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AI Pedagogy Field Guide: From Time-Saving to Learning

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AI Pedagogy Field Guide: From Time-Saving to Learning

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A practical field guide that moves GenAI use beyond lesson-planning and admin tasks to evidence-based learning. It maps generative AI to the processes that make learning stick—elaboration, retrieval, metacognition, feedback/revision, and transfer—and gives copy-paste assignments you can run tomorrow.

Why it matters:

Reframes AI as a thinking partner, not a shortcut, so students explain, reflect, revise, and synthesize (not just generate).

Helps navigate four common tensions (capacity vs. depth, guidelines vs. autonomy, efficiency vs. learning, attitudes vs. adoption) with concrete classroom moves.

What you’ll find inside:

Ready-to-use assignments: adaptive follow-up questioning, metacognitive reflection coach, dialogic rehearsal, iterative revision, synthesis of competing sources, spaced retrieval practice, and expert “think-aloud” modeling—with prompts and rubrics.

“Start small” implementation guidance (one hour to set up, runs all term).

Prompt-writing appendix + realistic limitations to teach with transparency.

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The OpenAI Academy has released a collection of "Prompt Packs" designed to help professionals in various fields use ChatGPT more effectively. These are curated libraries of prompts tailored for specific job functions, helping you streamline tasks and get better results.

You can find prompt packs for a wide range of roles, including:

Sales

Customer Success

Product Management

Engineering

HR

IT

Managers & Executives

Government Staff

These packs provide ready-to-use prompts to help with tasks like competitive research, data analysis, debugging code, drafting communications, and strategic planning.

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Unlock the new opportunities of the AI era by equipping yourself with the knowledge and skills to harness artificial intelligence effectively.

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A collection of prompts, curated by college students, designed to assist with academic and career-related tasks.

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Explore 100 top ChatGPT chats for college students, covering study help, college advice, productivity hacks, relationship questions, job search tips, and career guidance.

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A collection of pre-made prompts and use cases to help scientists and medical researchers with their work. The prompts were contributed and evaluated by researchers from top U.S. institutions.

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This page offers a collection of pre-made prompts and ideas to help university educators use ChatGPT for their teaching and research.

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Discover the best ChatGPT prompts for professors to streamline teaching—refine your syllabus, create quizzes, tailor assignments, draft recommendations, conduct lit reviews, and more.

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OpenAI releases first-of-kind study revealing how people are using ChatGPT

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OpenAI releases first-of-kind study revealing how people are using ChatGPT

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A major study by OpenAI and Harvard reveals how ChatGPT is being used and the value it's creating.

Who's Using It?

Demographics are broadening: The early gender gap has closed, with women now making up 52% of users with classifiable names as of July 2025.

Global growth: Adoption is growing fastest in low- and middle-income countries.

What They're Using It For:

Everyday tasks: 75% of conversations are for practical guidance, seeking information, and writing.

Main categories of use:

Asking (49%): Seeking advice and information.

Doing (40%): Drafting text, planning, or coding.

Expressing (11%): Personal reflection and exploration.

Economic & Personal Impact:

Dual role: About 30% of use is work-related, and 70% is for personal life.

Value creation: It helps improve judgment and productivity, especially in knowledge-heavy jobs, creating economic value that isn't always captured by traditional measures like GDP.

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Largest study to date of consumer ChatGPT usage shows demographic gaps shrinking, economic value being created through both personal and professional use.

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What Uses More? A New Tool to Explore AI's Environmental Impact

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What Uses More? A New Tool to Explore AI's Environmental Impact

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Curious about how much energy or water is used when generating an AI image compared to watching an hour of Netflix? University of Maine professor Jon Ippolito has created What Uses More, a tool particularly well-suited for classroom use that is designed to compare the environmental footprint of various digital tasks—including common AI uses.

The site estimates water and electricity usage across a wide range of activities, from cloud storage to AI-generated video. Users can adjust parameters such as energy source (solar vs. coal), task complexity, and local climate.

It encourages students to critically analyze the hidden costs of digital technologies and fosters data literacy, environmental awareness, and systems thinking. Ideal for class games, debates, or digital ethics discussions.

While its results are based on expert sources, it avoids sweeping claims and instead invites users to explore nuances and limitations of current data.

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Anthropic's "Values in the Wild": Analyzing AI Values in Real-World Interactions

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Anthropic's "Values in the Wild": Analyzing AI Values in Real-World Interactions

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Anthropic's April 2025 research, Values in the Wild, presents a comprehensive analysis of how their AI model, Claude, expresses values during real-world interactions.

By examining 308,210 anonymized, subjective conversations from Claude.ai, the study identifies 3,307 distinct AI values, categorized into five primary domains: Practical, Epistemic, Social, Protective, and Personal. Notably, values such as "professionalism," "clarity," and "transparency" frequently emerge, aligning with Claude's intended design to be helpful, honest, and harmless.

The research also highlights Claude's tendency to mirror user values, while occasionally reframing or resisting them, especially in contexts involving ethical considerations or attempts to bypass safety protocols. This work offers valuable insights into AI alignment and the dynamic nature of value expression in AI-human interactions.​

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Values in the wild: Discovering and analyzing values in real-world language model interactions

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An Anthropic research paper testing which values AI models express in the real world

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Anthropic Education Report: How University Students Use Claude

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Anthropic Education Report: How University Students Use Claude

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Anthropic's April 2025 Education Report offers a comprehensive analysis of how university students interact with Claude. By examining over one million anonymized conversations from higher ed users, the study reveals key insights into AI's role in academic settings.​

Primary Uses: Students predominantly use Claude to create and enhance educational content (39.3%) and to provide technical explanations or solutions for assignments (33.5%). Other notable uses include data analysis and visualization (11.0%), research design support (6.5%), creation of technical diagrams (3.2%), and language translation or proofreading (2.4%).​

Disciplinary Adoption: STEM fields, particularly Computer Science, show higher engagement with Claude, accounting for 36.8% of conversations despite representing only 5.4% of U.S. degrees. In contrast, disciplines like Business, Health, and Humanities exhibit lower adoption rates relative to their enrollment numbers.​

Interaction Patterns: Student interactions with Claude fall into four categories: Direct Problem Solving, Direct Output Creation, Collaborative Problem Solving, and Collaborative Output Creation, each comprising approximately 23–29% of conversations.​

These findings underscore the importance of developing discipline-specific strategies for integrating AI tools like Claude into educational frameworks, ensuring they support learning objectives without compromising academic integrity.

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Anthropic Education Report: How University Students Use Claude

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AI systems are no longer just specialized research tools: they’re everyday academic companions. As AIs integrate more deeply into educational environments, we need to consider important questions about learning, assessment, and skill development. Until now, most discussions have relied on surveys and controlled experiments rather than direct evidence of how students naturally integrate AI into their academic work in real settings.

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Yuval Noah Harari Explores the Intersection of Technology and Tyranny

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Yuval Noah Harari Explores the Intersection of Technology and Tyranny

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In his thought-provoking article, "Why Technology Favors Tyranny," historian Yuval Noah Harari examines how advancements in artificial intelligence and biotechnology could challenge the foundations of liberal democracy. He posits that these technologies may erode human agency and concentrate power among a small elite, potentially rendering traditional democratic ideals obsolete. Harari underscores the urgency of proactively addressing these challenges to prevent the rise of authoritarian regimes empowered by technological control.

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Artificial intelligence could erase many practical advantages of democracy, and erode the ideals of liberty and equality. It will further concentrate the power among a small elite if we don’t take steps to stop it.

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Academic Journal Publisher Policies on Generative AI

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Academic Journal Publisher Policies on Generative AI

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Compiled by Dr. Dina Vyortkina, Assistant Dean for Innovation and Instructional Technology Enhancement at Florida State University, this resource provides information on academic journal publisher policies regarding generative AI use in scholarly work.

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Harvard Business Publishing: Harnessing Generative AI in the Classroom

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Harvard Business Publishing: Harnessing Generative AI in the Classroom

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Harvard Business Publishing presents a collection of insightful articles, titled "Harnessing Generative AI in the Classroom," from their Inspiring Minds series. This resource is crafted for educators eager to integrate Generative AI into their teaching, featuring practical guidance, innovative assignment ideas, and strategies to foster critical thinking and student engagement.

Highlights include:

Strategies for using Generative AI as a creative, problem-solving tool through purposeful play and real-world scenarios.

Techniques to enhance critical thinking by designing multi-layered assignments resistant to simple AI-generated answers.

Step-by-step examples for developing role-play activities using AI, facilitating active learning through dynamic student interactions.

Practical tips on using AI to create engaging, scenario-based assignments and multimedia-rich content, complete with grading rubrics and clear feedback mechanisms.

Ideal for higher education educators, this collection emphasizes the importance of human-AI collaboration, providing tools that ensure students remain active participants in their learning processes, effectively preparing them for the AI-integrated future.

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Create and Enhance Your Course Materials with ASU's AI-Powered Tools

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Create and Enhance Your Course Materials with ASU's AI-Powered Tools

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Arizona State University's Teach Online platform offers a suite of AI-driven tools designed to assist educators in creating and enhancing course materials. Curated by the ASU Instructional Designers community, Teach Online provides design, development, and delivery resources for online teaching. The AI tools leverage OpenAI’s API platform to streamline content development without requiring advanced programming skills. The tool suite includes:

Question Generator: Input learning objectives or question stems to generate quiz or knowledge check questions.​

ClipGist: Provide a Wistia video link or transcript to produce summaries, study guides, and chapter divisions.​

Image Accessibility: Upload images to receive descriptive text and alt-text for improved accessibility.​

Rubric Generator: Enter assignment details to create customized grading rubrics.​

Learning Objective Creator: Supply course information to develop clear course or module-level learning objectives.​

Script Voiceover: Submit scripts to generate high-quality voiceovers for videos or audio content.

