurt human learning, skill development, and productivity.

In our experiment, we also found that the consultants fell asleep at the wheel - those using AI actually had less accurate answers than those who were not allowed to use AI (but they still did a better job writing up the results than consultants who did not use AI). The authoritativeness of AI can be deceptive if you don't know where the frontier lies.

But a lot of consultants did get both inside and outside the frontier tasks right, gaining the benefits of AI without the disadvantages. The key seemed to be following one of two approaches: becoming a Centaur or becoming a Cyborg. Fortunately, this does not involve any actual grafting of electronic gizmos to your body or getting cursed to turn into the half-human/half-horse of Greek myth. They are rather two approaches to navigating the jagged frontier of AI that integrates the work of person and machine.

Centaur work has a clear line between person and machine, like the clear line between the human torso and horse body of the mythical centaur. Centaurs have a strategic division of labor, switching between AI and human tasks, allocating responsibilities based on the strengths and capabilities of each entity. When I am doing an analysis with the help of AI, I often approach it as a Centaur. I will decide on what statistical techniques to do, but then let the AI handle producing graphs. In our study at BCG, centaurs would do the work they were strongest at themselves, and then hand off tasks inside the jagged frontier to the AI.

On the other hand, Cyborgs blend machine and person, integrating the two deeply. Cyborgs don't just delegate tasks; they intertwine their efforts with AI, moving back and forth over the jagged frontier. Bits of tasks get handed to the AI, such as initiating a sentence for the AI to complete, so that Cyborgs find themselves working in tandem with the AI. This is how I suggest approaching using AI for writing, for example. It is also how I generated two of the illustrations in the paper (the Jagged Frontier image and the 54 line graph, both of which were built by ChatGPT, with my initial direction and guidance)

Our paper, along with a stream of excellent work by other scholars, suggests that, regardless of the philosophic and technical debates over the nature and future of AI, it is already a powerful disrupter to how we actually work. And this is not a hyped new technology that will change the world in five years, or that requires a lot of investment and the resources of huge companies - it is here, NOW. The tools the elite consultants used to supercharge their work are the exact same as the ones available to everyone reading this post. And the tools the consultants used will soon be much worse than what is available to you. Because the technological frontier is not just jagged, it is expanding. I am very confident that in the next year, at least two companies will release models more powerful than GPT-4. The Jagged Frontier advances, and we have to be ready for that.

Even aside from any anxiety that statement might cause, it is also worth noting the other downsides of AI. People really can go on autopilot when using AI, falling asleep at the wheel and failing to notice AI mistakes. And, like other research, we also found that AI outputs, while of higher quality than that of humans, were also a bit homogenous and same-y in aggregate. Which is why Cyborgs and Centaurs are important - they allow humans to work with AI to produce more varied, more correct, and better results than either humans or AI can do alone. And becoming one is not hard. Just use AI enough for work tasks and you will start to see the shape of the jagged frontier, and start to understand where AI is scarily good... and where it falls short.

In my mind, the question is no longer about whether AI is going to reshape work, but what we want that to mean. We get to make choices about how we want to use AI help to make work more productive, interesting, and meaningful. But we have to make those choices soon, so that we can begin to actively use AI in ethical and valuable ways, as Cyborgs and Centaurs, rather than merely reacting to technological change. Meanwhile, the Jagged Frontier advances.

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Centaurs and Cyborgs on the Jagged Frontier

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I think we have an answer on whether AIs will reshape work....

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Share your videos with friends, family, and the world

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Generative AI Tools - Design & share beautiful engaging content with Visme

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AI in the classroom: What’s cheating? What’s OK?

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AI in the classroom: What’s cheating? What’s OK?

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AI in the classroom: What’s cheating? What’s OK?

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Now that generative AI tools like ChatGPT are available, classwork is bound to change as a result. How do we preserve academic integrity? What’s cheating,

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Artificial intelligence (AI) tools and systems are significantly shaping the education landscape, presenting both opportunities and challenges for institutions, faculty, students, and professional staff.

This AI Preparedness Checklist was created by 1EdTech's Emerging Digital Pedagogies Innovation Leadership Network (ILN) to provide institutions with guiding prompts for establishing protocols, policies, and best practices for using AI in teaching and learning.

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Artificial intelligence (AI) tools and systems are significantly shaping the education landscape, presenting both opportunities and challenges for institutions, faculty, students, and professional staff. This AI Preparedness Checklist was created by 1EdTech's Emerging Digital Pedagogies Innovation Leadership Network (ILN) to provide institutions with guiding prompts for establishing protocols, policies, and best practices for using AI in teaching and learning.

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Impact of AI assistance on student agency

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Impact of AI assistance on student agency

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I came across this paper this morning via Linkedin: www.sciencedirect.com/science/article/pii/... which has interesting implications for how we use AI pedagogically. The key research questions were, "Do students learn from the regular, detailed and personalised feedback provided by AI systems, and will they continue to exhibit similar behaviour in the absence of assistance? Or do they instead continue to rely on AI assistance without learning from it?" Note though that the context is fairly specific to using AI in the process of peer learning/feedback, and so the findings may not be applicable more widely for other forms of AI assistance. Also, this was not using the more modern large language models like GPTs. Interestingly, they found, "Results of the experiment suggest that students tended to rely on rather than learn from AI assistance. If AI assistance was removed, self-regulated strategies could help fill the gap but were not as effective as AI assistance. Results also showed that hybrid human-AI approaches that complement AI assistance with self-regulated strategies (SAI) were not more effective than AI assistance on its own."

-Danny Liu

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Cross-Campus Approaches to Building a Generative AI Policy

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Cross-Campus Approaches to Building a Generative AI Policy

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Particularly for new technologies that disrupt long-standing practices and cultural beliefs, the work of carefully and intentionally developing effective policies can pay significant dividends.

Credit: vinnstock / Shutterstock.com © 2023

Higher education focuses on building knowledge and investing in the written word, whether it's scholarly research or demonstrations of learning such as essays, research papers, theses, or dissertations. The underlying assumption is that such work could not be significantly faked aside from paying another individual (such as student paper mills) or taking the work of others without attribution. Generative AI upends much of that baseline and in fact will challenge many institutions' academic integrity policies because they often prohibit copying from websites or acquiring papers from other individuals. Generative AI will also introduce other ethical and procedures considerations throughout the institution.

This piece evolved from our institutional roles (instructional design, writing support, and research and library support), our individual work at our respective institutions, and a half-day workshop on generative AI policy development that we facilitated for the NERCOMP 2023 Annual Conference. The workshop included a student panel, followed by focused conversations around several topics related to generative AI: ethics and plagiarism, bias, instructional technology, opportunities, and threats. For the final part of the workshop, we facilitated a mini design sprint for policy development.

The Need for Policy

One of the most pressing examples of areas for guidance is the discourse around plagiarism and how to detect it in an era of AI-generated content. Institutions are under pressure (whether organic or manufactured) to respond in some manner to the rise of generative AI as a widely available tool. The first wave of awareness seemed to be generated around plagiarism concerns and worker replacement theories ("Students will never write original papers again" and "Robots are replacing everyone"). For those in higher education, the drumbeat of fearmongering about plagiarism has succeeded in capturing faculty and administrative attention.

Early on, some assumed that generative AI work submitted by students could be identified with generative AI plagiarism checkers. TurnItIn's documentation states, "Our AI Writing detector's false positive rate is less than 1% for documents with 20% or more AI writing (our tests showed that in cases where we detect less than 20% of AI writing in a document, there is a higher incidence of false positives)." However, after the TurnItIn AI Detector was used in practice, across 38 million papers, false negative and positive detections are now at a higher risk. TurnItIn and other companies claim to offer greater odds of correct detection than reality seems to bear out. Additionally, further research is showing biases in what AI-plagiarism detection tools flag as being AI generated, including an increased likelihood of inaccurately flagging multi-language learners' work as having been generated by AI. Beyond the problem of false accusations, this environment also creates an untenable situation for students who must somehow defend themselves against a machine that cannot show its work but is just a projection. Additionally, students are using tools such as QuillBot, which will paraphrase text and use synonyms, to circumvent AI detection tools.

As original student writing becomes increasingly difficult to verify and confirm, developing a policy for AI detection might result in its being outdated just as the policy is implemented. Having an agile committee representative of diverse campus needs who can both review these issues and considerations to craft a more useful policy is part of what is needed to both protect students and choose the right tools for navigating the technological changes.

This is but one issue that higher education needs to navigate when crafting policy around generative AI. Other issues include but are not limited to the following:

The role of generative AI in visual and textual outputs of the institution, be they for marketing, for social media, or in reports

How and where faculty can use generative AI in the creation of course content, assignments, and feedback or assessments

Addressing the embedded biases of the data and outputs

The impact and challenge of information literacy

The environmental impact of generative AI, including greenhouse gasses and water usage

The impact on workers needed to run generative AI

Starting with the End

In many areas of life, including instructional design, an ideal place to start is with the results-what you want the outcome to be or do. To feel that your-and your collaborators'-time has not been wasted, and to see that you have made progress toward what might still be an abstract version of achievement, codify what success looks like for you, your collaborators, your stakeholders, and the final institutional arbiters. As AI continues to develop at a rapid pace, it is important to pause and do enough research so that you can formulate the questions you ultimately seek to answer. Below are some questions to get you started in thinking about this policy:

Whom is the policy going to focus on? Students, faculty, staff, administration, third-party vendors, contractors, etc.?

Can the same policy apply across the institution, or will different policies be needed for different parts of the organization?

Will the policy stand on its own, or will there be room for adjustments (for example, will students encounter variations depending on whether instructors-under the notion of academic freedom-want to encourage or discourage certain uses of generative AI for the purposes of teaching)?

What can or will be the implications of violating the policy?

What methods of accountability with the policy can be created when it may be hard to actually detect generative AI text?

Will there be differences between institutionally affiliated generative tools and those that are available to anyone?

Perhaps you are still in the fact-finding and idea-generating stage-you might need to specifically dedicate time to consider what will constitute your goal for the mission. You may decide that success for your group means developing and writing down these measurable goals. That is, you might still be in the phase of brainstorming and research, and you might need to take time to decide what your goals are. Ultimately, all involved should agree on specific measurable outcomes that are necessary for your group to have completed your work.

It is natural to feel a sense of urgency to take action, and it might be tempting to rush through this step. Although this process will not necessarily be easy, it will be helpful in carving out exactly what you are going to do in this phase. One of the trickiest parts about generative AI is that it has continued to change and shift in the past 12 months and will likely continue to do so, especially as other forms of generative AI (image, audio, video, slides, etc.) become increasingly available. Therefore, no matter what your goals are, creating a mechanism for revisiting, adjusting, updating will be equally important to name and anticipate early on. In that case, think in terms of not getting it perfect the first time through but having a process to iterate.

Identifying Stakeholders

Generative AI has the possibility of hitting every part of the institution. Because it is difficult to imagine an area that generative AI won't touch, it's useful to think about all the institutional stakeholders who will need guidance around usage of these new tools. Start by considering all the user groups and their iterations at your institution. One approach would be to conduct an audit of your campus; if your institution has an organization chart, that is a great document to use as the foundation. Some people and groups are likely not represented, so detective work will come into play. This is a great opportunity to break down institutional silos and examine your own invisible biases about the roles others play on our campuses.

Identify non-classroom student- and faculty-facing roles, which could include instructional designers, writing specialists, librarians, and academic support services. Then move on to people whose work has an impact on the campus experience as a whole. This could include staff working in IT (e.g., information security, academic computing, or web services), institutional communications, student life, even alumni relations. Be mindful that just because a group is not readily visible to you does not mean that they will not be impacted by or have an impact on your policies. We are a good example of coming together to collaborate on AI policy. The diversity of our professional perspectives has lent itself to not only seeing different viewpoints but also creating the opportunity to truly understand the nuances in just how many ways AI is impacting our institutions.

Consider, too, your contingent communities, such as part-time educators (adjuncts) and part-time staff. How are they included in the community in general? Do they experience regular communication patterns as the full-time employees do, such as being included in campus-wide emails? Will you need to set aside time to engage them in discussion separately, and will they need more training and professional development as you enact the policies you create? Look for allies who can serve as conduits to get people involved. For instance, adjuncts are often on campus for limited amounts of time during the day or might work elsewhere during typical business hours. When you are scheduling conversations, be mindful that you will need to create opportunities for those who work nontraditional schedules to be involved.

Some questions to consider for other entities at your institution may include the following:

Will the institution's upper management consider using generative AI to surveil employees' work to detect efficiency or generate employee evaluations?

Should your policymaking efforts account for this scale of institutional use?

How will AI be addressed in human resources, especially the recruiting and hiring process?

In what ways will the community outside the campus be impacted by your policies?

Do you have community partnerships in which you offer students an opportunity to put their theoretical learning into practice? Do you have an obligation to educate your students on the uses of generative AI as part of that collaboration?

Will your institution be viewed as being a policy and position leader on the subject of generative AI?

What will be reasonable and equitable means of challenging outputs by generative AI?

Finally, one of the most visible and yet frequently overlooked communities is the students. The value of engaging students in determining this policy cannot be overstated. To craft a policy without their input could result in a policy that feels out of touch and irrelevant to them. At the NERCOMP workshop, the highlight was hearing at the student panel how they demonstrated their own deep and sophisticated thoughts about generative AI and its roles in their lives. In particular, the conversation should extend beyond plagiarism and also include student perspectives about how other institutional areas should be using it (e.g., faculty, marketing, communications). Many students are aware of and interested in how generative AI is currently being employed in a wide variety of industries. Students might be reading about the way Paul McCartney used generative AI to restore John Lennon's voice to create a "new" Beatles track, or they may be interested in how generative AI is being used to dramatically improve recycling programs. These interests might be general curiosity (to be encouraged!), but they might also tie into students' future employment goals.

The consequences of not including a diverse array of groups in these conversations far outweigh any inconvenience you may experience when trying to find times and ways to gather input throughout the policy development process. Our workshop included people from many of the groups mentioned, and a key takeaway was just how much we learned from each other. Many realized that while something may seem obviously important to you, it may not even be considered by someone working in a different position.

Models for Developing Policies

Depending how an institution is structured and how much its leaders want to include different groupings within the organization, any of several models can be deployed to develop policies around generative AI. The following options are useful approaches and, to some degree, can be mixed and matched to meet the institution's needs and structures.

Task Force Model

Put out a call to action to form an inclusive team representing all aspects of the institution to create a policy task force. Alternatively, it could be a smaller, nimbler team that works to engage with different stakeholders across the institution, creating a template policy for generative AI and adjusting and tweaking it for each area of the institution, in conversation and collaboration with relevant stakeholders.

For instance, the policy for students and faculty is likely to look different from that of the marketing department, but realistically all of these groups should have AI policies. This might seem self-evident for students, but there are important questions to consider even for marketing, such as the use of generative AI image tools to represent students. Would that adhere to the institution's ethos or marketing ethics? Those are the kind of questions that would be relevant to marketing that students and faculty may not need to be involved in.

Governance Model

Some institutions have governance models in which faculty and staff play pivotal roles in the development and creation of institutional policy, particularly around policies that directly impact the classroom and students. This can be a useful model to gather a range of voices throughout the organization and have a clear pathway and record (e.g., committee notes) of how decisions are made and implemented. Yet these processes may encounter challenges in moving at an effective pace to make decisions that are timely and responsive to a changing environment. Due to the rate of change in generative AI, committees might create policies for assumptions around generative AI that are no longer valid.

Design Sprint Model

1. Understand: The first step is to understand how generative AI is currently being used (or isn't) at your institution, the concerns of the community, and the ideal state for AI use. This can be achieved through a listening tour, lighting talks, surveys, or structured conversations.

2. Define: Next, the policy team should review all the information gathered in the first step to define their main goals and desired outcomes that reflect the needs of the community.

3. Sketch: Each individual on the policy committee should sketch out their own draft policy that meets the definitions outlined previously. The larger committee can then review all policy drafts and then narrow down the ideas of each draft into a finalized Solution Sketch.

4. Decide: The policy committee will then review all Solution Sketches to decide which version they want to use to move forward with the process.

5. Prototype: The prototype can be considered the first draft of the policy to be shared with the community.

6. Validate: Return to the community members from the first phase of the design sprint process, gather feedback based on your policy draft, and make relevant edits. This is the last stage of the design sprint process, which will involve crafting the final version of the policy ready to be shared with your institution.

Consultant Model

Exemplar Model

Another option is to follow the lead of institutions that are publishing their own policies. For instance, Lance Eaton and his students have proposed and posted policies for College Unbound. Starting with one of these policies, the institution can then review, share for input, and adapt as needed. This can save a lot of time in terms of coming up with the initial approach, but it will need to be tailored to the specific needs of the institution and its population. If the policy creation team is reluctant to borrow from another institution, they could use a generative AI tool to draft the initial policy and workshop it across the institution. We used ChatGPT to generate several examples of policies to help get you started crafting your own.

Recommendations for Gathering Voices

To create the clearest and most effective policy, all stakeholders' voices must be heard. Because recruiting volunteers for their insights in drafting a policy can sound tedious, consider using both formal and informal routes to get their assistance. Also important is conveying how each stakeholder may be impacted by such a policy and emphasizing that their experiences and observations are highly valuable to the process. Asking for help drafting a policy document can be overwhelming to many. These approaches might make this more engaging:

Connect with people in mini roundtable discussions or small-group conversations

Find help through social media-use polling features to generate feedback and locate allies

Have one-on-one conversations with the people you see as your greatest supporters and greatest challengers in this process. Diverse perspectives are necessary to have a policy that works for everyone.

Allow for enough time in discussions. It can be easy to rush through things, but these discussions will include deep topics and issues that need time to be fully processed.

Provide collaborative documents for participants to add text, and use the comment feature for questions or additional thoughts.

Institutional knowledge is both an advantage and a disadvantage in this process. Some stakeholders will be obvious, and some people will quickly volunteer. Others may be more challenging to reach because of their disinterest or because they don't see the relevance to their work. These are important people to bring into the conversation, rather than making assumptions about their thoughts or possible contributions.

Framework for Generative AI Policy Creation

Below are the relevant sections that a generative AI policy should address to help institutions in their policy development processes. Each institution should have its own individual policy based on the needs of its community.

Policy Audience: Whom is this policy for? Is it for the entire institution, faculty, students, staff, departments, third-party vendors, or others?

Policy Timeline: What is the timeline for implementation? Should that timeline include a review and update cycle after initial implementation?

Policy Tools: What counts as AI for this policy? Is it focused on all AI or only generative AI? Is it focused on all generative AI or just text-generating AI?

Academic Integrity Guidelines: If the policy is related to student or faculty work, what are the integrity requirements to make sure academic integrity is upheld?

Acceptable Use: If AI usage is acceptable, are there any limitations on the amount of usage (e.g., a certain percentage must be individually generated in certain contexts) or purposes for which generative AI may be used?

Transparency: What practices are in place for communicating its usage throughout the institution?

Security and Legal Considerations: What concerns need to be addressed concerning privacy, intellectual property, and proprietary knowledge around using external or enterprise generative AI tools? Does endorsing the use of generative AI conflict with any laws such as the General Data Protection Regulation?

Ethical Considerations: What concerns or responsibilities does the institution have explicitly or implicitly within its mission that conflict with the environmental, human-exploitation, and bias issues related to generative AI?

Institutional Resources: Which areas of the institution will be committed to supporting, responding to, and implementing uses of generative AI?

Processes for Policy Violation: If the policy is violated, what are the steps for identifying and addressing it?

Conclusion

Institutional policy development can often be a methodical process that both informs and better unifies an institution's approach to a particular challenge. And yes, it can be tedious. Yet many institutions are often unprepared for technological developments, and the delays in catching up have a deep impact on students, faculty, and staff (e.g., the reactive response to the pandemic). Although the generative AI cat is out of the bag, there's still an opportunity to meaningfully guide-through a collaborative effort-how it can be best used by all the stakeholders. Such an approach will result in a better learning and working environment for students, faculty, and staff. Taking an iterative approach to AI policy might feel unnatural for those who operate best with exhaustive policy documents, but it just might be the key to successfully navigating this new technological reality.

Notes

1. Daniel E. O'Leary, "Gartner's Hype Cycle and Information System Research Issues," International Journal of Accounting Information Systems 9, no. 4 (December 1, 2008): 240-52; Jackie Wiles, "What's New in Artificial Intelligence from the 2022 Gartner Hype Cycle," Gartner, September 15, 2022.

2. Sam Levine, "Vanderbilt Apologizes for Using ChatGPT in Email on Michigan Shooting," The Guardian, February 22, 2023; Josh Moody, "The ChatGPT Commencement Address," Inside Higher Ed, June 29, 2023; Lauren Coffey, "Harvard Taps AI to Help Teach Computer Science Course," Inside Higher Ed, June 30, 2023.

Esther Brandon is Manager of Learning Design & Technology Adoption at Harvard Medical School.

Lance Eaton is Director of Faculty Development & Innovation at College Unbound.

Dana Gavin is Director of the Writing Center at Dutchess Community College.

Allison Papini is Assistant Director/Manager of Research & Instruction Services at Bryant University.

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cross-campus approaches to building a generative ai policy

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Cross-Campus Approaches to Building a Generative AI Policy

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Particularly for new technologies that disrupt long-standing practices and cultural beliefs, the work of carefully and intentionally developing effect

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Pope, once a victim of AI-generated imagery, calls for treaty to regulate artificial intelligence

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Pope Francis is calling for an international treaty to ensure artificial intelligence is developed and used ethically

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7 Things You Should Know About Generative AI

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7 Things You Should Know About Generative AI

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The release of ChatGPT and similar AI tools that generate content including text, images, and audio has prompted both excitement and apprehension among leaders, faculty, students, and others in higher education.

Credit: TARASIRI / Shutterstock.com © 2023

Scenario

Over her many years teaching European history, Margaret Hilliard had been generally open to incorporating new approaches to teaching, certainly more open than those of her colleagues who bristled at any suggestion that their teaching methods could benefit from updates, including those rooted in technology. But when ChatGPT was released, Hilliard felt unprepared and deeply unnerved. Her courses had long featured essays as a central element, not only as a way for students to demonstrate their understanding of the course material but also as a regular activity to help students improve their writing. Hilliard stressed the importance of thinking broadly about historical events as well as the necessity of being able to clearly articulate those ideas. With ChatGPT, the dynamic in her courses changed, essentially overnight. Her students were suddenly producing longer essays that were better organized, grammatically impeccable, and always on time. At the same time, though, many of the essays were conceptually flimsy, lacking the kind of broad thinking she tried to draw out of her students, and the essays often included outlandish factual inaccuracies.

Hilliard spoke to her colleagues in the history department, as well as faculty who taught literature and sociology, and all were seeing the same changes in student assignments. A design instructor told her that he was wrestling with how to deal with student work that he believed was being generated by image-based AI. Meanwhile, campus leaders had begun issuing statements, policy updates, and other guidance about the use of generative AI tools for academic work, but those efforts seemed always to be a step behind the technology and how it was being used.

Talking directly and candidly with her undergraduate students-and conferring at length with her graduate students and TAs-Hilliard undertook to reorient her teaching to try to preserve the value of the essay without being naïve about the reality that generative AI tools were here to stay and would only get better over time. For some assignments, she required her students to use AI to create a first draft of a short essay and then critique that draft, looking for errors, omissions, and opportunities to improve the arguments. This enabled a different kind of learning for the students, including forcing students to carefully consider the prompts they feed into the AI tools, and it helped remove the stigma of AI in education. For other essays, she told her students to write their own drafts unaided and then use AI to generate an essay on the same topic. Students would then compare the two, seeking to understand how and why they overlapped and diverged; this process proved particularly beneficial for students whose skills with organization and sentence structure needed attention. She knew, of course, that a few students would simply use AI as a crutch and flaunt the honor code, but such students existed before ChatGPT. For Hilliard, it felt a bit like the Wild West of education, but she found that pulling AI tools into the learning process rather than vilifying them provided opportunities for different kinds of learning and avoided much of the panic that surrounded these new technologies.

What Is It?

Artificial intelligence has long operated behind the scenes in many of the technologies we use every day, but with the introduction of ChatGPT in November 2022, generative AI took the world by storm, capturing the imaginations and stoking the fears of broad swaths of users. Within its first 60 days, the platform grew by 9,900% to reach 100 million users. When the 2023 academic year began, the ChatGPT platform experienced another surge, with 180.5 million unique visitors worldwide during the month of August alone. Artificial intelligence came to dominate conversations and left many searching to understand... what is generative AI?

While the term artificial intelligence has taken on many meanings over the years, generative AI creates content that resembles something produced by humans. The best-known tools create written content, but as the technology has evolved, generative AI tools now can create text, images, sound, or even entire applications. Generative AI is increasingly integrated into various products and services-an estimated 77% of all global companies are either using AI or exploring its use in their business operations. In many cases, these functions are so intricately woven into the fabric of the product that users might not even be aware of their existence. ChatGPT and other generative AI tools are extremely versatile, evolving, and developing into multidimensional tools that are challenging norms and disrupting diverse sectors, including education. An important part of the discussion about generative AI is what it is not. It is not sentient. It lacks awareness and cannot perceive or understand human thought or emotions. The operations are based purely on the data used to train the AI models.

How Does It Work?

Generative AI revolves around neural networks, complex structures inspired by the human brain with interconnected artificial neurons for processing information and learning from data. Whereas algorithm-based models make decisions based on rules that are provided by developers, generative AI tools review large amounts of data to construct an evolving set of rules that guide the generation of new content. Generative AI operates by making predictions about the next item in a series. When generative AI is writing a paragraph, for example, it constructs sentences by predicting each next word in succession. Over time, with lots of practice and lots of data, the system gets better at making predictions.

Large language models (LLMs) are trained on massive textual datasets, including books, articles, and websites, to learn the intricacies of human language. The datasets used to train ChatGPT, for example, were developed using public information on the internet, licensed third-party content, and information supplied by platform users and human trainers. Patterns are identified within the training data through statistical analysis as the machine learning model detects recurring sequences of data, such as words, phrases, or patterns of pixel values in images, and associates them with specific outcomes or contexts. By discovering and modeling these patterns, generative AI can produce new content, ensuring consistency within the context while introducing creativity and novelty. Combining LLMs with natural language processing (NLP) models, which process and understand human language such as language translation and sentiment analysis, results in content that mirrors human-generated material.

Who's Doing It?

The use of generative AI is rapidly growing. Students are increasingly using generative AI tools for essays, research papers, and other written assignments. Faculty members are leveraging generative AI for creating custom learning materials, quizzes, and assessments, and they can also use generative AI to provide timely and accurate feedback to students. Librarians are using generative AI for creating text summaries or discovering insights in their collections. Instructional designers are using generative AI for rapidly iterating on created materials. For administrators, generative AI aids decision-making and communication by summarizing documents and meetings as well as creating documents. Beyond textual tools, generative AI is being used to create visual artifacts and to generate or debug computer code. Software companies are including generative AI into their products like Adobe's Photoshop's Generative Fill or Microsoft's 365 Copilot.

Why Is It Significant?

Generative AI is suitable for a wide range of applications. There are stand-alone applications-such as chatbots, customizable agents, and virtual assistants-in which generative AI is the primary feature. In other applications such as content generators, creative tools, and everyday tools like Microsoft Word and Google Docs, generative AI is essentially invisible and just one feature among many. Part of the reason for this proliferation is that generative AI is becoming open to developers with cloud-based services, open-source frameworks, and a growing number of AI tools that make it easy to implement generative AI solutions.

By allowing individuals and businesses to create, design, and innovate in ways that were previously difficult or expensive, generative AI has the potential to be transformative in many ways, in various professional and educational contexts. Free (or inexpensive) tools that can quickly create content that otherwise requires human skill and time might alter the landscape of what certain jobs look like and the qualifications needed for those roles. In education, the ability for any student to generate written work that is difficult or impossible to discern as having been produced by technology can fundamentally change the kinds of activities that have long been the hallmarks of how students learn and demonstrate mastery of learning material.

What Are the Downsides?

The mismatch in the pace of technological development and the readiness of our social, ethical, and legal frameworks raises concerns about the responsible use of generative AI. It necessitates the development of robust guidelines, policies, and ethical considerations to ensure that this technology is harnessed for the greater good while minimizing potential risks. Training generative AI by using content from the internet means that significant amounts of material that is racist, sexist, or violent must be managed so that it doesn't infect the tool's output. Generative AI's ability to produce human-like content raises the risk that plagiarism and academic dishonesty will become more challenging to detect, necessitating the adoption of advanced detection tools or a shift in policy. This can also be addressed by fostering a culture of academic integrity and educating students about the ethical use of AI tools.

Because these tools can appear to magically provide the correct answers to questions, some users will trust the output without question, even though the tools sometimes generate counterfactual content. This misplaced confidence creates opportunities for those who use technology to do harm, such as through deepfake images or videos. For those inclined to verify the accuracy of generative AI output, the tools do not always cite sources or explain the reasoning behind the generated content. Meanwhile, the output is difficult to cite as a source because it is not repeatable.

Generative AI tools are accepting larger amounts and more types of data, and it can be difficult to know whether the tools are retaining and using data for other purposes. This uncertainty risks exposing data that should not be shared outside of an organization, including personally identifying information and other private data. The ownership of both the data that the tools are trained on and the output is currently being litigated. Several lawsuits are pending against companies for using data without permission. Depending how these cases are resolved, generative AI tools might be required to remove some data or exclude certain datasets, potentially causing the tools to be less accurate. Meanwhile, the output generated by AI tools has been ruled as ineligible for U.S. copyright because it is not authored by a human. However, a U.S. Supreme Court ruling from 1884 could provide a different argument for those who are interacting with generative AI tools as a "copilot" to create new content.

Many of the companies developing generative AI tools have not provided information regarding how the tools are trained and how safety tests are conducted. Each company has its own set of guidelines for what kinds of content are appropriate in terms of ethical, moral, and equity considerations, but this information is often not made clear to the users of these tools. While multiple governments are developing policies and guidelines for detecting AI-generated content and rooting out algorithmic discrimination, this guidance has not yet been finalized.

Another downside is the environmental impact of these tools. The carbon footprint includes training the model, running the model, and operating data centers. It is estimated that creating GPT-3 "consumed 1,287 megawatt hours of electricity and generated 552 tons of carbon dioxide equivalent, the equivalent of 123 gasoline-powered passenger vehicles driven for one year."

Where Is It Going?

In response to privacy concerns, organizations will begin to purchase or develop their own generative AI tools that do not share data beyond the organization. A college or university could purchase a tool for its faculty, staff, and students that has access to the institution's data but does not share the data beyond the institution or with certain user groups. As generative AI tools are provided with larger, cleaner datasets, larger LLMs will become better at more tasks. Some of these tasks include providing image-to-text services that could be used to make online courses more accessible by describing images used in the course, better online captions for audio and video, and language translations that are more accurate and localized. Several vendors have already integrated generative AI tools into their products to provide students with tutoring and support in areas such as coaching through math problems and the writing process.