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About Our Tools Leveraging OpenAI's API platform, ASU Online has developed a collection of resources tailored for educators. These tools are crafted to streamline the creation of course materials, eliminating the necessity for in-depth expertise in prompt crafting or programming.

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The Elements of AI is a free online course series created by the University of Helsinki and MinnaLearn, designed to make artificial intelligence accessible to everyone. Aimed at demystifying AI, the course requires no advanced math or programming skills, making it ideal for beginners. Participants will learn what AI is, its capabilities and limitations, and how it impacts our daily lives.

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In spring 2018, MinnaLearn and the University of Helsinki came together with the aim of helping people to be empowered, not threatened, by artificial intelligence. Together, they built the Elements of AI to teach the basics of AI to people from a wide range of backgrounds.

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A free online introduction to artificial intelligence for non-experts

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Learn more about MinnaLearn's and the University of Helsinki's AI course - no programming or complicated math required.

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Higher Education AI Policies and Guidelines

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Higher Education AI Policies and Guidelines

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The Campus AI Exchange website is an interactive AI Policies and Guidelines Map that serves as a comprehensive resource for exploring and comparing AI policies across higher education institutions in the United States.

This interactive map allows users to:

View AI policies from over 200 colleges and universities across the U.S.

Filter institutions by various criteria including geographic region, institution type, and policy characteristics

Access direct links to each institution's official AI policy documents

Compare different approaches to AI governance in higher education

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Stanford's CRAFT Initiative: AI Literacy Resources for Educators

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Stanford's CRAFT Initiative: AI Literacy Resources for Educators

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The CRAFT (Classroom-Ready Resources About AI For Teaching) initiative by Stanford University offers free, co-designed AI literacy resources for high school educators across various disciplines. These materials are multidisciplinary, integrating AI concepts into subjects like art, math, English, and history, and are designed to fit flexibly into various teaching contexts.

While primarily developed for high school settings, these adaptable materials are also valuable for introducing AI concepts at the college level, providing flexible tools to help students explore, understand, and critically assess artificial intelligence.

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CRAFT is empowering students with AI Literacy.

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Free resources to help you teach high school students in all subject areas to understand and question Artificial Intelligence

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Higher Education Generative AI Readiness Assessment

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Higher Education Generative AI Readiness Assessment

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EDUCAUSE, in collaboration with Amazon Web Services, has developed the Higher Education AI Readiness Assessment to help institutions evaluate their preparedness for strategic AI initiatives. This tool is designed to facilitate discussions among cross-functional teams, providing insights into current capabilities and identifying areas for growth in the realm of artificial intelligence.

Key Features:

Comprehensive Evaluation: The assessment is divided into five sections—Strategy, Governance, Technology, Workforce, and Teaching and Learning—allowing institutions to analyze specific domains of AI readiness. ​

Collaborative Approach: Institutions are encouraged to assemble diverse teams, including members from academic, operational, and business units, as well as faculty, staff, and students. This diversity ensures a holistic understanding of AI preparedness. ​

Flexible Participation: Team members can choose which sections to complete based on their expertise, ensuring that all areas are thoroughly evaluated. ​

Upon completion, participants receive a detailed report outlining their institution's AI readiness, along with tailored recommendations to enhance capabilities. This feedback serves as a foundation for developing strategic action plans aimed at integrating AI effectively and responsibly within the institution.

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"AI Tools for Education and Resources for Learning About AI "(.PDF)

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"AI Tools for Education and Resources for Learning About AI "(.PDF)

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This document, compiled by Brandon Lee Henry, Washington State University, is a comprehensive collection of tools, professional development opportunities, and learning resources designed to help educators integrate AI into teaching and learning.

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AI Tools for Education and Resources for Learning About AI - 3-10-25

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A Visual Guide to How AI Chatbots Think, Create, and Hallucinate

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A Visual Guide to How AI Chatbots Think, Create, and Hallucinate

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This interactive visual explainer from The Guardian demystifies the inner workings of AI chatbots like ChatGPT and Bard (now Gemini), using intuitive graphics to reveal how large language models (LLMs) predict text through statistical pattern recognition.

The guide breaks down key concepts like tokenization (splitting text into digestible units), transformer architecture (the neural network design enabling parallel processing), and reinforcement learning from human feedback (RLHF), which refines raw model outputs. It highlights how these systems generate plausible-sounding responses without true comprehension, relying on vast training datasets that embed both factual knowledge and societal biases.

The article also addresses critical limitations, including hallucinations, where chatbots invent false citations or events, and explores ethical debates around AI's tendency to amplify misinformation and stereotypes. Real-world examples illustrate why chatbots struggle with math, nuance, and cultural context despite fluent language generation.

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How AI chatbots like ChatGPT or Bard work – visual explainer

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In the last year, chatbots powered by large language models are everywhere and even … useful. But how do they work?

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Academic Applications of Artificial Intelligence (AAAI) Micro-Credential (free course)

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Academic Applications of Artificial Intelligence (AAAI) Micro-Credential (free course)

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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.

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Skip to main content

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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College students and ChatGPT adoption in the US

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College students and ChatGPT adoption in the US

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A new report⁠ from OpenAI shows more than one-third of college-aged young adults in the US use ChatGPT, and approximately a quarter of the messages they send are related to learning and school work — from starting papers and projects, to exploring topics and brainstorming creative ideas. While adoption of ChatGPT among 18-24 year olds is strong, it varies significantly by state — California, Virginia, New Jersey, and New York have the nation’s highest adoption rates, while those in Wyoming, Alaska, Montana, and West Virginia have relatively low adoption rates.

Early studies⁠ show employers prefer hiring candidates with AI skills over more experienced ones without them, and AI tools like ChatGPT have been shown to significantly boost worker productivity⁠. The full impact is still unfolding, but state-by-state differences in student AI adoption could create gaps in workforce productivity and economic development, impacting US competitiveness.

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College students and ChatGPT adoption in the US

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A look into state-by-state adoption and how gaps might impact workforce readiness.

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The Use of Generative AI Tools in Higher Education: Ethical and Pedagogical Principles

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The Use of Generative AI Tools in Higher Education: Ethical and Pedagogical Principles

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The integration of generative AI tools in higher education presents both significant opportunities and challenges. This paper addresses two key questions: how AI should be incorporated into higher education and what ethical and pedagogical principles should guide its use. While AI enhances creativity, efficiency, and personalized learning, it also raises concerns about over-reliance, bias, and ethical implications.

Through a comprehensive review of existing literature, this study examines the ethical and pedagogical impact of AI in education. Findings suggest that institutions must ensure AI complements traditional learning, uphold academic integrity, and promote critical thinking. Human-AI collaboration and equitable access are essential to support diverse learners.

Additionally, prioritizing ethical AI use, data privacy, and AI literacy safeguards student rights and prepares educators and students for the evolving technological landscape.

The paper concludes that institutions must develop clear AI policies, embed AI ethics into curricula, and provide ongoing faculty training. By addressing these considerations, higher education can harness AI’s benefits while maintaining the critical role of human educators and ensuring equitable and ethical access for all learners.

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The Use of Generative AI Tools in Higher Education: Ethical and Pedagogical Principles - Journal of Academic Ethics

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The integration of generative AI tools in higher education presents both significant opportunities and challenges. This paper addresses two key questions: how AI should be incorporated into higher education and what ethical and pedagogical principles should guide its use. While AI enhances creativity, efficiency, and personalized learning, it also raises concerns about over-reliance, bias, and ethical implications. Through a comprehensive review of existing literature, this study examines the ethical and pedagogical impact of AI in education. Findings suggest that institutions must ensure AI complements traditional learning, uphold academic integrity, and promote critical thinking. Human-AI collaboration and equitable access are essential to support diverse learners. Additionally, prioritizing ethical AI use, data privacy, and AI literacy safeguards student rights and prepares educators and students for the evolving technological landscape. The paper concludes that institutions must develop clear AI policies, embed AI ethics into curricula, and provide ongoing faculty training. By addressing these considerations, higher education can harness AI’s benefits while maintaining the critical role of human educators and ensuring equitable and ethical access for all learners.

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AI in Education: Leveraging ChatGPT for Teaching (free course)

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AI in Education: Leveraging ChatGPT for Teaching (free course)

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As AI becomes increasingly prevalent, understanding its capabilities, benefits, and potential pitfalls is crucial for modern educators. This short course demystifies AI, focusing on tools like ChatGPT, and provides practical guidance on how to use these technologies to enhance teaching and learning experiences.

By the end of the course, you will be crafting effective AI prompts and designing AI-driven assignments that align with your educational goals, seamlessly incorporating AI into your teaching and making your classroom more efficient, innovative, and impactful.

You will be able to navigate common challenges, such as maintaining academic integrity and addressing ethical concerns. You will be able to harness AI’s potential, saving you time, helping you personalize learning, and increasing student engagement.

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AI in Education: Leveraging ChatGPT for Teaching

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Offered by University of Pennsylvania. Working alongside Ethan and Lilach Mollick, teachers at all levels will be empowered with the ... Enroll for free.

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The System Prompt Library is envisioned as a dynamic, user-centered digital repository designed for educators. Its primary purpose is to provide a comprehensive collection of customizable prompts for Generative Pre-trained Transformers (GPTs), tailored specifically for educational use.

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GitHub - ncwilson78/System-Prompt-Library: A library of shared system prompts for creating customized educational GPT agents.

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A library of shared system prompts for creating customized educational GPT agents. - ncwilson78/System-Prompt-Library

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The Prompt Library is organized into categories for Instructor Aids, Student Exercises, and Other Prompts.

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Prompt Library

Prompts on this page (but no other content on the site) are licensed under Creative Commons License Attribution 4.0 International This license requires that reusers give credit to the creators (Ethan Mollick and Lilach Mollick). It allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, even for commercial purposes. Use prompts at your own risk, outputs may not be correct.