Generative AI tools are emerging that will help faculty curate content for their courses. Some of these tools will build a learning module based on the course learning objectives and other prompts. Other tools can be used to develop assessment activities that are linked to the course content and learning objectives. Future developments may include tools that monitor students' performance and suggest adjustments to course content and assessments.

Another expectation is that generative AI tools will be further integrated into many everyday applications, including learning management systems, common productivity applications such as document and spreadsheet tools, and image editors. It will become difficult to use these common applications without AI tools providing suggestions and guidance.

As the workforce changes, how we prepare students will also need to change. We will need to teach students how AI in their fields will impact their work. This includes determining the source of data and understanding how algorithms are currently being used. Then we will need to teach students how to work with AI so that they are able to use these tools as a "copilot," as opposed to being replaced by these tools.

What Are the Implications for Higher Education?

Generative AI promises to give educators the ability to provide deeper learning and a more personalized experience along with reduced workload, but this will require focus on what and how we want students to learn and how they can obtain the tools to thrive in an environment in which AI is ubiquitous. The ability to rapidly generate many versions of material from different viewpoints allows students and faculty to explore ideas in novel ways. For example, an image could be synthesized around a given concept based upon a student's understanding. This image could be compared with images created by other students to discuss what commonalities and differences are present. This is something that is a strength of generative AI and gives all students, not just those with artistic skills, the ability to communicate concepts visually. It is incumbent on institutions to promote AI literacy among students and faculty and to provide information on how to use generative AI tools effectively and ethically, which can improve students' and faculty's experiences and facilitate learning.

For students, feedback is critical to success. Generative AI can assist with many of the techniques of effective feedback, two of which state that feedback should be specific and given on a timely basis. Faculty can use generative AI to rapidly craft specific feedback for students. Faculty can also leverage AI to teach students how to evaluate their own work, which involves them more deeply in the process of learning.

Institutions need to evaluate how students are being taught and evaluated in light of a tool that can generate content. What is the role that an essay plays in demonstrating mastery, and what is an appropriate role for generative AI in creating that essay? What is the role of homework? What constitutes ethical use of generative AI, and how much uniformity does the institution want when it comes to the use of generative AI tools? Significant investments in professional development and support will be needed to help faculty and administrators effectively integrate generative AI into teaching and administrative processes. Meanwhile, the absence of clear policies and guidelines regarding the use of AI in higher education could lead to inconsistencies in its implementation. Educational institutions should develop comprehensive strategies addressing areas including ethical considerations, data privacy, and intellectual property rights to ensure responsible and equitable use of generative AI.

Notes

1. Graham Duggan, "We've Been Using AI for Years - We Just Didn't Know It," CBC Docs, September 19, 2023 (last updated September 28, 2023); "107 Up-to-Date ChatGPT Statistics & User Numbers," Nerdynav, November 27, 2023; Anna Tong, "Exclusive: ChatGPT Traffic Slips Again for Third Month in a Row," Reuters, September 7, 2023.

2. See "Second Request for Reconsideration for Refusal to Register Théâtre D'opéra Spatial," Copyright Review Board, September 5, 2023.

3. See "Burrow-Giles Lithographic Co. v. Sarony," March 17, 1884, Legal Information Institute, Cornell Law School.

Contributors

Heather Brown is Instructional Designer at Tidewater Community College.

Steven Crawford is the District Director for the Maricopa Center for Learning and Innovation at Maricopa County Community College District.

Kate Miffitt is Director for Innovation at California State University, Office of the Chancellor.

Tracy Mendolia-Moore is Manager of 3D Education Technology Innovations at Western University of Health Sciences.

Dean Nevins is Executive Director of Information Technology at Santa Barbara City College.

Joshua Weiss is Director of Digital Learning Solutions at Stanford Graduate School of Education.

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7 things you should know about generative ai

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7 Things You Should Know About Generative AI

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The release of ChatGPT and similar AI tools that generate content including text, images, and audio has prompted both excitement and apprehension amon

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2023 EDUCAUSE Horizon Action Plan: Generative AI

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This report describes an ideal future for generative AI in higher education, drawing on the insights of a small panel of teaching and learning higher

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Today, many priorities for improvements to teaching and learning are unmet. Educators seek technology-enhanced approaches addressing these priorities that would be safe, effective, and scalable. Like all of us, educators use AI-powered services in their everyday lives, such as voice assistants in their homes; tools that can correct grammar, complete sentences, and write essays; and automated trip planning on their phones. As a result, educators see opportunities to use AI-powered capabilities like speech recognition to increase the support available to students with disabilities, multilingual learners, and others who could benefit from greater adaptivity and personalization in digital tools for learning. They are further exploring how AI can enable writing or improving lessons, as well as their process for finding, choosing, and adapting material for use in their lessons.

Educators are also aware of new risks. Useful, powerful functionality can also be accompanied with new data privacy and security risks. Educators recognize that AI can automatically produce output that is inappropriate or wrong. They are wary that the associations or automations created by AI may amplify unwanted biases. They have noted new ways in which students may represent others' work as their own. They are well-aware of "teachable moments" and pedagogical strategies that a human teacher can address but are undetected or misunderstood by AI models. They worry whether recommendations suggested by an algorithm would be fair. Educators' concerns are manifold.

Everyone in education has a responsibility to harness the good to serve educational priorities while also protecting against the dangers that may arise as a result of AI being integrated in educational technology.

NEW!! Report on AI and the Future of Teaching & Learning

Key Insights

AI enables new forms of interaction. Students and teachers can speak, gesture, sketch, and use other natural human modes of communication to interact with a computational resource and each other. AI can generate human-like responses, as well. These new forms of action may provide supports to students with disabilities.

AI can help educators address variability in student learning. With AI, designers can anticipate and address the long tail of variations in how students can successfully learn-whereas traditional curricular resources were designed to teach to the middle or most common learning pathways. For example, AI-enabled educational technology may be deployed to adapt to each student's English language abilities with greater support for the range of skills and needs among English learners.

AI supports powerful forms of adaptivity. Conventional technologies adapt based upon the correctness of student answers. AI enables adapting to a student's learning process as it unfolds step-by-step, not simply providing feedback on right or wrong answers. Specific adaptations may enable students to continue strong progress in a curriculum by working with their strengths and working around obstacles.

AI can enhance feedback loops. AI can increase the quality and quantity of feedback provided to students and teachers, as well as suggesting resources to advance their teaching and learning.

AI can support educators. Educators can be involved in designing AI-enabled tools to make their jobs better and to enable them to better engage and support their students.

Recommendations: Call to Action for Education Leaders

Recommendation #1: Emphasize Humans in the Loop

We reject the notion of AI as replacing teachers. Teachers and other people must be "in the loop" whenever AI is applied in order to notice patterns and automate educational processes. We call upon all constituents to adopt Humans-in-the-Loop as a key criteria.

Recommendation #2: Align AI Models to a Shared Vision for Education

We call upon educational decision makers, researchers, and evaluators to determine the quality of an educational technology based not only on outcomes, but also based on the degree to which the models at the heart of the AI tools and systems align to a shared vision for teaching and learning. The figure to the right describes the important qualities of AI models for education leaders to consider.

Recommendation #3: Design Using Modern Learning Principles

Achieving effective systems requires more than processing "big data"-it requires more than data science. Applications of AI must be based on established, modern learning principles, the wisdom of educational practitioners, and should leverage the expertise in the educational assessment community around detecting bias and improving fairness. Going forward, we also must seek to create AI systems that are culturally responsive and culturally sustaining, leveraging the growth of published techniques for doing so. Further, most early AI systems had few specific supports for students with disabilities and English learners and we must ensure that AI-enabled learning resources are intentionally inclusive of these students.

Recommendation #4: Prioritize Strengthening Trust

Technology can only help us to achieve educational objectives when we trust it. Yet, we learned through a series of public listening sessions that distrust of educational technology and AI is commonplace. Because trust develops as people meet and relate to each other, we call for a focus on building trust and establishing criteria for trustworthiness of emerging educational technologies within the associations, convenings, and professional organizations that bring educators, innovators, researchers, and policymakers together.

Recommendation #5: Inform and Involve Educators

We call on educational leaders to prioritize informing and involving educational constituents so they are prepared to investigate how and when AI fits specific teaching and learning needs, and what risks may rise. Now is the time to show the respect and value we hold for educators by informing and involving them in every step of the process of designing, developing, testing, improving, adopting, and managing AI-enabled educational technology. This includes involving educators in reviewing existing AI-enabled systems, tools, and data use in schools, designing new applications of AI based on teacher input, carrying out pilot evaluations of proposed new instructional tools, collaborating with developers to increase the trustworthiness of the deployed system, and raising issues about risks and unexpected consequences as the system is implemented.

Recommendation #6: Focus R&D on Addressing Context and Enhancing Trust and Safety

Research that focuses on how AI-enabled systems can adapt to context (diversity among learners, variability in instructional approaches, differences in educational settings) is essential to answering the question "Do specific applications of AI work in education, and if so, for whom and under what conditions?" We call upon researchers and their funders to prioritize investigations of how AI can address the long tail of learning variability and to seek advances in how AI can incorporate contextual considerations when detecting patterns and recommending options to students and teachers. Further, researchers should accelerate their attention to how to enhance trust and safety in AI-enabled systems for education.

Recommendation #7: Develop Education-Specific Guidelines and Guardrails

Data privacy regulation already covers educational technology; further, data security is already a priority of school educational technology leaders. Modifications and enhancements to the status quo will be required to address the new capabilities alongside the risks of AI. We call for involvement of all perspectives in the ecosystem to define a set of guidelines (such as voluntary disclosures and technology procurement checklists) and guardrails (such as enhancements to existing regulations or additional requirements) so that we can achieve safe and effective AI for education.

Listening Sessions

The U.S. Department of Education's Office of Educational Technology, with support from Digital Promise, held listening sessions about Artificial Intelligence (AI). We connected with all constituents involved in making decisions about technology in education, including but not limited to teachers, educational leaders, students, parents, technologists, researchers, and policy makers.

The goal of these listening sessions were to gather input, ideas, and engage in conversations that will help the Department shape a vision for AI policy that is inclusive of cutting-edge research and practices while also informed by the opportunities and risks.

Blog Posts

Need More Info?

Contact ed.tech@ed.gov with questions.

Please Include "OET AI" in the Subject Line

For a print-out and summary of the Artificial Intelligence Initiative, please review our AI One-pager

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Artificial Intelligence - Office of Educational Technology

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OET is working to develop policies and supports focused on the effective, safe, and fair use of AI-enabled educational technology.

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Shared by Mark Green

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Among the great challenges posed to democracy today is the use of technology, data, and automated systems in ways that threaten the rights of the American public. Too often, these tools are used to limit our opportunities and prevent our access to critical resources or services. These problems are well documented. In America and around the world, systems supposed to help with patient care have proven unsafe, ineffective, or biased. Algorithms used in hiring and credit decisions have been found to reflect and reproduce existing unwanted inequities or embed new harmful bias and discrimination. Unchecked social media data collection has been used to threaten people's opportunities, undermine their privacy, or pervasively track their activity-often without their knowledge or consent.

These outcomes are deeply harmful-but they are not inevitable. Automated systems have brought about extraordinary benefits, from technology that helps farmers grow food more efficiently and computers that predict storm paths, to algorithms that can identify diseases in patients. These tools now drive important decisions across sectors, while data is helping to revolutionize global industries. Fueled by the power of American innovation, these tools hold the potential to redefine every part of our society and make life better for everyone.

This important progress must not come at the price of civil rights or democratic values, foundational American principles that President Biden has affirmed as a cornerstone of his Administration. On his first day in office, the President ordered the full Federal government to work to root out inequity, embed fairness in decision-making processes, and affirmatively advance civil rights, equal opportunity, and racial justice in America.[i] The President has spoken forcefully about the urgent challenges posed to democracy today and has regularly called on people of conscience to act to preserve civil rights-including the right to privacy, which he has called "the basis for so many more rights that we have come to take for granted that are ingrained in the fabric of this country."[ii]

To advance President Biden's vision, the White House Office of Science and Technology Policy has identified five principles that should guide the design, use, and deployment of automated systems to protect the American public in the age of artificial intelligence. The Blueprint for an AI Bill of Rights is a guide for a society that protects all people from these threats-and uses technologies in ways that reinforce our highest values. Responding to the experiences of the American public, and informed by insights from researchers, technologists, advocates, journalists, and policymakers, this framework is accompanied by From Principles to Practice-a handbook for anyone seeking to incorporate these protections into policy and practice, including detailed steps toward actualizing these principles in the technological design process. These principles help provide guidance whenever automated systems can meaningfully impact the public's rights, opportunities, or access to critical needs.

You should be protected from unsafe or ineffective systems. Automated systems should be developed with consultation from diverse communities, stakeholders, and domain experts to identify concerns, risks, and potential impacts of the system. Systems should undergo pre-deployment testing, risk identification and mitigation, and ongoing monitoring that demonstrate they are safe and effective based on their intended use, mitigation of unsafe outcomes including those beyond the intended use, and adherence to domain-specific standards. Outcomes of these protective measures should include the possibility of not deploying the system or removing a system from use. Automated systems should not be designed with an intent or reasonably foreseeable possibility of endangering your safety or the safety of your community. They should be designed to proactively protect you from harms stemming from unintended, yet foreseeable, uses or impacts of automated systems. You should be protected from inappropriate or irrelevant data use in the design, development, and deployment of automated systems, and from the compounded harm of its reuse. Independent evaluation and reporting that confirms that the system is safe and effective, including reporting of steps taken to mitigate potential harms, should be performed and the results made public whenever possible.

From Principles to Practice: Safe and Effective Systems

You should not face discrimination by algorithms and systems should be used and designed in an equitable way. Algorithmic discrimination occurs when automated systems contribute to unjustified different treatment or impacts disfavoring people based on their race, color, ethnicity, sex (including pregnancy, childbirth, and related medical conditions, gender identity, intersex status, and sexual orientation), religion, age, national origin, disability, veteran status, genetic information, or any other classification protected by law. Depending on the specific circumstances, such algorithmic discrimination may violate legal protections. Designers, developers, and deployers of automated systems should take proactive and continuous measures to protect individuals and communities from algorithmic discrimination and to use and design systems in an equitable way. This protection should include proactive equity assessments as part of the system design, use of representative data and protection against proxies for demographic features, ensuring accessibility for people with disabilities in design and development, pre-deployment and ongoing disparity testing and mitigation, and clear organizational oversight. Independent evaluation and plain language reporting in the form of an algorithmic impact assessment, including disparity testing results and mitigation information, should be performed and made public whenever possible to confirm these protections.