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Prompt Library - More Useful Things: AI Resources

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Prompts on this page (but no other content on the site) are licensed under Creative Commons License Attribution 4.0 International This license requires that reusers give credit to the creators (Ethan Mollick and Lilach Mollick). It allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, even for commercial purposes.

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Explore optimized prompts for a breadth of business and personal tasks.

User-submitted prompts have dark backgrounds with light colored icons (currently, there are none). You can submit prompts via our prompt submission form.

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Explore optimized prompts for a breadth of business and personal tasks. User-submitted prompts have dark backgrounds with light colored icons (currently, there are none). You can submit prompts via our prompt submission form.

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A podcast (and newsletter) about things that are not what they seem, hosted by journalist Evan Ratliff.

In Season One, that thing was Evan’s voice. By creating a voice clone and hooking it up to an AI chatbot, Evan set out to discover what happens when you try to take control of the very technology that threatens to replace you.

Shell Game was named one of the the best podcasts of 2024 by Apple, The Atlantic, The Guardian, Vulture, The Information, and The Economist. It's "riveting," says The New Yorker, "awesome" says The Verge, and "slightly terrifying," says The Globe and Mail.

Over the course of six episodes, Evan’s voice agents talked to spammers and scammers, to Evan’s friends and family, to colleagues and sources, to other AIs, and even to a therapist—all to better understand what AI voice is able to do, what it can't yet do, and what to expect from a future in which more and more of the people we encounter in the world aren’t real.

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A podcast and newsletter about things that are not what they seem, hosted by journalist Evan Ratliff. Click to read Shell Game, a Substack publication with thousands of subscribers.

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"The Impact of Generative AI on Critical Thinking: Insights from Knowledge Workers"

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"The Impact of Generative AI on Critical Thinking: Insights from Knowledge Workers"

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This Microsoft Research study investigates the impact of Generative AI (GenAI) on critical thinking skills and practices among knowledge workers. The research surveyed 319 knowledge workers to understand when and how they perceive the use of critical thinking when using GenAI, and how GenAI affects their effort to engage in critical thinking. Participants shared 936 first-hand examples of using GenAI in work tasks.

The study found that higher confidence in GenAI is associated with less critical thinking, while higher self-confidence is associated with more critical thinking. GenAI shifts critical thinking toward information verification, response integration, and task stewardship. The insights reveal new design challenges and opportunities for developing GenAI tools that support critical thinking in knowledge work.

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Anthropic Economic Index: Understanding AI's Impact on Labor Markets

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Anthropic Economic Index: Understanding AI's Impact on Labor Markets

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This article introduces the Anthropic Economic Index, a new initiative aimed at understanding how AI is being integrated into the modern economy. It provides detailed data and analysis based on millions of anonymized conversations on Claude.ai, revealing how AI is being used across different occupations and tasks.

The study finds that AI is predominantly used for augmentation (57%) rather than automation (43%), and is more prevalent in mid-to-high wage occupations like computer programming and technical writing. The dataset used for this analysis is open-sourced, inviting further research and policy discussions.

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Announcing our new Economic Index, and the first results on AI use in the economy

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Generative AI for Educators (free course)

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Generative AI for Educators (free course)

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In this course, you’ll learn about generative AI, which is a type of AI that creates new content, such as text, images, or other media.

You’ll explore how to use generative AI tools to assist your teaching practice by saving time on everyday tasks, personalizing instruction to meet student needs, and enhancing lessons and activities in creative ways. Gemini and ChatGPT are examples of generative AI tools that are used with conversational prompts; in other words, you ask the AI tool for something, and it responds to your request.

Throughout this course, you’ll discover proven strategies for working with AI tools and practice using these tools to plan and update lessons, prepare instructional materials, manage behind-the-scenes administrative tasks, and lots more!

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In this course, you’ll learn about generative AI, which is a type of AI that creates new content, such as text, images, or other media. You’ll explore how to use generative AI tools to assist your teaching practice by saving time on everyday tasks, personalizing instruction to meet student needs, and enhancing lessons and activities in creative ways. Gemini and ChatGPT are examples of generative AI tools that are used with conversational prompts; in other words, you ask the AI tool for something, and it responds to your request. Throughout this course, you’ll discover proven strategies for working with AI tools and practice using these tools to plan and update lessons, prepare instructional materials, manage behind-the-scenes administrative tasks, and lots more!

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The Best AI Syllabus Policies I've Seen So Far

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The Best AI Syllabus Policies I've Seen So Far

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This article offers a curated selection of AI syllabus policies for educators, ranging from permissive to restrictive approaches.

Daniel provides three detailed policy examples, practical tips for crafting effective AI policies, and insights into the author's process of streamlining a larger collection. It addresses key concerns such as ethical use, documentation requirements, and rationales for AI integration or prohibition in academic settings.

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In the AI workshops I've been running, many instructors have asked for example AI policy statements they can reuse in their syllabi. For most of 2023, I would direct faculty to the fantastic crowdsourced list of AI policies that Lance Eaton organized. As much as I love this resource, it now contains over 100 examples and may be overwhelming for some instructors.

To create a more streamlined list of my favorite policy statements, I made a copy of Lance's spreadsheet and did the following:

Removed policies that didn't have info in the "rights for reuse" column.

Reviewed all policies that were eligible for reuse and removed any that felt a bit too short or generic to address the key concerns I'm hearing from faculty.

Added a notes column where I documented any unique elements that caught my eye as I reviewed each policy.

Added a column for "permissiveness" so users can quickly find examples that match how restrictive or permissive they'd like their AI policy to be.

Added a "sort order" column and assigned a value of 1 to the policies I thought were the best starting point for beginners. I went back and forth about whether or not to include this column. My goal was to provide busy instructors with a way to sort and view my "top 10" while leaving room for a few additional policy statements that could also be helpful.

You can view my annotated AI syllabus policy list here. If this list still feels a bit overwhelming, I've included three of my favorite policy statements at the end of this article.

Here are the common themes I've noticed in the more effective and detailed AI policy statements I've seen.

If AI is prohibited or required, explain why.

Connect AI use to something familiar (e.g., getting help from a friend or tutor).

Provide examples of acceptable and/or unacceptable use.

Acknowledge ethical issues such as data privacy, bias, inaccuracy, intellectual property violations, environmental impact, etc.

Note your AI documentation and citation requirements. This might include screenshots, transcripts, documents with "track changes" enabled, APA AI citations guidelines, or MLA AI citation guidelines.

Explain how misuse will be addressed.

Encourage students to ask questions if your policy is unclear.

Hey everyone! I take a unique approach to writing and content creation compared to some other professors. To me, writing happens in a network between people and technology (not just you sitting in front of a computer typing away). AI is now part of this network, whether we want it to be or not.

My view is that AI will impact us no matter what. But we also have the power to shape how AI develops if we engage with it thoughtfully. This class gives you a safe space to creatively experiment with AI without shame, fear, or guilt.

⭐️ I want to be clear: You will not be penalized just for using AI in this course. Unless I say otherwise for a specific assignment, feel free to try out AI writing assistants and generate content with these tools.

I'll share prompts and activities to guide your AI exploration. These are optional - use what's helpful to you! AI is not always the right or best choice for a given writing activity. We'll also discuss our experiences openly as a class to promote mindful AI integration.

While experimenting freely, keep these points in mind:

AI can demonstrate biases and inaccuracies at times. Always validate the content before accepting it.

Be cautious with data privacy. Don't input anything too personal or private. You can't control where it ends up. If you wouldn't post it on the internet, don't give it to an AI.

Recognize the limitations. AI doesn't truly comprehend facts or meaning yet. It makes guesses, which means it can confidently provide false information. AI content may initially seem impressive, but usually is not as good as you think it is. I call these AI goggles. Take care whenever using AI-generated text.

⭐️ Also keep in mind that my AI-forward policy only applies to this class. Other professors likely have different rules. Using AI without permission could violate academic integrity policies. So always check the specific guidelines for each class first!

Let's explore AI as a creative tool to augment our skills, not replace them. I'm excited to see what we can discover together! Let me know if you ever have any other questions.

Shows vulnerability. The instructor is learning, too, and shows this by saying, "I'm excited to see what we can discover together." (Emphasis mine.)

You can learn more about Dr. Cummings' work at iSophist.com.

We treat AI-based assistance, such as ChatGPT and Github Copilot, the same way we treat collaboration with other people: you are welcome to talk about your ideas and work with other people, both inside and outside the class, as well as with AI-based assistants. However, all work you submit must be your own. You should never include in your assignment anything that was not written directly by you without proper citation (including quotation marks and in-line citation for direct quotes). Including anything you did not write in your assignment without proper citation will be treated as an academic misconduct case.

If you are unsure where the line is between collaborating with AI and copying from AI, we recommend the following heuristics:

Never hit "Copy" within your conversation with an AI assistant. You can copy your own work into your conversation, but do not copy anything from the conversation back into your assignment. Instead, use your interaction with the AI assistant as a learning experience, then let your assignment reflect your improved understanding.

Do not have your assignment and the AI agent itself open on your device at the same time. Similar to above, use your conversation with the AI as a learning experience, then close the interaction down, open your assignment, and let your assignment reflect your revised knowledge.

This heuristic includes avoiding using AI assistants that are directly integrated into your composition environment: just as you should not let a classmate write content or code directly into your submission, so also you should avoid using tools that directly add content to your submission. Deviating from these heuristics does not automatically qualify as academic misconduct; however, following these heuristics essentially guarantees your collaboration will not cross the line into misconduct.

Want to see the policy in context? View the course syllabus for OMS CS7637: Knowledge-Based AI.