From Principles to Practice: Algorithmic Discrimination Protections

You should be protected from abusive data practices via built-in protections and you should have agency over how data about you is used. You should be protected from violations of privacy through design choices that ensure such protections are included by default, including ensuring that data collection conforms to reasonable expectations and that only data strictly necessary for the specific context is collected. Designers, developers, and deployers of automated systems should seek your permission and respect your decisions regarding collection, use, access, transfer, and deletion of your data in appropriate ways and to the greatest extent possible; where not possible, alternative privacy by design safeguards should be used. Systems should not employ user experience and design decisions that obfuscate user choice or burden users with defaults that are privacy invasive. Consent should only be used to justify collection of data in cases where it can be appropriately and meaningfully given. Any consent requests should be brief, be understandable in plain language, and give you agency over data collection and the specific context of use; current hard-to-understand notice-and-choice practices for broad uses of data should be changed. Enhanced protections and restrictions for data and inferences related to sensitive domains, including health, work, education, criminal justice, and finance, and for data pertaining to youth should put you first. In sensitive domains, your data and related inferences should only be used for necessary functions, and you should be protected by ethical review and use prohibitions. You and your communities should be free from unchecked surveillance; surveillance technologies should be subject to heightened oversight that includes at least pre-deployment assessment of their potential harms and scope limits to protect privacy and civil liberties. Continuous surveillance and monitoring should not be used in education, work, housing, or in other contexts where the use of such surveillance technologies is likely to limit rights, opportunities, or access. Whenever possible, you should have access to reporting that confirms your data decisions have been respected and provides an assessment of the potential impact of surveillance technologies on your rights, opportunities, or access.

From Principles to Practice: Data Privacy

You should know that an automated system is being used and understand how and why it contributes to outcomes that impact you. Designers, developers, and deployers of automated systems should provide generally accessible plain language documentation including clear descriptions of the overall system functioning and the role automation plays, notice that such systems are in use, the individual or organization responsible for the system, and explanations of outcomes that are clear, timely, and accessible. Such notice should be kept up-to-date and people impacted by the system should be notified of significant use case or key functionality changes. You should know how and why an outcome impacting you was determined by an automated system, including when the automated system is not the sole input determining the outcome. Automated systems should provide explanations that are technically valid, meaningful and useful to you and to any operators or others who need to understand the system, and calibrated to the level of risk based on the context. Reporting that includes summary information about these automated systems in plain language and assessments of the clarity and quality of the notice and explanations should be made public whenever possible.

From Principles to Practice: Notice and Explanation

You should be able to opt out, where appropriate, and have access to a person who can quickly consider and remedy problems you encounter. You should be able to opt out from automated systems in favor of a human alternative, where appropriate. Appropriateness should be determined based on reasonable expectations in a given context and with a focus on ensuring broad accessibility and protecting the public from especially harmful impacts. In some cases, a human or other alternative may be required by law. You should have access to timely human consideration and remedy by a fallback and escalation process if an automated system fails, it produces an error, or you would like to appeal or contest its impacts on you. Human consideration and fallback should be accessible, equitable, effective, maintained, accompanied by appropriate operator training, and should not impose an unreasonable burden on the public. Automated systems with an intended use within sensitive domains, including, but not limited to, criminal justice, employment, education, and health, should additionally be tailored to the purpose, provide meaningful access for oversight, include training for any people interacting with the system, and incorporate human consideration for adverse or high-risk decisions. Reporting that includes a description of these human governance processes and assessment of their timeliness, accessibility, outcomes, and effectiveness should be made public whenever possible.

From Principles to Practice: Human Alternatives, Consideration, and Fallback

Applying the Blueprint for an AI Bill of Rights

While many of the concerns addressed in this framework derive from the use of AI, the technical capabilities and specific definitions of such systems change with the speed of innovation, and the potential harms of their use occur even with less technologically sophisticated tools.

Thus, this framework uses a two-part test to determine what systems are in scope. This framework applies to (1) automated systems that (2) have the potential to meaningfully impact the American public's rights, opportunities, or access to critical resources or services. These Rights, opportunities, and access to critical resources of services should be enjoyed equally and be fully protected, regardless of the changing role that automated systems may play in our lives.
This framework describes protections that should be applied with respect to all automated systems that have the potential to meaningfully impact individuals' or communities' exercise of: Rights, Opportunities, or Access
Civil rights, civil liberties, and privacy, including freedom of speech, voting, and protections from discrimination, excessive punishment, unlawful surveillance, and violations of privacy and other freedoms in both public and private sector contexts;

Equal opportunities, including equitable access to education, housing, credit, employment, and other programs; or,

Access to critical resources or services, such as healthcare, financial services, safety, social services, non-deceptive information about goods and services, and government benefits.

A list of examples of automated systems for which these principles should be considered is provided in the Appendix. The Technical Companion, which follows, offers supportive guidance for any person or entity that creates, deploys, or oversees automated systems.

Considered together, the five principles and associated practices of the Blueprint for an AI Bill of Rights form an overlapping set of backstops against potential harms. This purposefully overlapping framework, when taken as a whole, forms a blueprint to help protect the public from harm. The measures taken to realize the vision set forward in this framework should be proportionate with the extent and nature of the harm, or risk of harm, to people's rights, opportunities, and access.

[i] The Executive Order On Advancing Racial Equity and Support for Underserved Communities Through the Federal Government. https://www.whitehouse.gov/briefing-room/presidential-actions/2021/01/20/executive-order-advancing-racial-equity-and-support-for-underserved-communities-through-the-federal-government/

[ii] The White House. Remarks by President Biden on the Supreme Court Decision to Overturn Roe v. Wade. Jun. 24, 2022. https://www.whitehouse.gov/briefing-room/speeches-remarks/2022/06/24/remarks-by-president-biden-on-the-supreme-court-decision-to-overturn-roe-v-wade/

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Blueprint for an AI Bill of Rights | OSTP | The White House

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Among the great challenges posed to democracy today is the use of technology, data, and automated systems in ways that threaten the rights of the American public. Too often, these tools are used to limit our opportunities and prevent our access to critical resources or services. These problems are well documented.

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Cite This Webpage

McAdoo, T. (2024, February 23). How to cite ChatGPT. APA Style. https://apastyle.apa.org/blog/how-to-cite-chatgpt

We, the APA Style team, are not robots. We can all pass a CAPTCHA test, and we know our roles in a Turing test. And, like so many nonrobot human beings this year, we've spent a fair amount of time reading, learning, and thinking about issues related to large language models, artificial intelligence (AI), AI-generated text, and specifically ChatGPT. We've also been gathering opinions and feedback about the use and citation of ChatGPT. Thank you to everyone who has contributed and shared ideas, opinions, research, and feedback.

In this post, I discuss situations where students and researchers use ChatGPT to create text and to facilitate their research, not to write the full text of their paper or manuscript. We know instructors have differing opinions about how or even whether students should use ChatGPT, and we'll be continuing to collect feedback about instructor and student questions. As always, defer to instructor guidelines when writing student papers. For more about guidelines and policies about student and author use of ChatGPT, see the last section of this post.

Quoting or reproducing the text created by ChatGPT in your paper

If you've used ChatGPT or other AI tools in your research, describe how you used the tool in your Method section or in a comparable section of your paper. For literature reviews or other types of essays or response or reaction papers, you might describe how you used the tool in your introduction. In your text, provide the prompt you used and then any portion of the relevant text that was generated in response.

Unfortunately, the results of a ChatGPT "chat" are not retrievable by other readers, and although nonretrievable data or quotations in APA Style papers are usually cited as personal communications, with ChatGPT-generated text there is no person communicating. Quoting ChatGPT's text from a chat session is therefore more like sharing an algorithm's output; thus, credit the author of the algorithm with a reference list entry and the corresponding in-text citation.

When prompted with "Is the left brain right brain divide real or a metaphor?" the ChatGPT-generated text indicated that although the two brain hemispheres are somewhat specialized, "the notation that people can be characterized as 'left-brained' or 'right-brained' is considered to be an oversimplification and a popular myth" (OpenAI, 2023).
Reference
OpenAI. (2023). ChatGPT (Mar 14 version) [Large language model]. https://chat.openai.com/chat

You may also put the full text of long responses from ChatGPT in an appendix of your paper or in online supplemental materials, so readers have access to the exact text that was generated. It is particularly important to document the exact text created because ChatGPT will generate a unique response in each chat session, even if given the same prompt. If you create appendices or supplemental materials, remember that each should be called out at least once in the body of your APA Style paper.

When given a follow-up prompt of "What is a more accurate representation?" the ChatGPT-generated text indicated that "different brain regions work together to support various cognitive processes" and "the functional specialization of different regions can change in response to experience and environmental factors" (OpenAI, 2023; see Appendix A for the full transcript).
Reference
OpenAI. (2023). ChatGPT (Mar 14 version) [Large language model]. https://chat.openai.com/chat

Creating a reference to ChatGPT or other AI models and software

The in-text citations and references above are adapted from the reference template for software in Section 10.10 of the Publication Manual (American Psychological Association, 2020, Chapter 10). Although here we focus on ChatGPT, because these guidelines are based on the software template, they can be adapted to note the use of other large language models (e.g., Bard), algorithms, and similar software.

The reference and in-text citations for ChatGPT are formatted as follows:

OpenAI. (2023). ChatGPT (Mar 14 version) [Large language model]. https://chat.openai.com/chat

Parenthetical citation: (OpenAI, 2023)

Narrative citation: OpenAI (2023)

Let's break that reference down and look at the four elements (author, date, title, and source):

Author: The author of the model is OpenAI.

Date: The date is the year of the version you used. Following the template in Section 10.10, you need to include only the year, not the exact date. The version number provides the specific date information a reader might need.

Title: The name of the model is "ChatGPT," so that serves as the title and is italicized in your reference, as shown in the template. Although OpenAI labels unique iterations (i.e., ChatGPT-3, ChatGPT-4), they are using "ChatGPT" as the general name of the model, with updates identified with version numbers.

The version number is included after the title in parentheses. The format for the version number in ChatGPT references includes the date because that is how OpenAI is labeling the versions. Different large language models or software might use different version numbering; use the version number in the format the author or publisher provides, which may be a numbering system (e.g., Version 2.0) or other methods.

Bracketed text is used in references for additional descriptions when they are needed to help a reader understand what's being cited. References for a number of common sources, such as journal articles and books, do not include bracketed descriptions, but things outside of the typical peer-reviewed system often do. In the case of a reference for ChatGPT, provide the descriptor "Large language model" in square brackets. OpenAI describes ChatGPT-4 as a "large multimodal model," so that description may be provided instead if you are using ChatGPT-4. Later versions and software or models from other companies may need different descriptions, based on how the publishers describe the model. The goal of the bracketed text is to briefly describe the kind of model to your reader.

Source: When the publisher name and the author name are the same, do not repeat the publisher name in the source element of the reference, and move directly to the URL. This is the case for ChatGPT. The URL for ChatGPT is https://chat.openai.com/chat. For other models or products for which you may create a reference, use the URL that links as directly as possible to the source (i.e., the page where you can access the model, not the publisher's homepage).

Other questions about citing ChatGPT

You may have noticed the confidence with which ChatGPT described the ideas of brain lateralization and how the brain operates, without citing any sources. I asked for a list of sources to support those claims and ChatGPT provided five references-four of which I was able to find online. The fifth does not seem to be a real article; the digital object identifier given for that reference belongs to a different article, and I was not able to find any article with the authors, date, title, and source details that ChatGPT provided. Authors using ChatGPT or similar AI tools for research should consider making this scrutiny of the primary sources a standard process. If the sources are real, accurate, and relevant, it may be better to read those original sources to learn from that research and paraphrase or quote from those articles, as applicable, than to use the model's interpretation of them.

We've also received a number of other questions about ChatGPT. Should students be allowed to use it? What guidelines should instructors create for students using AI? Does using AI-generated text constitute plagiarism? Should authors who use ChatGPT credit ChatGPT or OpenAI in their byline? What are the copyright implications?

On these questions, researchers, editors, instructors, and others are actively debating and creating parameters and guidelines. Many of you have sent us feedback, and we encourage you to continue to do so in the comments below. We will also study the policies and procedures being established by instructors, publishers, and academic institutions, with a goal of creating guidelines that reflect the many real-world applications of AI-generated text.

For questions about manuscript byline credit, plagiarism, and related ChatGPT and AI topics, the APA Style team is seeking the recommendations of APA Journals editors. APA Style guidelines based on those recommendations will be posted on this blog and on the APA Style site later this year.

We, the APA Style team humans, appreciate your patience as we navigate these unique challenges and new ways of thinking about how authors, researchers, and students learn, write, and work with new technologies.

Reference

American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.). https://doi.org/10.1037/0000165-000

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This post outlines how to create references for large language model AI tools like ChatGPT and how to present AI-generated text in a paper.

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How do I Cite Generative AI in MLA Style?

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How do I Cite Generative AI in MLA Style?

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The MLA's method for citing sources uses a template of core elements-standardized criteria that writers can use to evaluate sources and create works-cited-list entries based on that evaluation. That new technologies like ChatGPT emerge is a key reason why the MLA has adopted this approach to citation-to give writers flexibility to apply the style when they encounter new types of sources. In what follows, we offer recommendations for citing generative AI, defined as a tool that "can analyze or summarize content from a huge set of information, including web pages, books and other writing available on the internet, and use that data to create original new content" (Weed).

You should

cite a generative AI tool whenever you paraphrase, quote, or incorporate into your own work any content (whether text, image, data, or other) that was created by it

acknowledge all functional uses of the tool (like editing your prose or translating words) in a note, your text, or another suitable location

take care to vet the secondary sources it cites (see example 5 below for more details)

See below for specific examples. And keep in mind: the MLA template of core elements is meant to provide flexibility in citation. So if you find a rationale to modify these recommendations in your own citations, we encourage you to do so. We've opened this post up for commenting, so let us know what you think and how you're using and citing generative AI tools!

Using the MLA Template

Author

We do not recommend treating the AI tool as an author. This recommendation follows the policies developed by various publishers, including the MLA's journal PMLA.

Title of Source

Describe what was generated by the AI tool. This may involve including information about the prompt in the Title of Source element if you have not done so in the text.

Title of Container

Use the Title of Container element to name the AI tool (e.g., ChatGPT).

Version

Name the version of the AI tool as specifically as possible. For example, the examples in this post were developed using ChatGPT 3.5, which assigns a specific date to the version, so the Version element shows this version date.

Publisher

Name the company that made the tool.

Date

Give the date the content was generated.

Location

Give the general URL for the tool.

Example 1: Paraphrasing Text

Passage in Source

Paraphrased in Your Prose

While the green light in The Great Gatsby might be said to chiefly symbolize four main things: optimism, the unattainability of the American dream, greed, and covetousness ("Describe the symbolism"), arguably the most important-the one that ties all four themes together-is greed.

Works-Cited-List Entry

"Describe the symbolism of the green light in the book by F. Scott Fitzgerald" prompt. The Great Gatsby ChatGPT, 13 Feb. version, OpenAI, 8 Mar. 2023, chat.openai.com/chat.

Example 2: Quoting Text

Passage in Source

Quoted in Your Prose

When asked to describe the symbolism of the green light in The Great Gatsby, ChatGPT provided a summary about optimism, the unattainability of the American dream, greed, and covetousness. However, when further prompted to cite the source on which that summary was based, it noted that it lacked "the ability to conduct research or cite sources independently" but that it could "provide a list of scholarly sources related to the symbolism of the green light in The Great Gatsby " ("In 200 words").

Works-Cited-List Entry

"In 200 words, describe the symbolism of the green light in The Great Gatsby" follow-up prompt to list sources. ChatGPT, 13 Feb. version, OpenAI, 9 Mar. 2023, chat.openai.com/chat.

While we've provided fairly detailed descriptions of the prompts above, a more general one (e.g., Symbolism of the green light in The Great Gatsby prompt) could be used, since you are describing something that mimics a conversation, which could have various prompts along the way.