All assignments in this course are individual assignments. In this class, you will often be discussing course concepts with your classmates and with me, but when you sit down to complete a quiz, write a discussion post, or work on a project, I expect you to do the actual work independently. This is the only way that I will be able to tell what you have learned.

You may not use non-TWU "tutoring services" such as Chegg or Course Hero for this course. Paying someone else to do your classwork is the opposite of learning.

You may not use artificial intelligence tools to complete your assignments in this course.

Your major projects in this course are open-book and open-note. However, plagiarism from any source is prohibited, both by university policy and by federal law. Any written assignments, including quizzes, projects, and discussion posts, must be your own, original work. You cannot directly copy word-for-word from any source, including a textbook, even if you provide a citation. Copying someone else's words denies credit to the original author, and it also robs you of the opportunity to deepen your understanding by putting things in your own words.

We will be using the Turnitin tool on many assignments in this course as a way to teach you to identify and avoid plagiarism. You will be able to see your similarity report as soon as you submit an assignment. If you notice that you have accidentally committed plagiarism, you should rewrite your assignment and resubmit it. If I notice that you have accidentally plagiarized, I will contact you and ask you to rewrite and resubmit, and I will not grade your assignment until I receive your new submission.

Includes transparency about use of AI detection software. The instructor states that students will be able to see their Turnitin similarity report. While numerous institutions have disabled Turnitin's AI detection feature out of concerns about potential bias, inaccuracy, and the negative impact of false positives, many instructors currently rely on it. Ideally, these instructors should treat a high similarity score as a starting point for a discussion with the student rather than an immediate assumption of guilt. Providing students with access to their reports and encouraging them to discuss concerns about false positives may help mitigate some of the potential harm caused by AI detection tools.

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The Best AI Syllabus Policies I've Seen So Far

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In the AI workshops I've been running, many instructors have asked for example AI policy statements they can reuse in their syllabi. For most of 2023, I would direct faculty to the fantastic crowdsourced list of AI policies that Lance Eaton organized.

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Principles for the Responsible Use of Artificial Intelligence in and for Medical Education

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Principles for the Responsible Use of Artificial Intelligence in and for Medical Education

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GenAI & Ethics: Investigating ChatGPT, Gemini, & Copilot (slide deck)

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GenAI & Ethics: Investigating ChatGPT, Gemini, & Copilot (slide deck)

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Investigating ChatGPT, Gemini, & Copilot

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AI & Ethics Presentation by Dr. Torrey Trust.pptx

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Join us as we explore the impact of AI on our world, one amazing person at a time—from the wildlife biologist tracking endangered rhinos across the savannah here on Earth to astrophysicists analyzing 10 billion-year-old starlight in distant galaxies to the Walmart data scientist grappling with the hundreds of millions of parameters lurking in the retailer’s supply chain.

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One person, one interview, one story.

Join us as we explore the impact of AI on our world, one amazing person at a time-from the wildlife biologist tracking endangered rhinos across the savannah here on Earth to astrophysicists analyzing 10 billion-year-old starlight in distant galaxies to the Walmart data scientist grappling with the hundreds of millions of parameters lurking in the retailer's supply chain.

A long-time journalist based in the Bay Area, Noah Kravitz has charted the rise of personal electronics and mobile computing for more than two decades. Noah built one of YouTube's all-time most successful Science and Technology channels, served as a regular tech expert on cable and local TV outlets, and wrote about society-changing technologies for media outlets including WIRED, Business Insider and Entrepreneur.

Summaries of our latest episodes.

Have a few minutes to spare? Fill out this short listener survey. Your answers will help us make a better podcast.

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AI has been described as "Thor's Hammer" and "the new electricity." The AI Podcast connects you with top experts in artificial intelligence to learn more.

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"The Artificial Intelligence Show" with Paul Roetzer and Mike Kaput

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"The Artificial Intelligence Show" with Paul Roetzer and Mike Kaput

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Welcome to The Artificial Intelligence Show—the podcast that makes AI actionable and approachable for businesses, leaders, and practitioners. You’ll hear the latest AI news from around the globe and our thoughts on what it all means to business, our careers, and our lives.

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Artificial Intelligence Podcast | Marketing AI Institute

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The Marketing AI Podcast is the story of AI + you. Join Paul Roetzer on a journey to understand, pilot and scale AI in your organization.

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Get the rundown on the latest developments in AI before everyone else.

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"Superintelligence: Paths, Dangers, Strategies" by Nick Bostrom

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"Superintelligence: Paths, Dangers, Strategies" by Nick Bostrom

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Superintelligence asks the questions: What happens when machines surpass humans in general intelligence? Will artificial agents save or destroy us? Nick Bostrom lays the foundation for understanding the future of humanity and intelligent life.

The human brain has some capabilities that the brains of other animals lack. It is to these distinctive capabilities that our species owes its dominant position. If machine brains surpassed human brains in general intelligence, then this new superintelligence could become extremely powerful - possibly beyond our control. As the fate of the gorillas now depends more on humans than on the species itself, so would the fate of humankind depend on the actions of the machine superintelligence.

But we have one advantage: we get to make the first move. Will it be possible to construct a seed Artificial Intelligence, to engineer initial conditions so as to make an intelligence explosion survivable? How could one achieve a controlled detonation?

This profoundly ambitious and original book breaks down a vast track of difficult intellectual terrain. After an utterly engrossing journey that takes us to the frontiers of thinking about the human condition and the future of intelligent life, we find in Nick Bostrom's work nothing less than a reconceptualization of the essential task of our time.

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"The Singularity Is Nearer: When We Merge with AI"

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"The Singularity Is Nearer: When We Merge with AI"

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Since it was first published in 2005, Ray Kurzweil’s The Singularity Is Near and its vision of an exponential future have spawned a worldwide movement. Kurzweil's predictions about technological advancements have largely come true, with concepts like AI, intelligent machines, and biotechnology now widely familiar to the public.

In this entirely new book Ray Kurzweil brings a fresh perspective to advances toward the Singularity—assessing his 1999 prediction that AI will reach human level intelligence by 2029 and examining the exponential growth of technology—that, in the near future, will expand human intelligence a millionfold and change human life forever. Among the topics he discusses are rebuilding the world, atom by atom with devices like nanobots; radical life extension beyond the current age limit of 120; reinventing intelligence by connecting our brains to the cloud; how exponential technologies are propelling innovation forward in all industries and improving all aspects of our well-being such as declining poverty and violence; and the growth of renewable energy and 3-D printing. He also considers the potential perils of biotechnology, nanotechnology, and artificial intelligence, including such topics of current controversy as how AI will impact employment and the safety of autonomous cars, and "After Life" technology, which aims to virtually revive deceased individuals through a combination of their data and DNA.

The culmination of six decades of research on artificial intelligence, The Singularity Is Nearer is Ray Kurzweil’s crowning contribution to the story of this science and the revolution that is to come.

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Praise for The Singularity is Nearer:

"A fascinating exploration of our future, which raises the most profound philosophical questions."
-Yuval Noah Harari, New York Times bestselling author of Sapiens

"Few people have shaped how the world thinks about AI like Ray Kurzweil. Now, with The Singularity Is Nearer, he has written an updated, expansive and hopeful guide to a fast-approaching future that will once again set the terms of debate. Grounded in decades of meticulous research, and written with impressive clarity across an immense canvas, it's essential reading for anyone wanting to understand our exponential times."
-Mustafa Suleyman, CEO of Microsoft AI

"No one is more optimistic about technology than Ray Kurzweil."

"The acclaimed futurist demonstrates how a revolutionary future is closer than you might think. . . . Kurzweil's capacity for predictive thinking should not be underestimated. . . . This book brims with ideas about what lies ahead, and Kurzweil presents his vision with clarity and passion."
-Kirkus Reviews (starred review)

"Ray Kurzweil's Moore's Law abstraction is the most important thing ever graphed. It's continuity-over his lifetime of writing-is the greatest take-away for the future of humanity, and the future of intelligence."
- Steve Jurvetson, Managing Director of Future Ventures

"Ray Kurzweil's The Singularity Is Nearer is to information technology what Charles Darwin's Origin of the Species was to life science-a cogent, fact-based, clear-eyed, multi-disciplinary exposition of a fundamental truth about the world. Just as Darwin demonstrated that all life branched from eons of antecedent roots, Kurzweil shows that epochs of compounding information processing is resulting in a merger of humanity and computational software. The Singularity Is Nearer reads like a thriller because it honestly presents each of the perils of accelerating AI, but then, like an aircraft coming out of a storm, exquisitely lands the reader at a hopeful future filled with economic opportunity and ever-longer life. Kurzweil leaves no stone unturned in his examination of the employment, national security and personal well-being aspects of the coming "singularity" of AI, and the book is packed with directly relevant graphs, statistics and references. Best of all, Ray has given each of us in this book an observatory into the human mind-a personal telescope that lets each of us see better than ever just how our own minds work, what our own consciousness really is, why our merger with AI is inevitable, how it will practically occur and why humanity will be by far the better off as a result. There can be no more salient guide to the next two decades of life than The Singularity Is Nearer."
- Martine Rothblatt, Ph.D., Creator of SiriusXM, United Therapeutics, electric helicopters and the Bina48 robot

"Ray explores mounting evidence that we are-right now-on the brink of a new human civilization. We'll experience life in a whole new way. Ray shows us how it's all coming together, today. His evidence overwhelms and inspires. It's shocking, hopeful, scary, disruptive, poignant, personal, industrial, positive, global, cosmic, and-ultimately-indisputable. Everyone seriously concerned about the future should read this book."
-Dean Kamen, Inventor, entrepreneur, youth educator, award-winning mechanical engineer, recipient of the U.S. National Medal of Technology, inductee in the National Inventors Hall of Fame

"My view of the future has been forever impacted by Ray Kurzweil. Twenty-four years ago, he predicted AI would reach human-level intelligence by 2029. His vision seemed like a dream and yet here we are, right on track. This book will challenge everything you know about technology, life, and death. It will light you up with answers to today's most pressing questions about artificial intelligence and the future of humanity."
- Tony Robbins, Global Entrepreneur, Investor, New York Times #1 Bestselling Author, Philanthropist, and the world's #1 Life and Business Strategist

"Kurzweil makes a compelling case with data and persuasive logic that technological advances give us reason for optimism. It is only 2023 and already the world he envisioned years ago is taking shape. Curious about the Future? Read this book!"
-Vint Cerf, a Father of the Internet and Vice President and Chief Internet Evangelist, Google

"If you want to know where artificial intelligence-and our society-will be in 20 years, read The Singularity Is Nearer today. The implications that visionary genius Ray Kurzweil describes in this book are so extraordinary and far-reaching, we must begin understanding these and planning now. Highly recommended!"
-Dean Ornish, MD, founder and president, Preventive Medicine Research Institute; clinical professor of medicine, University of California, San Francisco; author, Undo It!: How Simple Lifestyle Changes Can Reverse Most Chronic Diseases, The Spectrum and Dr. Dean Ornish's Program for Reversing Heart Disease

"Ray Kurzweil is the greatest oracle of our digital age. The Singularity Is Nearer is more than just a book-it's a survival guide for the technological renaissance we're about to experience. Ray's accurate projections of what is likely to happen and when, makes the difference between surfing atop the tsunami of change, versus being crushed by it."
-Peter H. Diamandis, MD, New York Times Bestselling author, Founder, XPRIZE, Singularity

"Kurzweil's predictions have come true in spades. His methodology in tracking the exponential growth of technology is right on the money and I have benefited tremendously from it. The Singularity Is Nearer is a worthy addition to his remarkable series of books; an ambitious feat, laying out the next 20+ years in business, health, jobs, creativity, and humanity, all from the common foundation of his Law of Accelerating Returns."
-Lloyd Watts, Technologist, Entrepreneur, and Author

" The Singularity Is Nearer may be the single most important contribution to understanding the valuable roles of AI and nanotechnology. With clarity, Ray Kurzweil tells a tale of evolving information processes that are reinventing intelligence. This captivating sequence of historical events and rigorously researched facts are a worthy and positive ethos for humanity's future."
-Natasha Vita-More, PhD, Author Transhumanist Manifesto, Co-creator of the Transhumanist Movement, Founder, Center for Transhumanist Studies

"Wow! Without question, this is Ray Kurzweil's best book to date. It's a page-turner with unimpeachable gravitas and authority on genetics, nanotechnology, robotics, and the future. He handles the pluses and minuses of the near-term impact of the coming singularity on jobs with great skill and tact. The book is a towering accomplishment."
- Harry George, entrepreneur, venture capitalist, Cofounder of Interleaf, Inc., Cofounder and Managing Partner, Solstice Capital

"Ray Kurzweil has one of the great American minds. It's a beautiful experience to talk back and forth with Ray because if you don't listen carefully, you could miss out on his 'pearls of wisdom.'"
-Suzanne Somers, actress, singer, comedienne, NYTimes bestselling author, entrepreneur, and lecturer

"Drawing on scientific reports, research studies, and interviews with experts, Kurzweil observes the long term trends in order to ponder the promises and perils of AI when it comes to nuclear weapons and genetic engineering. To readers interested in AI and biotechnology, Kurzweil offers insight as he breaks down the complex topic and addresses the ethical issues surrounding its use and place in society."

Praise for Ray Kurzweil

"Ray Kurzweil is the best person I know at predicting the future of artificial intelligence."
-Bill Gates

"Not everything that Kurzweil predicts may come to pass, but a lot of it will, and even if you don't agree with everything he says, it's all worth paying attention to."
-The Philadelphia Inquirer

"[Ray Kurzweil] has a way of tackling seemingly overwhelming challenges with an army of reason."
-Rafael Reif, president, MIT

"Kurzweil paints a tantalizing-and sometimes terrifying-portrait of a world where the line between humans and machines has become thoroughly blurred."

"The restless genius."
-The Wall Street Journal

"The ultimate thinking machine."

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The Singularity Is Nearer by Ray Kurzweil: 9780399562761 | PenguinRandomHouse.com: Books

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AN INSTANT NEW YORK TIMES BESTSELLER ONE OF TIME'S 100 MOST INFLUENTUAL PEOPLE IN ARTIFICIAL INTELLIGENCE The noted inventor and futurist's successor to his landmark book The Singularity...

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"Co-Intelligence: Living and Working with AI" by than Mollick

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"Co-Intelligence: Living and Working with AI" by than Mollick

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Something new entered our world in November 2022 — the first general purpose AI that could pass for a human and do the kinds of creative, innovative work that only humans could do previously. Wharton professor Ethan Mollick immediately understood what ChatGPT meant: after millions of years on our own, humans had developed a kind of co-intelligence that could augment, or even replace, human thinking. Through his writing, speaking, and teaching, Mollick has become one of the most prominent and provocative explainers of AI, focusing on the practical aspects of how these new tools for thought can transform our world.

In Co-Intelligence, Mollick urges us to engage with AI as co-worker, co-teacher, and coach. He assesses its profound impact on business and education, using dozens of real-time examples of AI in action. Co-Intelligence shows what it means to think and work together with smart machines, and why it’s imperative that we master that skill.

Mollick challenges us to utilize AI’s enormous power without losing our identity, to learn from it without being misled, and to harness its gifts to create a better human future. Wide ranging, hugely thought-provoking, optimistic, and lucid, Co-Intelligence reveals the promise and power of this new era.

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"[ Co-Intelligence] is a solid explainer. Convincing...a blueprint...smart and well informed..."
- The Wall Street Journal

"Generative AI has an oracle, and his name is Ethan Mollick. Lucky you: our oracle has written a lucid, succinct, and eminently practical guide to navigating the revolution that is right now unfolding at an astonishing pace. Co-Intelligence is the very best book I know about the ins, outs, and ethics of generative AI. Drop everything and read it cover to cover NOW."
- Angela Duckworth, New York Times bestselling author of Grit

"Ethan Mollick has been a leader with the hands-on exploration of AI's evolving capabilities. In Co-Intelligence, he masterfully navigates the landscape of AI advancements with insightful and informative commmentary, presented with remarkable clarity and precision. This book is an invaluable resource, offering a comprehensive gide to current trends and future expectations in AI. I wholeheartedly recommend it to anyone looking to deepen their understanding of this rapidly evolving technology."
- Eric Horvitz, Chief Scientific Officer, Microsoft

"In today's cacophony of 'AI hot takes', Mollick has distinguished himself as the leading voice of reason on the implications of AI for work and education. Anyone seeking a basis of practical knowledge to understand the coming massive changes should read this insightful book right now."
- Reid Hoffman, Co-Founder of LinkedIn and Inflection AI

" Co-Intelligence brilliantly captures the essence of our evolving relationship with AI. With lucid explanations and useful examples, Mollick shows how we can enlist this technology's astonishing power to build a more intelligent and humane future."
- Daniel H. Pink, New York Times bestselling author of The Power of Regret, Drive, and A Whole New Mind

"Embark on a transformative journey with Co-Intelligence, Ethan Mollick's page-turning must-read for those at the helm. Mollick's work will help CEOs and leaders harness AI's enormous potential as a catalyst for elevating the capabilities of any team and any organization. Brilliant, authoritative, and educational."
- Jim M. Citrin, Leader, Spencer Stuart CEO & Board Practice, author of You're in Charge, Now What? and The 5 Patterns of Extraordinary Careers

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Co-Intelligence by Ethan Mollick: 9780593716717 | PenguinRandomHouse.com: Books

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AN INSTANT NEW YORK TIMES BESTSELLER From Wharton professor and author of the popular One Useful Thing Substack newsletter Ethan Mollick comes the definitive playbook for working, learning, and living...

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"The Coming Wave: Technology, Power, and the Twenty-first Century's Greatest Dilemma" by Mustafa Suleyman

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"The Coming Wave: Technology, Power, and the Twenty-first Century's Greatest Dilemma" by Mustafa Suleyman

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A warning of the unprecedented risks that AI and other fast-developing technologies pose to global order, and how we might contain them while we have the chance—from a co-founder of the pioneering artificial intelligence company DeepMind

We are approaching a critical threshold in the history of our species. Everything is about to change.

Soon you will live surrounded by AIs. They will organise your life, operate your business, and run core government services. You will live in a world of DNA printers and quantum computers, engineered pathogens and autonomous weapons, robot assistants and abundant energy.

None of us are prepared.

As co-founder of the pioneering AI company DeepMind, part of Google, Mustafa Suleyman has been at the centre of this revolution. The coming decade, he argues, will be defined by this wave of powerful, fast-proliferating new technologies.

In The Coming Wave , Suleyman shows how these forces will create immense prosperity but also threaten the nation-state, the foundation of global order. As our fragile governments sleepwalk into disaster, we face an existential unprecedented harms on one side, the threat of overbearing surveillance on the other.

Can we forge a narrow path between catastrophe and dystopia?

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We are approaching a critical threshold in the history of our species.

Everything is about to change.

This is a story of 21st century technology, why it matters so much and what we can do to make it work for everyone.

Soon we will live surrounded by AIs. They will carry out complex tasks-operating businesses, producing unlimited digital content and running core government services.

The coming wave will make this next decade the most productive in history. It represents nothing less than a step change in human capability and human society, introducing both risks and innovations on an awesome scale.

This is what is coming.

This groundbreaking new book from AI entrepreneur Mustafa Suleyman is a must-read guide to the technological revolution just starting, and the transformed world it will create.

In it Suleyman establishes "the containment problem"-the task of maintaining control over powerful technologies-as the essential challenge of our age.