Example 3: Citing Creative Visual Works

If you are incorporating an AI-generated image in your work, you will likely need to create a caption for it following the guidelines in of the MLA Handbook. Use a description of the prompt, followed by the AI tool, version, and date created:

Fig. 1. "Pointillist painting of a sheep in a sunny field of blue flowers" prompt, DALL-E, version 2, OpenAI, 8 Mar. 2023, labs.openai.com/.

You can use this same information if you choose to create a works-cited-list entry instead of including the full citation in the caption (see MLA Handbook, sec. 1.7).

Example 4: Quoting Creative Textual Works

If you ask a generative AI tool to create a work, like a poem, how you cite it will depend on whether you assign a title to it. Let's say, for example, you ask ChatGPT to write a villanelle titled "The Sunflower" that-you guessed it!-describes a sunflower and then quote it in your text. Your works-cited-list entry might look like this:

"The Sunflower" villanelle about a sunflower. ChatGPT, 13 Feb. version, OpenAI, 8 Mar. 2023, chat.openai.com/chat.

If you did not title the work, incorporate part of or all of the first line into the description of the work in the Title of Source element:

"Upon the shore . . ." Shakespearean sonnet about seeing the ocean. ChatGPT, 13 Feb. version, OpenAI, 8 Mar. 2023, chat.openai.com/chat.

For guidance on using descriptions and text from the work itself in the Title of Source element, see the MLA Handbook, 5.28 and 5.29.

Example 5: Citing Secondary Sources Used by an AI Tool

You should also take care to vet the secondary sources cited by a generative AI tool-with the caveat that AI tools do not always cite sources or, when they do, do not always indicate precisely what a given source has contributed. If you cite an AI summary that includes sources and do not go on to consult those sources yourself, we recommend that you acknowledge secondary sources in your work.

For example, let's say that you ask Bing AI to explain the concept of the political unconscious, citing sources, and it provides the following answer:

Let's say that you then decide to quote from the final sentence. You need to click through to the source listed in the note in order to get more information than just a URL for the source. There, you will read the following:

Now, you can treat Oxford Reference as your source since Bing AI was merely a research conduit to the source (see MLA Handbook 5.34 for more information). If for some reason you want to treat a source cited in a generative AI tool as an indirect source-and you know it is, in fact, the source for the information provided by the AI, follow the guidance in section 6.77 of the MLA Handbook.

Note

1. At the time of writing this post, ChatGPT doesn't have a built-in feature to create a unique URL to the conversation. However, an outside tool like the Chrome extension ShareGPT can generate such a link. If you use that type of outside tool, include the unique URL that the tool generates instead of the general URL.

DALL-E allows users to download the AI-generated images they create or generate a publicly-available URL that leads to an image. If you choose to create a shareable link for an image you generate with DALL-E (or other similar AI image generators), include that unique URL that leads to the image instead of the general URL.

Works Cited

MLA Handbook. 9th ed., Modern Language Association of America, 2021. MLA Handbook Plus, 2021, mlahandbookplus.org/.

Weed, Julie. "Can ChatGPT Plan Your Vacation?" The New York Times, 16 Mar. 2023, www.nytimes.com/2023/03/16/travel/chatgpt-artificial-intelligence-travel-vacation.html.

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How do I cite generative AI in MLA style? | MLA Style Center

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The MLA's method for citing sources uses a template of core elements-standardized criteria that writers can use to evaluate sources and create works-cited-list entries based on that evaluation. That new technologies like ChatGPT emerge is a key reason why the MLA has adopted this approach to citation-to give writers flexibility to apply the style when ...

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Shared by Christine (McCrane) Schoettle

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Hamna Aslam Kahn on LinkedIn: ChatGPT has 100 million+ users But 99% are still stuck in beginner... | 77 comments

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ChatGPT has 100 million+ users But 99% are still stuck in beginner mode Go from Zero to Pro with this Ultimate ChatGPT Cheat Sheet: | 77 comments on LinkedIn

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Developing Institutional Level AI Policies and Practices: A Framework

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Developing Institutional Level AI Policies and Practices: A Framework

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WICHE Cooperative for Educational Technologies

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ChatGPT recently turned one and what a wild, first year it has been. Over the last twelve months, institutions have scrambled to not only better understand generative Artificial Intelligence (AI) and its impact on teaching and learning, but also to determine the best ways to provide guardrails and guidance for faculty, staff, and students. Many institutions have struggled to develop institutional-level policies.

In a spring survey administered by WCET, only eight percent of respondents reported that their institution had developed and/or implemented at least one AI-related policy.

The overwhelming majority of institutions, 65 percent, indicated that they have or will be developing policies but have not done so yet. The initial focus of campus policy discussions has centered on academic integrity. And while these discussions are critical, they cannot be the end of the AI conversation on campuses. Institutions must also consider additional areas such as data security and privacy, promotion and tenure practices, professional development planning, and many other policy and practice areas.

WCET has developed an AI policy and practice framework to help institutions identify the policy areas that they need to address and develop policies and guidelines for those areas.

The Framework

In 2023, Cecilia Ka Yuk Chan conducted research on perceptions and implications of text generative AI technologies in order to develop an AI policy for higher education. Based on the findings, she proposed an AI Ecological Education Policy Framework to address "the multifaceted implications of AI integration in university teaching and learning."The WCET framework adapts Chan's framework and categorizes institutional AI policy needs in three areas:

Undergirding all three areas of our policy and practice framework is the ethical and responsible use of AI. All policy decisions at colleges and universities should be grounded in ethical considerations of AI. Doing so ensures the most effective and responsible use of, and teaching about, these technologies. And it is often institutional administrators who lead this work. Not developing and implementing AI policies within the context of ethical considerations opens up the institution - and thus its leaders - to, at best, inefficient use of resources that often include funds from taxpayers, and, at worst, serious breaches of privacy, security, transparency, and equity.

Governance

This dimension emphasizes the governance considerations surrounding AI usage in higher education. Governance refers to the senior management at an institution, including such positions and roles as Chancellor/President, Chief Academic Officer, Chief Information Office, Vice President for Student Services, VP for Institutional Research/Effectiveness, and others depending on the campus context. Governance may also encompass managers such as Deans and Chairs of academic discipline units. Members of senior leadership will be the initiators for the Governance dimension of the framework. As they hold decision-making authority, they should set the tone for effective and innovative AI use across campus and ensure that all AI policies and practices support the mission and goals of the institution and foster an equitable and inclusive environment.

Here we highlight six areas of responsibility:

Data governance refers to an institution's policies and processes that ensure that effective and responsible management, including security, exists throughout the complete lifecycle of the data, and data controls are implemented that support business objectives.

Evaluation of AI use across the institution.

Campus administrators should also oversee (working in concert with such units as Institutional Research and Information Technology) the evaluation of the effectiveness of AI in every use. The information and data collected should be harnessed for continuous improvement of AI planning, policies, and practices. By regularly collecting feedback from all users, including students, colleges and universities can make informed decisions about how to improve AI implementation. Evaluating the effectiveness of AI tools in enhancing learning outcomes is also vital to determine their value and make adjustments as needed.

Promoting and monitoring faculty and staff usage of AI, including research.

Where appropriate, institutional governance should work to encourage campus personnel, including faculty, and students to utilize AI technologies. It may be important to continue to emphasize that, even in AI use, faculty remain centered as the subject matter experts and that AI technologies can support their ongoing role as SMEs. Along with this, though, comes the responsibility of monitoring that use - including while conducting research - to ensure that it is ethical, effective, and appropriate.

Ensuring equitable access to AI technologies is crucial for fostering an inclusive learning environment. Universities should work to provide the appropriate technologies and support to all students, faculty, administrators, and staff, regardless of their background or access to technology. By promoting equal access to AI technologies, universities can help level the playing field and ensure that all students and staff have the opportunity to benefit from the advantages offered by AI integration. Not doing so widens the digital divide.

Leaders at institutions will need to consider how intellectual property, including research, course materials, and student-produced work, is defined and, where needed, protected when created using AI, either fully or in part. However, these policies must be developed in accordance with U.S. and international copyright laws (which are scrambling to keep up with the new technologies) and, thus, likely should involve collaboration with the institution's legal counsel.

AI use and promotion, tenure, & re-appointment practices.

Institutional leaders should also consider how works produced using AI are considered in promotion, tenure, and reappointment of faculty. These processes can be used to reward and incentivize innovative research and teaching, but they also should guard against plagiarism of content in portfolios and dossiers.

Operations

This dimension assists in the understanding and implementation of AI across the institution and includes staff in key areas such as Academic Affairs, Information Technology, and Centers for Teaching & Learning Effectiveness/Excellence. Here we highlight three areas of responsibility:

Professional development (training & support).

Training and support on AI technologies should be offered to all who use or may use AI, including administrators, staff, faculty, and students. Effective training and support can go a long way to mitigate and alleviate often extensive (and legitimate) concerns about integrating AI into work, instruction, and learning. Investing in training, support, and resources can help educators, their students, and others feel more confident and capable in navigating the complexities and ever-changing landscape of AI technologies.

Developing and maintaining infrastructure for AI.

The responsibility for developing and maintaining an institution's AI infrastructure will likely fall primarily to Information Technology in consultation with other units to determine needs and evaluate costs and efficacy of tools.

Review and recommend AI implementation to improve operational practices.

All operational units should be engaged in scanning the landscape of AI to review and recommend platforms and tools that can enhance the efficiency and effectiveness of the institution's operations, whether for student services and support, instruction and learning, admissions, recruitment and marketing, staff workflows, and resource planning, among others.

Pedagogy

This dimension emphasizes the practical implementation of AI to support instruction and learning in the classroom. Faculty are the initiators of this dimension, working closely with those in Operations to actualize policy and planning from the Governance level while always considering ethical dimensions. Instructors are ultimately responsible for designing and implementing curricula, activities, and assessments that utilize AI technologies. They will need to gain some expertise to determine how AI can best support and enhance students' learning experiences while assisting learners in understanding the implications for academic integrity. Here we highlight seven areas of responsibility:

Generative AI has raised concerns that students will misuse technologies to plagiarize. The more clear and consistent policies are, the more likely students will understand and follow them, reducing the chances of misuse. Policies and guidelines may range from those that ban the use of AI in the classroom altogether, to those that allow and even encourage use. Policies regarding appropriate attribution and acknowledgment of AI technologies used to create assignments and other products of learning are crucial as well. There may be an institutional policy regarding this; if not, faculty should develop their own.

Assessing the effectiveness of learning is a hallmark of education; however, it has been historically fraught and intertwined with ensuring academic integrity. The increasing ubiquity of Generative AI has further complicated these practices, necessitating reconsideration of assessment methods to balance the benefits of AI with the need to maintain academic integrity.

Clear communication to students regarding AI expectations.

Faculty should clearly state in the syllabus how students will be expected to use AI in the class and should also verbally communicate those expectations on the first day of class. Being clear about how a faculty member will leverage AI in the course allows students to make informed decisions about whether to stay in the course.

Developing student AI competencies and skills/workforce preparation.

The increasing ubiquity of Generative AI in the workplace calls for a new digital literacy. This need makes it imperative for institutions to prepare students for this complex technological working landscape, equipping them with skills and knowledge to successfully navigate not only the current landscape, but a rapidly evolving one as well. Therefore, not only should faculty teach at least basic skills students need to integrate AI into their work, but also evaluate when it is appropriate to use AI, how to evaluate the tools, and to understand their role in professional settings.

Instructors should make students aware of the possibility of discrimination being programmed into AI, since fallible humans must be a part of the process to develop inputs used (with the recognition that humans may themselves perpetuate discriminatory practices through the data).

The use of AI to augment or even replace certain instructional and related support practices, such as information delivery, responding to questions, assessment, tutoring, and personalized learning and guidance, could have a significant impact on norms and expectations around interactions between students and instructors. Institutions should ensure that they address the extent to which faculty are allowed to automate instruction through the use of artificial intelligence and the aspects of instruction that can leverage artificial intelligence. For most institutions, this will mean revising existing policies on regular and substantive interaction.

It is important to consider the ways in which some generative AI tools might not be accessible for all students with disabilities and learning challenges in general, while others may support accessibility, including for users of assistive technology. All learners using assistive technology must be able to meaningfully engage and independently interact with AI interfaces and outputs.

Putting aside fears of AI surpassing human intelligence and achieving singularity - a hypothetical future point in time at which technological growth becomes uncontrollable and irreversible - legitimate concerns remain a year after the appearance of ChatGPT, including in education.

Issues surrounding academic integrity, the quality of knowledge produced by AI tools, the replacement of instructors by AI, mitigating a new "digital divide," and how to prepare students for an AI-infused workforce, among others, are real.

Institutions continue to grapple with security and privacy, equity and access, and other challenges that these technologies present.

Upcoming WCET Resource Addressing AI in Higher Ed

In his Substack AI + Education = Simplified, Lance Eaton suggests that the all-too-common reinvention of wheels in higher education - "the thing that contributes to institutions being so slow" - is stymieing effective use of AI in the sector. WCET is committed to addressing this challenge by bringing institutions together to share knowledge and providing resources to support the community. One of the resources that we are most excited about is the development of our AI Policy and Practice Toolkit which we will release later this month. This WCET members-only resource builds out our AI Policy and Practice Framework and includes sample policies and/or guidelines for each of the outlined areas.

If your institution is not a WCET member, you can join now. WCET is offering a discounted membership rate through the end of the year of 35% off new memberships in celebration of our 35 thanniversary. You can find more information about it here. And if you are wondering if your institution is a member of WCET, you can access a list of members here.

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Developing Institutional Level AI Policies and Practices: A Framework

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ChatGPT recently turned one and what a wild, first year it has been. Over the last twelve months, institutions have scrambled to not only better understand generative Artificial Intelligence (AI) and its impact on teaching and learning, but also to determine the best ways to provide guardrails and guidance for faculty, staff, and students.

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Shared by Christine (McCrane) Schoettle on Linked In

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Be specific when entering the topic e.g., enter "Graduate Level Instructional Design" instead of just entering "Instructional Design" or "Introduction to Inorganic Chemistry" instead of just entering "Inorganic Chemistry".

To save your results click on the blue Copy icon and paste into your word processor.

Click again on Generate if you are not satisfied with what's produced. Note the previous result will be lost when you do this.

Sometimes results may not format properly until all questions are generated.

Refresh your browser before entering a new topic.

ALWAYS double check the generated questions and answers as AI systems can sometimes produce erratic results.

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AI Teaching Assistant Pro | Contact North | Contact Nord

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ChatGPT is your AI chatbot for everyday use. Chat with the most advanced AI to explore ideas, solve problems, and learn faster.

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AI and ChatGPT Resources for Higher Education

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AI and ChatGPT Resources for Higher Education

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AI and ChatGPT Resources for Higher Education - Alchemy

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A collection of AI and ChatGPT resources for teaching and learning in higher education that were shared during our AI webinar series.

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Introducing Gemini: our largest and most capable AI model

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Introducing Gemini: our largest and most capable AI model

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Universal and Multimodal AI

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A note from Google and Alphabet CEO Sundar Pichai:
Every technology shift is an opportunity to advance scientific discovery, accelerate human progress, and improve lives. I believe the transition we are seeing right now with AI will be the most profound in our lifetimes, far bigger than the shift to mobile or to the web before it. AI has the potential to create opportunities - from the everyday to the extraordinary - for people everywhere. It will bring new waves of innovation and economic progress and drive knowledge, learning, creativity and productivity on a scale we haven't seen before.

That's what excites me: the chance to make AI helpful for everyone, everywhere in the world.