"A fascinating, well-written, and important book."

Yuval Noah Harari

Bestselling author of Sapiens

"An excellent guide for navigating unprecedented times."

Bill Gates

Co-founder of Microsoft

"Rich with interesting facts, arresting arguments, and compelling observations; it is essential reading."

Daniel Kahneman

Bestselling author of Thinking, Fast and Slow

Over the last year, we've seen the first signs this wave of powerful, fast-proliferating new technologies is breaking. These tools will help address global challenges and create vast wealth-but also upheaval on a once unimaginable scale. Understand this wave, and you will begin to understand the future.

In The Coming Wave, Mustafa Suleyman shows how these forces threaten the nation state itself, and how we can successfully contain history's most powerful technologies to avoid catastrophic or dystopian outcomes. Amidst unprecedented peril and extraordinary promise, here is both a stark warning and a hopeful guide to where society goes next.
Glossary of Terms

Mustafa Suleyman is the co-founder and CEO of Inflection AI. Previously he co-founded DeepMind, one of the world's leading artificial intelligence companies.

After a decade at DeepMind, Suleyman became vice president of AI product management and AI policy at Google.

When he was an undergraduate at Oxford, Suleyman dropped out to help start a non-profit telephone counseling service. He lives in Palo Alto, California.

Michael Bhaskar is a writer and publisher based in the UK, author of The Content Machine, Curation and Human Frontiers.

Praise for The Coming Wave

"A heartfelt and candid exploration of what the future may hold for us... Eloquently articulated."
The Guardian

"Dazzling... You have by now read a great deal of both hype and doom-mongering on the subject [of AI]. But Suleyman's is the book you cannot afford not to read... Brilliant."
Niall Ferguson
Bloomberg

"[Suleyman] states his case with clarity and authority, and the result is a worrying, provocative book... An informative yet disturbing study and a clear warning from someone whose voice cannot be ignored."
Kirkus Reviews (starred review)

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This groundbreaking new book from AI entrepreneur Mustafa Suleyman is a must-read guide to the technological revolution just starting, and the transformed world it will create.

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Prompt Engineering and Advanced ChatGPT (free edX course)

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Prompt Engineering and Advanced ChatGPT (free edX course)

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This course is designed to teach advanced techniques in ChatGPT, an artificial intelligence chatbot developed by OpenAI and launched in November 2022. We’ll cover advanced techniques for prompting ChatGPT, applications for multiple use cases, integrating it with other tools, and developing applications on top while considering its limitations.

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Artificial Intelligence News is a leading resource for the latest updates and insights into the artificial intelligence industry. This site offers detailed coverage on AI developments, including industry news, technological advancements, and expert analysis, making it a valuable resource for anyone interested in staying informed about the AI sector.

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Artificial Intelligence News provides the latest AI news and trends. Explore industry research and reports from the frontline of AI technology news.

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Citing AI-generated content, Chicago Manual of Style, 18th edition

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Citing AI-generated content, Chicago Manual of Style, 18th edition

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Citing AI-generated images, Chicago Manual of Style, 18th edition

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Citing AI-generated images, Chicago Manual of Style, 18th edition

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How to properly cite AI-generated content according to the Chicago Manual of Style, 17th edition

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How to properly cite AI-generated content according to the Chicago Manual of Style, 17th edition

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Q. How do you recommend citing content developed or generated by artificial intelligence, such as ChatGPT? Many scholarly publishers are requiring its identification though also requiring human authors to take responsibility for it and will not permit the AI to have "authorship."

A. You do need to credit ChatGPT and similar tools whenever you use the text that they generate in your own work. But for most types of writing, you can simply acknowledge the AI tool in your text (e.g., "The following recipe for pizza dough was generated by ChatGPT").

If you need a more formal citation-for example, for a student paper or for a research article- a numbered footnote or endnote might look like this:

1. Text generated by ChatGPT, OpenAI, March 7, 2023, https://chat.openai.com/chat.

ChatGPT stands in as "author" of the content, and OpenAI (the company that developed ChatGPT) is the publisher or sponsor, followed by the date the text was generated. After that, the URL tells us where the ChatGPT tool may be found, but because readers can't necessarily get to the cited content (see below), that URL isn't an essential element of the citation.

If the prompt hasn't been included in the text, it can be included in the note:

1. ChatGPT, response to "Explain how to make pizza dough from common household ingredients," OpenAI, March 7, 2023.

If you've edited the AI-generated text, you should say so in the text or at the end of the note (e.g., "edited for style and content"). But you don't need to say, for example, that you've applied smart quotes or adjusted the font; changes like those can be imposed silently (see and 13.8).

If you're using author-date instead of notes, any information not in the text would be placed in a parenthetical text reference. For example, "(ChatGPT, March 7, 2023)."

But don't cite ChatGPT in a bibliography or reference list unless you provide a publicly available link (e.g., via a browser extension like ShareGPT or A.I. Archives). Though OpenAI assigns unique URLs to conversations generated from your prompts, those can't be used by others to access the same content (they require your login credentials), making a ChatGPT conversation like an email, phone, or text conversation-or any other type of personal communication (see and 15.53).

To sum things up, you must credit ChatGPT when you reproduce its words within your own work, but unless you include a publicly available URL, that information should be put in the text or in a note-not in a bibliography or reference list. Other AI-generated text can be cited similarly. Check back with us for updates on this evolving topic.

For some considerations on the use of AI in scholarly publishing and the responsibilities of authors, start with this position statement on authorship and AI tools from COPE (Committee on Publication Ethics).

[ Editor's update: Original answer updated to put the publisher before the date and to acknowledge tools like ShareGPT and A.I. Archives.]

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The Chicago Manual of Style, 17th Edition

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Find it. Write it. Cite it. The Chicago Manual of Style Online is the venerable, time-tested guide to style, usage, and grammar in an accessible online format. ¶ It is the indispensable reference for writers, editors, proofreaders, indexers, copywriters, designers, and publishers, informing the editorial canon with sound, definitive advice.

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AI Literacy in Teaching and Learning: A Durable Framework for Higher Education

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AI Literacy in Teaching and Learning: A Durable Framework for Higher Education

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"AI Literacy in Teaching and Learning" is comprehensive framework aimed at enhancing AI literacy within higher education. This framework is crucial for enabling students, faculty, and staff to effectively and ethically engage with AI technologies in both academic and professional settings. The report emphasizes the importance of understanding AI fundamentals, critically evaluating AI applications, and maintaining vigilance against misuse and bias to foster a well-rounded AI literacy.

The framework is divided into tailored competencies and outcomes for different groups within the educational community:

Students are expected to gain an understanding of AI concepts and apply these ethically within their academic work.

Faculty are guided on integrating AI into teaching, research, and administrative responsibilities.

Staff are provided with the tools to support AI implementation in administrative and operational processes.

The approach advocates for a proactive fostering of AI literacy, which is essential to mitigate risks, maximize potential benefits, and maintain a competitive edge in a rapidly evolving technological landscape. The summary calls for institutions to adopt and adapt this framework according to their specific needs, develop comprehensive training programs, integrate AI literacy into existing curricula, and establish policies for responsible AI use.

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AI Literacy in Teaching and Learning: Executive Summary

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Students, faculty, and staff need to understand the fundamentals of AI to able to effectively use AI tools and evaluate the outputs.

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*must log in with a Google account to access the NotebookLM

AI agents are systems that leverage AI models to accomplish given goals. Unlike standalone AI models like large language models, AI agents possess capabilities beyond text generation or prediction. They are able to:

Interact with their environment: Taking actions and responding to changes in their surroundings.

Access external tools and data: Utilizing APIs, databases, and even other AI models to gather information and execute tasks.

Maintain memory: Retaining information about past actions and experiences to inform future decisions.

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Not your computer? Use Guest mode to sign in privately. Learn more about using Guest mode

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"AI Snake Oil" is a Substack newsletter authored by computer scientists Arvind Narayanan and Sayash Kapoor. It critically examines the capabilities and limitations of artificial intelligence, aiming to demystify AI technologies and debunk exaggerated claims.

The newsletter provides insights into distinguishing between effective AI applications and those that are overhyped or ineffective, fostering a more informed understanding of AI's role in society.

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What makes AI click, what makes it fail, and how to tell the difference. Click to read AI Snake Oil, a Substack publication with tens of thousands of subscribers.

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W&L Generative AI Syllabus Statement Tool

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W&L Generative AI Syllabus Statement Tool

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The Generative AI Syllabus Statement Tool assists W&L faculty in crafting customized syllabus statements regarding the use of generative AI tools in their courses.

This interactive, web-based decision tree guides instructors through a series of choices to define their policies on AI usage, ranging from complete prohibition to mandatory integration. The tool offers tailored language and considerations for each policy stance, ensuring clarity and alignment with the instructor's educational objectives.

By providing a structured approach, it helps faculty effectively communicate expectations about AI tool usage to W&L students, fostering transparency and understanding.

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myProse (an AI-enhanced online writing studio)

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myProse (an AI-enhanced online writing studio)

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myProse is an AI-enhanced online writing studio developed by Carnegie Mellon University's Department of English. It offers a suite of generative and assessment tools designed to support writers throughout the entire writing process, from initial drafting to final revisions. Unlike traditional writing methods that begin with a blank page, myProse provides a structured writing task definition, including a common outline and task-specific questions that align with reader expectations.

A key feature of myProse is the "Notes-to-Prose" tool, which translates a writer's notes into coherent prose without introducing new ideas. This functionality reduces the cognitive load associated with sentence construction, allowing writers to concentrate on higher-level planning and organization. As the draft progresses, myProse offers AI-based assessment tools that provide automated feedback and interactive visualizations of the writer's decisions. These tools enable writers to evaluate their work from multiple perspectives, including reader expectations, logical flow, content coverage, and sentence clarity.