Nearly eight years into our journey as an AI-first company, the pace of progress is only accelerating: Millions of people are now using generative AI across our products to do things they couldn't even a year ago, from finding answers to more complex questions to using new tools to collaborate and create. At the same time, developers are using our models and infrastructure to build new generative AI applications, and startups and enterprises around the world are growing with our AI tools.

This is incredible momentum, and yet, we're only beginning to scratch the surface of what's possible.

We're approaching this work boldly and responsibly. That means being ambitious in our research and pursuing the capabilities that will bring enormous benefits to people and society, while building in safeguards and working collaboratively with governments and experts to address risks as AI becomes more capable. And we continue to invest in the very best tools, foundation models and infrastructure and bring them to our products and to others, guided by our AI Principles.

Now, we're taking the next step on our journey with Gemini, our most capable and general model yet, with state-of-the-art performance across many leading benchmarks. Our first version, Gemini 1.0, is optimized for different sizes: Ultra, Pro and Nano. These are the first models of the Gemini era and the first realization of the vision we had when we formed Google DeepMind earlier this year. This new era of models represents one of the biggest science and engineering efforts we've undertaken as a company. I'm genuinely excited for what's ahead, and for the opportunities Gemini will unlock for people everywhere.

- Sundar

Introducing Gemini
By Demis Hassabis, CEO and Co-Founder of Google DeepMind, on behalf of the Gemini team
AI has been the focus of my life's work, as for many of my research colleagues. Ever since programming AI for computer games as a teenager, and throughout my years as a neuroscience researcher trying to understand the workings of the brain, I've always believed that if we could build smarter machines, we could harness them to benefit humanity in incredible ways.

This promise of a world responsibly empowered by AI continues to drive our work at Google DeepMind. For a long time, we've wanted to build a new generation of AI models, inspired by the way people understand and interact with the world. AI that feels less like a smart piece of software and more like something useful and intuitive - an expert helper or assistant.

Today, we're a step closer to this vision as we introduce Gemini, the most capable and general model we've ever built.

Gemini is the result of large-scale collaborative efforts by teams across Google, including our colleagues at Google Research. It was built from the ground up to be multimodal, which means it can generalize and seamlessly understand, operate across and combine different types of information including text, code, audio, image and video.

Gemini is also our most flexible model yet - able to efficiently run on everything from data centers to mobile devices. Its state-of-the-art capabilities will significantly enhance the way developers and enterprise customers build and scale with AI.

We've optimized Gemini 1.0, our first version, for three different sizes:

Gemini Ultra - our largest and most capable model for highly complex tasks.

Gemini Pro - our best model for scaling across a wide range of tasks.

Gemini Nano - our most efficient model for on-device tasks.

State-of-the-art performance

We've been rigorously testing our Gemini models and evaluating their performance on a wide variety of tasks. From natural image, audio and video understanding to mathematical reasoning, Gemini Ultra's performance exceeds current state-of-the-art results on 30 of the 32 widely-used academic benchmarks used in large language model (LLM) research and development.

With a score of 90.0%, Gemini Ultra is the first model to outperform human experts on MMLU (massive multitask language understanding), which uses a combination of 57 subjects such as math, physics, history, law, medicine and ethics for testing both world knowledge and problem-solving abilities.

Our new benchmark approach to MMLU enables Gemini to use its reasoning capabilities to think more carefully before answering difficult questions, leading to significant improvements over just using its first impression.

Gemini Ultra also achieves a state-of-the-art score of 59.4% on the new MMMU benchmark, which consists of multimodal tasks spanning different domains requiring deliberate reasoning.

With the image benchmarks we tested, Gemini Ultra outperformed previous state-of-the-art models, without assistance from optical character recognition (OCR) systems that extract text from images for further processing. These benchmarks highlight Gemini's native multimodality and indicate early signs of Gemini's more complex reasoning abilities.

See more details in our Gemini technical report.

Next-generation capabilities

Until now, the standard approach to creating multimodal models involved training separate components for different modalities and then stitching them together to roughly mimic some of this functionality. These models can sometimes be good at performing certain tasks, like describing images, but struggle with more conceptual and complex reasoning.

We designed Gemini to be natively multimodal, pre-trained from the start on different modalities. Then we fine-tuned it with additional multimodal data to further refine its effectiveness. This helps Gemini seamlessly understand and reason about all kinds of inputs from the ground up, far better than existing multimodal models - and its capabilities are state of the art in nearly every domain.

Learn more about Gemini's capabilities and see how it works.

Sophisticated reasoning

Gemini 1.0's sophisticated multimodal reasoning capabilities can help make sense of complex written and visual information. This makes it uniquely skilled at uncovering knowledge that can be difficult to discern amid vast amounts of data.

Its remarkable ability to extract insights from hundreds of thousands of documents through reading, filtering and understanding information will help deliver new breakthroughs at digital speeds in many fields from science to finance.

Understanding text, images, audio and more

Gemini 1.0 was trained to recognize and understand text, images, audio and more at the same time, so it better understands nuanced information and can answer questions relating to complicated topics. This makes it especially good at explaining reasoning in complex subjects like math and physics.

Advanced coding

Our first version of Gemini can understand, explain and generate high-quality code in the world's most popular programming languages, like Python, Java, C++, and Go. Its ability to work across languages and reason about complex information makes it one of the leading foundation models for coding in the world.

Gemini Ultra excels in several coding benchmarks, including HumanEval, an important industry-standard for evaluating performance on coding tasks, and Natural2Code, our internal held-out dataset, which uses author-generated sources instead of web-based information.

Gemini can also be used as the engine for more advanced coding systems. Two years ago we presented AlphaCode, the first AI code generation system to reach a competitive level of performance in programming competitions.

Using a specialized version of Gemini, we created a more advanced code generation system, AlphaCode 2, which excels at solving competitive programming problems that go beyond coding to involve complex math and theoretical computer science.

When evaluated on the same platform as the original AlphaCode, AlphaCode 2 shows massive improvements, solving nearly twice as many problems, and we estimate that it performs better than 85% of competition participants - up from nearly 50% for AlphaCode. When programmers collaborate with AlphaCode 2 by defining certain properties for the code samples to follow, it performs even better.

We're excited for programmers to increasingly use highly capable AI models as collaborative tools that can help them reason about the problems, propose code designs and assist with implementation - so they can release apps and design better services, faster.

See more details in our AlphaCode 2 technical report.

More reliable, scalable and efficient

We trained Gemini 1.0 at scale on our AI-optimized infrastructure using Google's in-house designed Tensor Processing Units (TPUs) v4 and v5e. And we designed it to be our most reliable and scalable model to train, and our most efficient to serve.

On TPUs, Gemini runs significantly faster than earlier, smaller and less-capable models. These custom-designed AI accelerators have been at the heart of Google's AI-powered products that serve billions of users like Search, YouTube, Gmail, Google Maps, Google Play and Android. They've also enabled companies around the world to train large-scale AI models cost-efficiently.

Today, we're announcing the most powerful, efficient and scalable TPU system to date, Cloud TPU v5p, designed for training cutting-edge AI models. This next generation TPU will accelerate Gemini's development and help developers and enterprise customers train large-scale generative AI models faster, allowing new products and capabilities to reach customers sooner.

Built with responsibility and safety at the core

At Google, we're committed to advancing bold and responsible AI in everything we do. Building upon Google's AI Principles and the robust safety policies across our products, we're adding new protections to account for Gemini's multimodal capabilities. At each stage of development, we're considering potential risks and working to test and mitigate them.

Gemini has the most comprehensive safety evaluations of any Google AI model to date, including for bias and toxicity. We've conducted novel research into potential risk areas like cyber-offense, persuasion and autonomy, and have applied Google Research's best-in-class adversarial testing techniques to help identify critical safety issues in advance of Gemini's deployment.

To identify blindspots in our internal evaluation approach, we're working with a diverse group of external experts and partners to stress-test our models across a range of issues.

To diagnose content safety issues during Gemini's training phases and ensure its output follows our policies, we're using benchmarks such as Real Toxicity Prompts, a set of 100,000 prompts with varying degrees of toxicity pulled from the web, developed by experts at the Allen Institute for AI. Further details on this work are coming soon.

To limit harm, we built dedicated safety classifiers to identify, label and sort out content involving violence or negative stereotypes, for example. Combined with robust filters, this layered approach is designed to make Gemini safer and more inclusive for everyone. Additionally, we're continuing to address known challenges for models such as factuality, grounding, attribution and corroboration.

Responsibility and safety will always be central to the development and deployment of our models. This is a long-term commitment that requires building collaboratively, so we're partnering with the industry and broader ecosystem on defining best practices and setting safety and security benchmarks through organizations like MLCommons, the Frontier Model Forum and its AI Safety Fund, and our Secure AI Framework (SAIF), which was designed to help mitigate security risks specific to AI systems across the public and private sectors. We'll continue partnering with researchers, governments and civil society groups around the world as we develop Gemini.

Making Gemini available to the world

Gemini 1.0 is now rolling out across a range of products and platforms:

Gemini Pro in Google products

We're bringing Gemini to billions of people through Google products.

Starting today, Bard will use a fine-tuned version of Gemini Pro for more advanced reasoning, planning, understanding and more. This is the biggest upgrade to Bard since it launched. It will be available in English in more than 170 countries and territories, and we plan to expand to different modalities and support new languages and locations in the near future.

We're also bringing Gemini to Pixel. Pixel 8 Pro is the first smartphone engineered to run Gemini Nano, which is powering new features like Summarize in the Recorder app and rolling out in Smart Reply in Gboard, starting with WhatsApp - with more messaging apps coming next year.

In the coming months, Gemini will be available in more of our products and services like Search, Ads, Chrome and Duet AI.

We're already starting to experiment with Gemini in Search, where it's making our Search Generative Experience (SGE) faster for users, with a 40% reduction in latency in English in the U.S., alongside improvements in quality.

Building with Gemini

Starting on December 13, developers and enterprise customers can access Gemini Pro via the Gemini API in Google AI Studio or Google Cloud Vertex AI.

Google AI Studio is a free, web-based developer tool to prototype and launch apps quickly with an API key. When it's time for a fully-managed AI platform, Vertex AI allows customization of Gemini with full data control and benefits from additional Google Cloud features for enterprise security, safety, privacy and data governance and compliance.

Android developers will also be able to build with Gemini Nano, our most efficient model for on-device tasks, via AICore, a new system capability available in Android 14, starting on Pixel 8 Pro devices. Sign up for an early preview of AICore.

Gemini Ultra coming soon

For Gemini Ultra, we're currently completing extensive trust and safety checks, including red-teaming by trusted external parties, and further refining the model using fine-tuning and reinforcement learning from human feedback (RLHF) before making it broadly available.

As part of this process, we'll make Gemini Ultra available to select customers, developers, partners and safety and responsibility experts for early experimentation and feedback before rolling it out to developers and enterprise customers early next year.

Early next year, we'll also launch Bard Advanced, a new, cutting-edge AI experience that gives you access to our best models and capabilities, starting with Gemini Ultra.

The Gemini era: enabling a future of innovation

This is a significant milestone in the development of AI, and the start of a new era for us at Google as we continue to rapidly innovate and responsibly advance the capabilities of our models.

We've made great progress on Gemini so far and we're working hard to further extend its capabilities for future versions, including advances in planning and memory, and increasing the context window for processing even more information to give better responses.

We're excited by the amazing possibilities of a world responsibly empowered by AI - a future of innovation that will enhance creativity, extend knowledge, advance science and transform the way billions of people live and work around the world.

More about Gemini

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Introducing Gemini: our largest and most capable AI model

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Gemini is our most capable and general model, built to be multimodal and optimized for three different sizes: Ultra, Pro and Nano.

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The Paradigm Shift: How Artificial Intelligence is Reshaping the World as "Things Will Never Be the Same"

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The Paradigm Shift: How Artificial Intelligence is Reshaping the World as "Things Will Never Be the Same"

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The Paradigm Shift: How Artificial Intelligence is Reshaping the World as “Things Will Never Be the Same”

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Change is an inherent part of the human experience, bringing with it a myriad of emotions and transformations. “Things Will Never Be the Same” captures the essence of this universal truth.

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You can use your Holy Family Account to sign up

Uses Chat GPT 4

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Intelligent search from Bing makes it easier to quickly find what you're looking for and rewards you.

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Multiple Chatbots

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Poe lets you ask questions, get instant answers, and have back-and-forth conversations with AI. Gives access to GPT-4, gpt-3.5-turbo, Claude from Anthropic, and a variety of other bots.

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Using ChatGPT to Support Student-Led Inquiry

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Using ChatGPT to Support Student-Led Inquiry

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Positioning AI as a mentor or simulator of real-world scenarios can promote student inquiry and aid project-based learning.

Picture students passionately debating about an original inquiry but hitting a roadblock. Disagreements arise, and learners crave solutions.

Next, imagine that students turn to ChatGPT to overcome these obstacles in their thinking. How can ChatGPT be used as such a tool? I'd like to share three ways to harness ChatGPT to bolster student-led inquiry.

1. SIMULATing SCENARIOS

Showing students that ChatGPT can simulate real-world scenarios offers them another way to problem-solve. Students can use strategies and ideas presented by ChatGPT's simulation and apply them to their learning context.

For example, this prompt for ChatGPT is crafted in a way that presents a student project (such as making a newspaper, book, or other product). I share the educational framework I'm using with my students and write, in detail, about what decisions the students get to make within the project or inquiry. I position students as practitioners (e.g., scientists, editors, writers, historians), and I give details about the problem they are facing. I also share important role details, such as how another student may be in a position of authority (the lead historian for the project or the manager of the science lab, for example).

I then share that we want to know what real-life professionals would do when there is disagreement. "Could you please simulate a scenario where two such professionals disagree, and make your response as realistic as possible?" I ask ChatGPT, sharing that students would like to use some of the strategies in the simulation to problem-solve in their project.

The framing of this simulation prompt is applicable across subjects; for example, I similarly managed to prompt ChatGPT to simulate a disagreement about chemical application for a science project.

2. EMPOWERing CRITICAL THINKING

I like to involve students in this process of thinking about how best to gather information and strategies to address problems within their learning context. Scaffolding the process positions students to independently harness ChatGPT to problem-solve. I like to use the " I do, we do, you do " teaching strategy.

For example, I brainstorm one or two problems that may arise from an inquiry in front of the class (I do). Then, students brainstorm possible problems that may arise from a project or inquiry (we do). I add onto their ideas, and we might end up with something like this: Student A strongly disagrees that student B should use certain chemicals in a science experiment.

I show students the prompt and break down its specifics or even distribute the prompt in printed form for group analysis. And then I demonstrate how to use ChatGPT to simulate a scenario. As a class, we select one of the problems that students brainstormed and use ChatGPT to simulate a scenario. After we practice prompting, they try to generate their own simulations and scenarios using new prompts. Eventually, students share their solutions on a Padlet or shared Word document.

ChatGPT may generate inaccuracies, which become learning opportunities for both the students and the teacher. Have a section on Padlet or a shared Word document for students to share possible errors that may need further examination. Here are some questions that educators can use to guide this type of examination: How do we know that ChatGPT gave us a faulty response? Why did these issues surface? Can they be rectified?

When students read ChatGPT's responses and discern inaccuracies, they further strengthen their critical reading skills and learn that ChatGPT is a tool, not a replacement for humans.

3. PROGRAMming AN EXPERT

Visits from experts sustain inquiry in project-based learning (PBL). Experts provide students with firsthand insights on a topic of study. When prompted, ChatGPT can give students an expert experience as well.