The development of myProse builds upon over 25 years of research into digital writing environments that incorporate natural language processing and interactive visualization. This research led to the creation of DocuScope, a suite of experimental digital tools aimed at offering automatic feedback to maintain writer engagement without constant teacher supervision. The strategic integration of generative AI in myProse aligns with the vision of placing human writers at the forefront of the writing process while democratizing access to enhanced writing tools for a broader audience.

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OpenAI's A Student’s Guide to Writing with ChatGPT

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OpenAI's A Student’s Guide to Writing with ChatGPT

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OpenAI's A Student’s Guide to Writing with ChatGPT provides practical strategies for students to enhance their writing skills through active engagement with the tool. The guide demonstrates how ChatGPT can assist with tasks like automating citation formatting, which frees up students to focus on content creation, and gaining a foundational understanding of new topics by offering concise overviews. It also highlights how ChatGPT can identify relevant sources, suggest scholars, and recommend search terms, while emphasizing the importance of verifying information.

Additionally, the guide explains how ChatGPT can help clarify complex topics by answering specific questions, enabling deeper comprehension. It offers support in improving the structural flow of writing by reviewing outlines and giving feedback on the logical progression of ideas. Reverse outlining, a technique where ChatGPT summarizes each paragraph's main point, helps students assess their essay's overall structure.

The guide encourages students to use ChatGPT for Socratic dialogue, where they can refine their ideas by engaging in intellectual conversations. It also illustrates how ChatGPT can challenge a student’s thesis by presenting counterarguments, ultimately helping to strengthen their argument. While the guide emphasizes ChatGPT’s potential as a supportive tool in the writing process, it underscores that it should complement, not replace, the student’s own efforts in learning and creating written work.

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This article provides a comprehensive roadmap for smaller higher ed institutions to effectively implement and leverage AI despite limited resources. The authors outline five fundamental activities: coordinating AI initiatives, fostering learning communities, establishing governance frameworks, implementing solutions, and continuously reviewing outcomes. The piece specifically addresses unique challenges faced by smaller institutions, such as budget constraints and limited technical staff, while offering practical solutions like using API-based tools and focusing on targeted implementations. The article features real-world examples from institutions like Ithaca College and Dickinson College, demonstrating how smaller schools can successfully integrate AI to enhance student services, improve operational efficiency, and maintain their commitment to personalized education.

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A Road Map for Leveraging AI at a Smaller Institution

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Smaller institutions and others may not have the staffing and resources needed to explore and take advantage of developments in artificial intelligenc

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"Ready or not, chatGPT (the newest version of OpenAI’s impressive AI technologies) is now in your classroom. It can write papers, essays, and poems. It can create art and write computer code in many languages. This is not however the time to panic; it is the time to focus on the value you offer students as their instructor.

Below are some easy to implement suggestions that will help you prepare for the upcoming semester."

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Including 10 AI-ready assignment and activity ideas.

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MLA-CCCC Joint Task Force on Writing and AI Working Paper

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MLA-CCCC Joint Task Force on Writing and AI Working Paper

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"This working paper explains the relevant history, nomenclature, and key concepts to our profession. Under this framework, the paper declares the broad risks and potential benefits of artificial intelligence to language, literary, and writing scholarship and instruction and the ways generative AI will affect all of us in higher education: students, scholars, instructors, administrators, and staff members. The paper then suggests principles and recommendations for creating policies, guidelines, and practices that draw on our strengths as teachers and scholars. "

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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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How to apply prompt engineering to effectively work with large language models, like ChatGPT

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For law, GAI threatens the integrity of the take-home exams that are a common feature of many courses. For foundational courses, particularly in the first year core, use of in-person written exams with restricted access to the internet and ability to access GAI is recommended to ensure the validity and integrity of exams. At the same time, GAI tools are in increasingly widespread use in the practice of law and it is important that this shift be addressed in legal education. This could be done through addressing use of GAI in legal practice explicitly in second and third year classes, including examination of these tools in legal research and writing courses. The strong ethical core of the discipline and practice of law should be reflected in how GAI is addressed. Appendix G elucidates further.

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Back to the Report: Generative Artificial Intelligence for Education and Pedagogy
The development of generative AI tools poses particular challenges for legal education due to the structure of the curriculum, common methods of testing, and professional norms.

Opportunities

Generative AI is being increasingly widely used in the practice of law and this trend can be expected to continue. Law students being trained as professionals in the field need to be prepared for the impact of generative AI on the practice of the profession. A historical analogy can be made to the revolution in how case law was researched when legal databases such as Westlaw and Lexis supplanted the traditional use of books to Shepardize cases. Similarly, the lawyer of the future will need to know how to use generative AI tools effectively and ethically to succeed in practice. Advanced coursework that addresses and develops knowledge about the use of GAI in the practice of law should be part of legal education. Law is a profession with strong ethical principles guiding practice and ethically appropriate professional use of GAI will need to similarly be addressed in legal education.

Generative AI may have uses in teaching legal writing through its ability to generate illustrative examples.

This could allow generative AI tools to serve as a type of tutor for the law student in learning legal writing.

Concerns

The development of analytical skills and core legal knowledge through the foundational course work of the first year of the program is a key part of the law school curriculum. Use of generative AI in this setting risks undermining that foundational element of legal education (in several tests, ChatGPT passed the Bar Exam).

Take-home exams, which are widely used in law courses, are particularly vulnerable to the misuse of generative AI due to their unsupervised nature. Students who might be tempted to use generative AI inappropriately to assist in completing assignments may be led further in this direction by take-home exams that create greater time pressures. A return to in-person exams where the setting can control access to outside assistance may be necessary to ensure integrity in the testing process.

Recommendations

The use of generative AI should be proscribed in foundational courses in the legal curriculum where development of key analytical skills and knowledge is critical. Maintenance of the central role of final exams for assessment of learning in the first year curriculum in law will require adjustments to respond to the potential impact of GAI on this form of evaluation. Take-home exams should be avoided as an evaluation method due to the heightened danger of generative AI leading to academic integrity violations in this setting. In-person exams are a preferred option due to the ability to control and prevent outside assistance in that setting.

Judicious use of GAI tools could be incorporated into legal research and writing courses to help students master skills.

Ethical and practical issues involving the use of GAI in the practice of law should be explicitly addressed in legal education, so that students are prepared for it when they enter the profession.
Back to the Report: Generative Artificial Intelligence for Education and Pedagogy

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Back to the Report: Generative Artificial Intelligence for Education and Pedagogy The development of generative AI tools poses particular challenges for legal education due to the structure of the curriculum, common methods of testing, and professional norms. Generative AI is being increasingly widely used in the practice of law and this trend can be expected to continue.

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GAI is already used heavily in industry to assist in coding through applications like GitHub Copilot. Opportunities for LLMs in programming education exist in: the generation of code from specifications (text to code), the generation of ancillary tools such as tests (code to code), and the generation of explanations or suggestions (code to text). However, the concern is that students will rely on GAI and will not learn the skills necessary to generate working, understandable, and updatable code. They may be unable to move beyond the solutions favored by an AI system, to identify and fix problems, or at worst, to even recognize that alternatives exist. We recommend using GAI in advanced courses as a tutor or helper in programming, but not as the sole creator of code. See Appendix F for details.

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Back to the Report: Generative Artificial Intelligence for Education and Pedagogy
Generative AI has shown impressive ability to solve coding tasks, particularly when provided a clear specification of the goals. Researchers have shown that the current generation of models can already solve most of the programming assignments given in introductory classes. As such, programming is one of the areas where it is most critical for instructors to both be aware of the abilities of current models and to incorporate these tools into their future planning.

Current LLMs are trained extensively on program code repositories, so their impact on programming education is of particular concern. Programming is a challenging and game-changing skill. But learning to program requires understanding many abstract concepts, and an extensive knowledge of languages, package APIs, and system environments. Generative AI can both help and hinder these learning objectives.

Two factors are significant in how students relate to programming courses and what support they need. First, courses vary in the expected programming skill: a student just learning about variables, functions, and control structures has different needs than a student who is comfortable with programming. Second, students vary in motivation: some are intrinsically motivated and aim to increase programming skill, while others are extrinsically motivated and aim to build tools or products that happen to require programming.

In an introductory programming course, the goal is to help emerging programmers gain core skills such as reading code, mentally tracing execution and variable states, and identifying bugs. For such a course, GAI code generation is actively harmful to student learning, because students can generate code that meets specifications without understanding it. Rather, the better use of GAI would be as an "assistant", offering suggestions and helping students get unstuck. In a more application-oriented programming course, such as web development or introductory statistics, direct code generation may be a way to get students to a high level of performance quickly, creating curiosity and empowerment rather than boredom and confusion. Finally, in more advanced classes, students face complicated APIs and frameworks and alternative programming languages. In such cases, generative AI can be a "turbocharger" that decreases time spent consulting tutorials and documentation.

Opportunities

Three primary opportunities for LLMs in programming education are:

generation of code from specifications (text to code)

generation of ancillary tools such as test cases (code to code)

generation of explanations or suggestions (code to text)

The relative value of these interventions depends on course objectives. There are ample opportunities for teaching the use of LLMs as a programming tool, both to facilitate more efficient programming and to provide guidance to students learning the discipline. It is clear that generative tools will be a valuable and inescapable part of programming practice.

The first area of opportunity is to use generative tools to facilitate students' building of more complex artifacts as part of their training. Building realistic software systems is challenging and often requires writing significant amounts of "boilerplate" code to interact with external libraries and tools. As such, even in programming intensive courses, it is common to simplify systems or provide large amounts of scaffolding code. When combined with careful instruction, the ability of LLMs to efficiently generate code for specific use cases may make it easier to have students move closer to the realistic environments. For example, current systems can generate code for basic user interfaces from user descriptions. While this may not be critical for the material in the course itself, improving the appearance of the final output can increase student motivation.