For example, students can prompt ChatGPT to become a mentor. Students can then type their questions into ChatGPT and read answers from the simulated mentor. Here is a prompt that I used successfully.

To frame this type of prompt, I ask ChatGPT to become a historian, literature professor, biologist, author, chemist, or other mentor. I tell ChatGPT that students will ask it questions and that it should be as unbiased as possible, making sure to answer factually. I then say that students will be focusing on a given topic-for example, World War II, Jonathan Swift's A Modest Proposal, or Newton's gravity law.

I tell it that its task is to be an expert about the topic so that students can better understand the objective or goal of the lesson.

SupportING STUDENTS' AI EXPLORATION

When used strategically to provide solutions, mend disagreements, and enhance students' critical thinking skills as they work through different simulations, ChatGPT can become a tool in the classroom.

By practicing the strategies above, students become empowered to make decisions during student-led inquiries that are aided by AI. And in doing so, they learn how to position ChatGPT as a mentor that is supportive of their ideas and questions.

AI is an innovation that will continue to evolve in education. By empowering students with these strategies, we can help them learn more about AI's vastness and capabilities. AI is more than a fact generator. Students can use skills gained from this article (and their other AI pieces as well) to experiment and explore possibilities within this ever-evolving AI landscape.

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Using ChatGPT to Support Student-Led Inquiry

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Positioning AI as a mentor or simulator of real-world scenarios can promote student inquiry and aid project-based learning.

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Notion AI is available to all users. You can prompt AI using your space bar, highlighting text and selecting "Ask AI", or via slash commands. You must take action for AI to activate.

It will not generate content or send requests to our AI providers unless you perform one of the actions above. You are in control.

Notion certainly isn't the only AI-powered tool out there. But here's what makes Notion AI special:

Artificial intelligence becomes more useful when it's integrated across the place where you're already doing your work, storing notes & docs, and collaborating with others. No need to switch back and forth between your notes and a separate AI-powered tool.

Compared to other tools, Notion has a unique and highly flexible drag-and-drop text editor, where any AI-generated content can be easily rearranged and transformed. Over time, Notion AI will be able to take advantage of even more Notion features.

Today, Notion AI has many uses across your notes and docs. But Notion can do a lot more than just notes - we'll continue to expand Notion AI's capabilities to help with project management and your team knowledge base in the future. This is just the beginning!

We follow our standard data protection practices to keep your data encrypted and private.

The Notion AI Writing Suite will not use your data to train our models unless you opt in to a request to share your data. Any information used to power Notion AI will be shared with our partners for the sole purpose of providing you with the Notion AI features.

For more information, see the Notion AI Supplementary Terms.

When you purchase the Notion AI add-on, you're enabling everyone in your workspace (except for guests) to have unlimited use of Notion AI.

To ensure optimal performance and fair usage across all Notion AI users, your access to AI features can be reduced depending on your usage.

Everyone can use Notion AI for free, so you can test its capabilities before purchasing the add-on subscription. The number of complimentary AI responses increases with the more members you have, and is shared among the entire workspace.

Once you've used all your complimentary AI responses, you will see a message that a Notion AI add-on subscription is necessary to continue using AI features. You'll need to be a workspace owner, and on desktop, to add Notion AI to your plan.

Notion AI is available to all users. You can prompt AI using your space bar, highlighting text and selecting "Ask AI", or via slash commands. You must take action for AI to activate.

It will not generate content or send requests to our AI providers unless you perform one of the actions above. You are in control.

Notion certainly isn't the only AI-powered tool out there. But here's what makes Notion AI special:

Artificial intelligence becomes more useful when it's integrated across the place where you're already doing your work, storing notes & docs, and collaborating with others. No need to switch back and forth between your notes and a separate AI-powered tool.

Compared to other tools, Notion has a unique and highly flexible drag-and-drop text editor, where any AI-generated content can be easily rearranged and transformed. Over time, Notion AI will be able to take advantage of even more Notion features.

Today, Notion AI has many uses across your notes and docs. But Notion can do a lot more than just notes - we'll continue to expand Notion AI's capabilities to help with project management and your team knowledge base in the future. This is just the beginning!

We follow our standard data protection practices to keep your data encrypted and private.

The Notion AI Writing Suite will not use your data to train our models unless you opt in to a request to share your data. Any information used to power Notion AI will be shared with our partners for the sole purpose of providing you with the Notion AI features.

For more information, see the Notion AI Supplementary Terms.

When you purchase the Notion AI add-on, you're enabling everyone in your workspace (except for guests) to have unlimited use of Notion AI.

To ensure optimal performance and fair usage across all Notion AI users, your access to AI features can be reduced depending on your usage.

Everyone can use Notion AI for free, so you can test its capabilities before purchasing the add-on subscription. The number of complimentary AI responses increases with the more members you have, and is shared among the entire workspace.

Once you've used all your complimentary AI responses, you will see a message that a Notion AI add-on subscription is necessary to continue using AI features. You'll need to be a workspace owner, and on desktop, to add Notion AI to your plan.

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Get answers to your urgent questions, using info across your wiki, projects & docs.

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AI Copilot

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AI Copilot Introducing Built for Enterprise Explore GENIE AI Our algorithms and methodologies explore the depths of AI potential, constructing groundbreaking solutions that will challenge the status quo. Only the bold and audacious will prosper in this enigmatic domain. ALBIS Meet ALBIS!

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AI in Higher Ed -Wyndham 2023 Q&A answers 

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AI in Higher Ed -Wyndham 2023 Q&A answers 

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Danny Liu

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Curated links about artificial intelligence and education relevant for USYD and Australian higher education AI-enabled tools relevant to education Text-based Media Code and data science Research Prompting Articles Relationships and inclusion Student responses Shifting on assessment and teaching...

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Advancing opportunities for AI in higher education

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Advancing opportunities for AI in higher education

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Last month, LinkedIn released their Future of Work Report and found that new AI tools have the potential to lighten workloads and help professionals, like educators, focus on the most critical parts of their job. For educators, this means that generative AI likely can augment tasks such as lesson planning while giving time back for their top specialized skills in areas like differentiated instruction. For students, this report demonstrated the potential impact on the future of work where the skills required for many jobs have changed by 25% since 2015, with that number expected to reach at least 65% by 2030.

Today, we're excited to share opportunities that will help support educators and prepare students for the future. We're introducing a new course on Bing Chat for educators, the AI Classroom University Hackathon, and innovative work from Anthology, University of Michigan, the eight UGC-funded universities in Hong Kong, and Linköping University.

Learning how to use Bing Chat and Bing Chat Enterprise

Bing Chat and Bing Chat Enterprise for faculty are AI-powered chat solutions that leverage powerful AI models from OpenAI, offer links to content sources, provide up-to-date answers, and allow you to ask questions about what you're viewing in the browser whether it's a webpage or PDF. In just the last few weeks, new features have been announced including adding support for DALL.E 3 and creating Content Credentials for AI-generated images.

To help educators build AI literacy and make the most of these capabilities, we are introducing a free new module on Microsoft Learn: Enhancing teaching and learning with Bing Chat. It's designed to guide educators through available features, learn how to create and iterate on prompts, and use their expertise to evaluate responses for overall quality and credibility. The module takes approximately an hour, is aligned to ISTE and UNESCO standards for educators, and builds on our introductory AI course for educators.

Announcing the AI Classroom University Hackathon

As the demand for AI and data related skills in the workforce grows, we are announcing a new opportunity for actively enrolled university undergraduates and graduate students to equip them with relevant experience. The AI Classroom University Hackathon is a global virtual challenge for students to reimagine the future of education using Azure AI and Azure Databases. Hackathon participants will be asked to solve for a real-world need in the future of learning and education, earning bonus points for their integration of Microsoft's Responsible AI Principles.

The hackathon will run virtually from October 26th to December 5th, 2023, with $11,000 in prizes and opportunities to receive mentorship from Microsoft experts. To register for the hackathon, students are invited to sign up on Devpost where they'll have access to the latest Microsoft Learn Resources on AI & Data.
As the demand for AI and data related skills in the workforce grows, students need opportunities to be equipped with relevant experience.
There will also be a launch event for Tampa area students in collaboration with the University of South Florida (USF) that will include AI-related activities, hackathon support, and a panel discussion. A leading public research university that is committed to advancing AI education and research, USF's vision is to create an AI-forward campus where students are equipped with the skills and confidence to solve problems with AI. "At USF we want to empower our students to design the future they wish to see and are thrilled to partner with Microsoft on the skilling and technologies that will build the next generation of intelligent applications," said Sidney Fernandes, Vice President and CIO at the University of South Florida.

Collaborating with Anthology to give time back to instructors

We are thrilled to share our recent collaboration with Anthology who are pioneering the use of generative AI capabilities, powered by the Azure OpenAI Service, in their flagship learning management system, Blackboard Learn. They are committed not only to assessing the opportunity for AI across their vast stack of EdTech solutions, but also to integrating it thoughtfully and responsibly. Anthology shares our commitment to responsible AI and has worked closely with their clients to build their Trustworthy AI Approach.

Knowing that instructors are central to the student experience, and how spread thin they can be, Anthology focused first on saving them time so they can spend more time with students and less time managing content. The AI Design Assistant in Blackboard Learn streamlines the course building process and gives experts a head start on creating engaging courses while keeping instructors in control. Using simple inputs, it can recommend course structures with titles and descriptions, standardize the rubric creation process, generate a diverse set of questions based on a document's content, and provide royalty-free image options.

We can't wait to hear your feedback and are encouraged by early excitement from the Anthology community. Hear more about how the AI Design Assistant is supporting instructors in the video below from Dr. Misty Cobb, Senior Product Manager at Anthology and Dr. Szymon Machajewski, Associate Director of Academic Technology & Instructional Innovation at the University of Illinois Chicago.

Bringing generative AI to the University of Michigan

At the University of Michigan, students, faculty, and staff can now tap into the power and possibilities of generative AI, thanks to a custom platform developed by U-M's Information and Technology Services (ITS) in collaboration with Microsoft.

"At the core of our GenAI services is our commitment to provide tools and technologies that enhance, support, and augment humanity. We are confident that our community will utilize these services to make a positive difference in the world," said Ravi Pendse, Vice President for Information Technology and Chief Information Officer at U-M. "The platform we built with Microsoft provides our users with AI tools that firmly emphasize the importance of equity, accessibility, and privacy."

Whether it's enhancing teaching and learning, acting as a resource for students, or serving as a catalyst for researchers, the platform offers options for all users. This is due to ITS' thoughtful and intentional efforts to provide access to everyone, which included prioritizing user-centered design and incorporating accessibility in each stage of development to work towards compliance with international standards. ITS AI Services are already receiving positive attention from their community and they are continuing to share resources and guidance for them. Read more and stay tuned for updates on the University of Michigan's generative AI services which integrate the Azure OpenAI Service.

Transforming the future of Hong Kong's teaching and learning models

Microsoft Hong Kong is collaborating with the eight UGC-funded universities to drive the development of AI adoption and enable the use of generative AI through the Azure OpenAI Service. Collectively, their goal is to harness the potential of the latest AI models in developing their own solutions while benefiting from Microsoft's comprehensive end-to-end enterprise-grade security measures, safeguarding sensitive research and ensuring data privacy within the academic environment.

They plan to make the technology available for professors, researchers, and students across the institutions to revolutionize the teaching and learning model and equip students with the necessary skills to utilize AI in their academic and professional careers. Stay tuned for future updates as they explore capabilities such as course design, student engagement, research and literature summary, and chatbots for university-wide operations.

Adopting Bing Chat Enterprise for faculty at Linköping University

We've been happy to hear from customers around the world who have started to adopt Bing Chat Enterprise for their faculty and staff, including Linköping University who is one of the first universities to leverage the service in Sweden. "Our community has been asking for the ability to use a generative AI solution, and I'm pleased to now be able to offer Bing Chat Enterprise due to the security and privacy that Microsoft provides" said Joakim Nejdeby, their Chief Information Officer. Their faculty, researchers, and employees are now exploring ways to search for information more efficiently, have assistance in conducting literature reviews, brainstorm ideas for projects and proposals, and draft educational content.

We hope you're as excited as we are to see the continued progress on making the promise of AI real in higher education. We look forward to further supporting the development of AI skills for students and instructors as well as collaborating with our customers and partners. Start your AI journey today by taking our latest educator course on Bing Chat.

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Advancing opportunities for AI in higher education

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Take the new free training for educators: Enhancing teaching and learning with Bing Chat. Build AI literacy with training and tools from Microsoft.

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Advancing meaningful learning in the age of AI

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Advancing meaningful learning in the age of AI

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Bloom's Taxonomy Revisited - Includes AI

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Remember

At this least-complex level of learning activities and assessments, students might use AI tools to look up basic facts about historical events or about character and plot line information in a literary text; to list possible answers or correctly select a category that a concept belongs to; to identify the correct definition of a term or chronological application of a concept; to outline related pieces of information, such as in a taxonomy; or to select the best response from a list.

AI tools are already very strong at producing accurate responses to prompts for basic information that is widely available on the web - much better than Google searches previously and with less effort required from the user; for this reason, in the visual outline above, this level on Bloom's Taxonomy is marked with a recommendation of amend, as learning activities or assessments at this level are more likely to warrant revision.

Faculty may want to consider if students will be expected to have these skills as foundational knowledge, such as doing simple calculations on the spot. Additionally, faculty might consider if there are any situations in which students would be without internet access and would need to recall detailed information for school, work, or personal purposes. For instance, a student doing field work counting fish in a remote area may not be able to access AI or other tools to help identify species. Students may not feel motivated to memorize information if such situations don't or won't exist for them.

Understand

Working on activities and assessments at this level, students might use AI tools to help them describe a concept in different words; to recognize an example of a specific idea; to explain a general theory in a specific context; to translate text from one language to another; or to relate a principle or concept to their own experience. Notably, when prompted with contextual information (such as providing personal details), AI tools can construct believable stories about human experiences, which is an important consideration for learning activities and assessments at this level and other levels of Bloom's Taxonomy.

Faculty may choose to emphasize distinctive human skills by contextualizing learning within the affective domain and incorporating relevant emotional, ethical, and moral perspectives into the curriculum. For example, AI may not be as capable as human thinking to explain the reasoning for respecting team members' pronouns in a group setting, or to infer subtext in a translated speech.

Faculty may also want to consider how students learn, grow, and develop from opportunities to make their own connections to content and applications at this level, and whether these opportunities are a significant loss if offloaded entirely or partially to AI tools; understanding how learning is built across practice tasks might be something to share more explicitly with students. Asking students to contextualize work at this level within learning materials and resources specific to the class and/or peer and instructor comments may also help students to stay focused on doing this cognitive work entirely (or mostly) from their own efforts.

Apply

Students are asked to demonstrate foundational skills related to "application" in their academic careers, such as writing, problem-solving, and decision-making, and these kinds of tasks are present across most courses in higher education. All of these tasks can be supported by the use of AI. AI tools can help students to apply problem-solving processes to industry-related challenges, and they can even create fictional scenarios where students can apply methods or models (e.g., SWOT) to make informed decisions. Students may use AI tools to support their writing process in the context of informal/low-stakes (e.g., journals, reflections, discussion boards) or formal/high-stakes (e.g., research papers, marketing plans, applications, etc.) products; for example, these tools can help a student to brainstorm, outline a paper, summarize sources, draft, and revise.

Despite the support that AI can provide, these tools cannot critically think within the context of the individual's interests and experience or within the course. For that reason, clearly articulating the purpose of assignments ( the "why") to drive student motivation and offering opportunities for students to make meaningful personal and real-world connections are best practices that faculty should continue to employ. A student stitching together their own ideas and supporting them in the context of existing expertise is critical to advancing learning and knowledge creation.

Regardless of a faculty member's position on AI use in their course, a key consideration should be how AI use is framed to students, particularly on application-level tasks. A faculty member may choose to communicate how AI can augment rather than take the place of learning and growth. But, balanced messaging might also prompt faculty to highlight the potential for AI tools to undermine student skill development, if not used judiciously. Considerations about disciplinary and relevant industry standards (e.g., what the field has described as standards for AI use or non-use) may also influence and justify faculty choices related to these kinds of common learning tasks.

Analyze

Students can now collaborate with AI tools on advanced processes such as data analysis. For example, a student can submit a data set, and the AI tool can merge, clean, calculate mathematical equations, compare and contrast information, identify trends, categorize outputs, and structure data. Students may guide, refine, and iterate on the AI analysis using sophisticated and targeted inputs, or analyze its methodology. Students may also use AI capabilities to support their learning and comprehension of the data, including the creation of data visualizations (e.g., charts, diagrams, mind maps, graphs, etc.). Human biases that can unintentionally skew analytical work may be reduced when work is completed by AI systems, though AI may introduce bias as well. AI's general proficiency in particular aspects of analytical tasks prompts us to indicate that activities and assessments at this level are more likely to require amendment.

Yet, no analytical work is entirely computational and there is much room yet for faculty to emphasize reflective pieces of the analytical process into which AI will have only a narrow perspective. For example, students could be asked, "What is missing?" "What needs to be added or adjusted?" "Are there any gaps?" "How is the AI making these determinations?," or "Do I agree with the analysis? Why or why not?"

Faculty revisiting course activities and assessments at this level may want to think holistically about analytical skill development, remembering connections to program-learning outcomes or additional objectives that aim to prepare students to think and reason as practitioners and researchers do within their chosen domain or industry. Situating analytical tasks within authentic and experiential learning contexts may also be helpful in promoting student engagement in these tasks

Evaluate

At this level of learning, students may be asked to critique, appraise, or judge. These are areas in which generative AI is useful, for example, to provide context and identify both strengths and weaknesses of creative works, working hypotheses, or alternative courses of action. Across the curriculum, students may use AI to assist in developing criteria, standards, or rubrics to provide a basis for evaluation, or to provide an evaluation of their work against a faculty-provided rubric.

Distinctive human capacities for self-awareness, metacognitive reflection, and empathy are particularly applicable to learning at this level. Generative AI does not replicate the qualities of human consciousness, such as self-interrogation and self-assessment, that are brought to bear in many evaluative processes.

Faculty may focus on communicating the ways in which AI has strengths in evaluative processes but also falls significantly short of human capacities and qualities in this area. As with assignments at other cognitive levels, balancing and possibly blending the use of generative AI in learning activities with human cognition and metacognition could afford an augmented learning experience that elevates and rewards higher-level thought processes that students engage in after or alongside their collaboration with an AI tool. Students could also benefit from learning to critique the output of generative AI itself.

Create

At this higher order of learning, students may be asked to produce original work such as designing a product, process, or plan. For instance, in creating elements of a management plan for public lands, an AI tool might be used to kick-start the creative process by suggesting a range of alternative scenarios to potentially sustain economic, recreational, environmental, and aesthetic values. A generative AI tool that incorporates a search engine (such as Microsoft Bing) could be prompted to provide both recent and historical information and sources, to enumerate potential drawbacks and advantages of each scenario, and to describe successful real-world cases that resemble each scenario. A number of AI tools also have emerging strengths in creative products, such as image blending and creation and creative writing.

Distinctive human skills come to the fore in constructing novel solutions to problems, whether real or hypothetical, and artistic works that reveal something unique about human perspectives and the human condition. Creativity is a deeply human trait. In the example above, individual students or collaborative groups would apply human judgment (involving application, analysis, and evaluation) to put together potential elements of management scenarios to create a workable, coherent plan. While generative AI is capable of creating meaningful text and images, students can excel at developing original ideas, exercising moral judgment, and collaborating in both spontaneous and structured processes.

Faculty might consider communicating the value of balancing human capacities with the assistance that AI can afford in the creative process. As students become adept at prompt engineering (specific initial prompts and extended follow-up prompting to refine the AI output), judicious use of AI has potential to accelerate and broaden human learning experiences and to augment creative processes.

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Oregon State Ecampus offers 100+ accredited online degrees, undergraduate and graduate, including business, computer science, natural resources and fisheries.

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ChatGPT, Chatbots and Artificial Intelligence in Education

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ChatGPT, Chatbots and Artificial Intelligence in Education

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ChatGPT, Chatbots and Artificial Intelligence in Education

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AI just stormed into the classroom with the emergence of ChatGPT. How do we teach now that it exists? How can we use it? Here are some ideas.FEATURED

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Instead of Policing Students, We Need to Abolish Cheating - Pedagogical Strategies

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Instead of Policing Students, We Need to Abolish Cheating - Pedagogical Strategies

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Pedagogical strategies that can be used to work toward abolishing cheating in essay writing:

Overarching narratives. Syllabi and lectures that present, connect, repeat, and reinforce a big picture, helping students understand the relation of the parts to the whole and thereby what they’re supposed to be learning and why.

Transparent design. Assignments that test students on content but also require them to reflect on how they’re learning and why, teaching them how to be better learners in the future.

Labor-based grading. Assignments that promote learning through process and practice, rather than evaluation and outcomes, rewarding current effort rather than previous preparation.

Pink Time. An exercise that requires students to skip class in order to reflect on personal motivation in the context of greater autonomy.

Collaboration. Students who work in teams through multistep assignments, focusing on the work-flow, organization, delegation, and teamwork skills required for many 21st-century jobs.

Accessibility auditing. Assignments designed to accommodate all students regardless of disability, making further accommodation probably unnecessary, evaluation more fair, and the classroom a model for inclusion as a standard.

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How can educators respond to students presenting AI-generated content as their own?

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How can educators respond to students presenting AI-generated content as their own?

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Do AI detectors work?

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Instead of Policing Students, We Need to Abolish Cheating

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Instead of Policing Students, We Need to Abolish Cheating

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"Policing students punishes their actions but does nothing to mitigate, let alone acknowledge, the contexts that motivate them toward such actions in the first place."

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Opinion | Instead of Policing Students, We Need to Abolish Cheating

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The best response to ChatGPT is to pay more attention to why students cheat in the first place.

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We have tested TII's AI detector and found definite false positives (and some false negatives).

Since our institution decided to embrace generative AI technologies, we have encouraged our faculty to grade based on the content alone and not to deduct points based off of alleged AI use. AI tools often create circular, overly wordy works that don't say too much.

On campus faculty are encourage to ask students a simple question: "What does this mean?" in relation to their work. If they can't describe it quickly and accurately, that is a problem. We would still avoid accusing of AI use, but instead grade based on the fact the student couldn't repeat or synthesize their own thoughts.

The "oral argument confirmation" tactic is obviously difficult in a large class or in asynchronous online courses.

It is my sincere belief that, by the nature of the technology, we will never fully be able to detect generative AI use in writing. The immorality of accusing a single student falsely balances the reality that some students may be skating by with copy-paste AI usage for me.

Cheers,

Jason Bock
--
Jason Bock

Director of Operations
Center for Online and Extended Learning
Tiffin University
419-448-3365
bockje@tiffin.edu

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Guidance on AI Detection and Why We’re Disabling Turnitin’s AI Detector

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Guidance on AI Detection and Why We’re Disabling Turnitin’s AI Detector

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In April of this year, Turnitin released an update to their product that reviewed submitted papers and presented their determination of how much of a paper was written by AI. As we outlined at that time, many people had important concerns and questions about this new tool, namely how the product exactly works and how reliable the results would be. After several months of using and testing this tool, meeting with Turnitin and other AI leaders, and talking to other universities who also have access, Vanderbilt has decided to disable Turnitin's AI detection tool for the foreseeable future. This decision was not made lightly and was made in pursuit of the best interests of our students and faculty.

When Turnitin launched its AI-detection tool, there were many concerns that we had. This feature was enabled for Turnitin customers with less than 24-hour advance notice, no option at the time to disable the feature, and, most importantly, no insight into how it works. At the time of launch, Turnitin claimed that its detection tool had a 1% false positive rate (Chechitelli, 2023). To put that into context, Vanderbilt submitted 75,000 papers to Turnitin in 2022. If this AI detection tool was available then, around 750 student papers could have been incorrectly labeled as having some of it written by AI. Instances of false accusations of AI usage being leveled against students at other universities have been widely reported over the past few months, including multiple instances that involved Turnitin (Fowler, 2023; Klee, 2023). In addition to the false positive issue, AI detectors have been found to be more likely to label text written by non-native English speakers as AI-written (Myers, 2023).

Additionally, there is a larger question of how Turnitin detects AI writing and if that is even possible. To date, Turnitin gives no detailed information as to how it determines if a piece of writing is AI-generated or not. The most they have said is that their tool looks for patterns common in AI writing, but they do not explain or define what those patterns are. Other companies that offer popular AI detectors have either begun to either pivot to other business models (Edwards, 2023) or closed down entirely (Coldewey, 2023). Even if other third-party software claimed higher accuracy than Turnitin, there are real privacy concerns about taking student data and entering it into a detector that is managed by a separate company with unknown privacy and data usage policies. Fundamentally, AI detection is already a very difficult task for technology to solve (if it is even possible) and this will only become harder as AI tools become more common and more advanced. Based on this, we do not believe that AI detection software is an effective tool that should be used.

Moving forward, with Turnitin's AI detection tool disabled, how should instructors handle concerns about students improperly using AI writing tools? First, instructors should communicate with their students early about this. Many students are open to discussion about using AI, what is allowable or not, and how this usage can/will impact their future both in college and in the workforce after they graduate. Clearly outlining expectations and guidelines will go a long way to guiding how AI can be used in your course. If AI usage is allowed in courses, disclosures and citations for its use should be used. There are already guidelines on how to cite AI and ChatGPT to meet APA (McAdoo, 2023) and MLA (2023) citation formats. Additionally, instructors can also consider reformatting assignments to mitigate any concerns about AI usage. Options like in-class writing, requiring students to write about specific topics discussed in class, or even focusing on current issues that AI tools are not trained on are all helpful steps in revising assignments. Additionally, the Center for Teaching Excellence at the University of Kansas has a great resource on " Adapting your course to artificial intelligence" (n.d.). One key aspect to remember throughout all of this is to balance the importance of mitigating inappropriate AI usage while also being mindful of AI's benefits in the teaching and learning process.

If you suspect that students are using AI inappropriately, there are things you can look at to determine if a submitted text is AI-generated. First, if you can, compare this writing to other work from the student. Does it match their style, tone, and level of previous writing? You can also look for inaccuracies in sources, arguments, facts, etc. AI text generators often have no concept of something being accurate or truthful, so they may often "hallucinate" and generate whole sources out of nothing (De Vynck, 2023; Sands, 2023). If you have concerns about students misusing AI, talk to them about it. Some students will just admit the use upfront if approached correctly.

As we move into the future, AI tools will be more and more prevalent. AI tools are already available in places like Google Drive and Bing search, and will soon be included in everyday products like Microsoft Office. For more information on how to use AI in your teaching, please refer to the links below for various resources from across campus.

References

1. (n.d.). Adapting your course to artificial intelligence. University of Kansas, Center for Teaching Excellence. Retrieved July 15, 2023, from https://cte.ku.edu/adapting-classes-artificial-intelligence-era

2. (2023, March 17). How do I cite generative AI in MLA style? MLA Style Center. Retrieved June 12, 2023, from https://style.mla.org/citing-generative-ai/

3. Chechitelli, A. (2023, May 23). AI writing detection update from Turnitin's Chief Product Officer. Retrieved June 1, 2023, from https://www.turnitin.com/blog/ai-writing-detection-update-from-turnitins-chief-product-officer

4. Coldewey, D. (2023, July 25). OpenAI scuttles AI-written text detector over 'low rate of accuracy'. Retrieved July 27, 2023, from https://techcrunch.com/2023/07/25/openai-scuttles-ai-written-text-detector-over-low-rate-of-accuracy

5. De Vynck, G. (2023, May 30). ChatGPT 'hallucinates.' Some researchers worry it isn't fixable. The Washington Post. Retrieved June 1, 2023, from https://www.washingtonpost.com/technology/2023/05/30/ai-chatbots-chatgpt-bard-trustworthy

6. Edwards, B. (2023, July 14). Why AI detectors think the US Constitution was written by AI. Retrieved July 28, 2023, from https://arstechnica.com/information-technology/2023/07/why-ai-detectors-think-the-us-constitution-was-written-by-ai/3

7. Fowler, G. A. (2023, April 3). We tested a new ChatGPT-detector for teachers. It flagged an innocent student. Retrieved April 4, 2023, from https://www.washingtonpost.com/technology/2023/04/01/chatgpt-cheating-detection-turnitin

8. Klee, M. (2023, June 6). She Was Falsely Accused of Cheating With AI - And She Won't Be the Last. Retrieved June 7, 2023, from https://www.rollingstone.com/culture/culture-features/student-accused-ai-cheating-turnitin-1234747351

9. McAdoo, T. (2023, April 7). How to cite ChatGPT. APA Style Blog. Retrieved June 12, 2023, from https://apastyle.apa.org/blog/how-to-cite-chatgpt

10. Myers, A. (2023, May 15). AI-Detectors Biased Against Non-Native English Writers. Stanford University Institute for Human-Centered Artificial Intelligence. Retrieved July 18, 2023, from https://hai.stanford.edu/news/ai-detectors-biased-against-non-native-english-writers

11. Sands, L. (2023, April 6). ChatGPT falsely told voters their mayor was jailed for bribery. He may sue. Retrieved May 30, 2023, from https://www.washingtonpost.com/technology/2023/04/06/chatgpt-australia-mayor-lawsuit-lies

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Guidance on AI Detection and Why We're Disabling Turnitin's AI Detector

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In April of this year, Turnitin released an update to their product that reviewed submitted papers and presented their determination of how much of a paper was written by AI. As we outlined at that time, many people had important concerns and questions about this new tool, namely how the product exactly works and how...

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EDUCAUSE AI Site

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This report describes an ideal future for generative AI in higher education, drawing on the insights of a small panel of teaching and learning higher education experts, focusing on a 10-year timescale.

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use of artificial intelligence in higher education

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impact of artificial intelligence on teaching and learning in higher education

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This page provides resources regarding the use of AI in higher education.

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15 Ways to Use ChatGPT as an Instructional Designer, Instructor, and Teacher

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15 Ways to Use ChatGPT as an Instructional Designer, Instructor, and Teacher

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S5 E13: Prompt Engineering, Formative Assessments, and Reflection

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Listen to this episode from The Learning Geeks Podcast on Spotify. We return to our podcast roots and bring in one thing to share that's currently on our mind. Bob shares the skills we need in AI, such as prompt engineering. Jake discusses formative assessments and why they're not as scary as they may sound.

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ASSIGNING AI:SEVEN APPROACHES FORSTUDENTS WITH PROMPTS

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ASSIGNING AI:SEVEN APPROACHES FORSTUDENTS WITH PROMPTS

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Webinar: Reimagining Higher Education Through AI/ChatGPT

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Webinar: Reimagining Higher Education Through AI/ChatGPT

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Association of College and University Educators

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Reimagining Higher Education Through AI/ChatGPT