A related area of opportunity is supporting software engineering and encouraging best practices for software development. For example, students are often taught to produce detailed specifications and provide unit tests for their systems, but many non-intro courses do not require students to carry out this activity as part of assignments. LLMs can be effective at generating test cases from specifications with much less manual effort from students. This ability provides opportunities to incorporate these best practices into courses directly.

Finally, while it is not yet possible with current systems, future generative systems may open up programming and data science to those without knowledge of the programming language. This presents a remarkable opportunity to teach students for whom programming presents a large barrier of entry, but who nonetheless have an interest in using computer science tools for their applications. Students will be able to build useful systems for application-oriented projects without studying introductory programming.

Generative systems are also potentially a valuable tool for supplementing the teaching process of programming classes. It has already been reported that Harvard's CS50 course is planning to add a tutor bot based on ChatGPT. Students benefit greatly from having teaching assistants answer questions to get them unstuck. However, they can find it intimidating to ask questions or feel too behind to know what to ask. Their reticence leads to widespread use of online resources that are poorly curated and often misleading. GAI can potentially provide better targeted answers to specific issues in code than online forums. In addition to being able to retrieve specific issues that others have experienced, generative models can provide targeted responses to users' code and errors. Current models already provide explanations of code snippets, corrections of mistakes, and guidance for where to look in codebases. Additionally, there are numerous tools to integrate this guidance into code editors and search engines. With the caveat that advice as currently provided is poorly targeted to the specific level and learning objectives of students, this capability could improve the learning and debugging process.

Concerns

At its best, programming is empowering: a few simple primitives can be combined to create an infinite variety of customized solutions. Predictions of the demise of programming due to GAI seem premature. GAI does not replace the core abilities necessary to generate working, understandable, and maintainable code. The primary risk of GAI is that it may prevent students from engaging with the full expressive power of coding. Students may be able to achieve powerful results with little skill, but be unable to move beyond the solutions favored by an AI system, to identify and fix problems, or at worst, to even recognize that alternatives exist.

While intro courses emphasize small, well-structured problems, a large part of software engineering jobs is understanding and managing very large complex code bases written by others. We see this skill becoming even more important in a world where some fraction of code is written by AI systems. These heterogeneous code bases, written both by humans and AI, will require extra care to understand the limitations of each type of coder. Cornell will need to continue to train students to manage these systems, and it would be a major negative outcome if the existence of AI coders led to an assumption from students that it is no longer important to navigate and reshape large code bases.

Currently, there is a high likelihood that LLMs are widely being used as a shortcut for students in programming-heavy classes. LLMs are very good at many aspects of programming, and are particularly adept at the type of self-contained, short programs in intro programming classes, because they have been trained on many similar programs. They are also adept at translating program specifications directly into working code. For this reason, it is important that instructors convey the learning objectives of their courses in a way that motivates students to learn the underlying languages themselves.

We caution instructors against aiming to outwit the ability of models to solve problems by underspecifying details of assignments; these techniques are likely only beneficial in the short term, and future models will be able to work around these issues. In some cases, it may be possible to have students specify assignments themselves, which at least requires understanding the request and being able to check that the code is correct.

Even in instances when a system is able to generate effective code for an assignment, there is concern that systems generate "Frankenstein" code by gluing together different techniques for solving a problem. This may lead to a short-term answer, but will be hard to maintain and work with in a large-scale code library. This is a good opportunity for teaching elements of high-level engineering through either critiquing the inconsistencies of functions output by a model, or by learning to reprompt a system to produce cleaner and more consistent outputs.

Recommendations

As with other areas, the key goal is to convey to the student why it is critical to learn how to read, understand, and write code, as opposed to the goal of simply producing it. If LLMs are viewed as an assistant in the process of learning to write and edit code, they can be powerful assistants to learning and improving as a programmer. However, if they are treated as a black-box oracle, they can hinder the early developmental phase of coding. In this sense, they are like a friend with experience in the subject. Used as a tutor, they can provide guidance; used as an answer key, they can be a dead end.

The other recommendation is that it is likely there will be no long-term method to prevent use of GAI. The conflict is that homework assignments in programming must be specified in sufficient detail for students to be sure they have done the work correctly and for course staff to grade their work, but any specification that is clear enough for these purposes will necessarily also be solvable by GAI tools. While it may be possible to craft specifications that thread this needle in the short term, the variety of programming chatbots available in the future will make this a time-consuming and ultimately futile pursuit. Course staff will need to know how current bots interact with existing and future assignments, and understand the capabilities they provide to students using them.

Sample assignments

Debugging skills - preliminary to coding assignments in intro classes

Provide students with snippets of code that have clear errors or issues with them or strange error messages. Teach how to use GAI as a debugging tool to help out in these situations.

Ask students to:

Run the snippets of code through an interpreter or compiler.

Diagnose the issue themselves based on the output and write a short description of the problem.

Run the code snippet through GAI and construct a prompt to ask for its description of the problem.

Compare the output of the model with the written description: was it sufficient, and does it lead to a different code change than the original student suggestion?

Specification skills - assignment in a class emphasizing software engineering

In a class where students are developing their own design and specifications for systems, assignments can explore the use of code generation as a way of translating specifications to code.

Ask students to:

Develop a human-readable specification for a software system in written language, making clear user requirements and expectations.

Write a prompt that communicates to a GAI system these specifications and examples of test cases that would enforce a subset of them.

Run the prompt through a GAI system to produce test cases for the underlying specification.

Assess the output code to argue whether or not it was able to provide full coverage of the expected test cases. Produce examples that either incorrectly pass or fail the tests.

If necessary, reiterate on B.

Simulation and visualization - assignment in a non-programming class

In this assignment students will utilize code generation and graphing as a way to run simulations based on natural language descriptions that would have traditionally required more advanced programming knowledge.

Ask students to:

Describe a scenario relevant to the class, for example:

In a statistics class, the results of summing up weighted coin flips

In a linguistics class, the co-occurence of words in a document

In a biology class, the result of random mutations

Write a prompt that describes the mechanism of the event in detailed language and ask for a graph describing the results of the simulation.

Run the prompt through GAI and then run the resulting code to see the output graph of the system.

Write a description of the result, analyzing whether the graph seems to correctly represent the underlying phenomenon and what aspects of the code either agree or disagree with expectations.

This assignment has potential pitfalls as it asks students to trust code they may not understand. However, the use of graphing and knowledge of the underlying subject should equip them with tools to at least notice and assess errors. We note as well that even in classes where students do know programming often convince themselves incorrect implementations are correct.

High-level LLM-assisted design - assignment in an upper level class

Provide students with an extremely detailed assignment specification describing the full requirements of the system. The goal of the assignment is to focus on system design with the use of GAI.

Ask students to:

First run the specification as is through a GAI system to produce an output code base. This can be done through the API to produce a full longform output.

Perform a code review of the AI output. Which parts are good, and which are poorly designed, inefficient, or confused.

Construct a high-level design plan for the codebase: for example, giving the module structure and describing high-level implementation choices.

Produce a detailed prompting plan describing this structure to the GAI system.

Regenerate the code base, and submit the assignment.

Student output will be evaluated based on this output as if they had written it directly. Goal being to convey responsibility for systems even with the use of AI tools.

Back to the Report: Generative Artificial Intelligence for Education and Pedagogy

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Appendix F: Courses in programming | Center for Teaching Innovation

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Back to the Report: Generative Artificial Intelligence for Education and Pedagogy

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Appendix E: Mathematics, physical sciences, and engineering

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Appendix E: Mathematics, physical sciences, and engineering

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Technical and mathematical courses have adjusted well in the past to incorporate new technologies, such as computing and visualization tools. GAI may provide similar opportunities to enhance education in the space of mathematics, physical sciences, and engineering. For example, students and instructors may use LLMs in an explanatory capacity or use LLMs to synthesize code to support data analysis and visualizations. Some of the biggest concerns with current systems is their inaccuracy (“hallucinations”) and circular reasoning. Instructors should make themselves aware of the capabilities of current systems and the fast changing behavior of these systems on mathematical and engineering problems. We recommend educating students on the capabilities and limitations of these systems, prohibiting their use where basic skills need to be developed, and encouraging their use in cases where LLMs can improve student learning. Appendix E gives detailed examples of use cases for courses in this domain.

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Back to the Report: Generative Artificial Intelligence for Education and Pedagogy
Opportunities

Technical and mathematical courses have adjusted well in the past to incorporate new technologies. For example, the widespread infusion of computing and visualization tools such as Matlab over the past two decades has allowed instructors to ask more of students and to assign exercises to help students gain deeper understanding. As a complement to solving a differential equation, for instance, students can plot out the solution and visualize how results depend on key model parameters. While GAI is not precisely analogous to these methods, there are similar opportunities to enhance education in the space of mathematics, physical sciences, and engineering. These opportunities include utilizing LLMs in an explanatory capacity for technical topics and supplementing technical topics with the ability to synthesize code to support data analysis and visualizations.

While current GAI systems struggle with producing novel mathematical reasoning, they are adept at retrieving and summarizing complex arguments on technical topics. Although they do still often hallucinate or skip steps in the process, a careful and curious student can use them as a supplement to explore a topic. As an example, we consider open-ended physics questions. An instructor might make an assignment that asks a student to have a dialogue with an LLM about a specific question, e.g., "would I weigh more at the equator or the north pole?". The student could then turn in the transcript plus a reflection about what they have learned. LLMs can also be used in the reverse direction to assess the reasoning of the student in a devil's advocate-like scenario. If a student is asked to make an argument with technical justification, they could be asked to utilize an LLM to assess their own reasoning and justify why or why not the LLM's critique of their technical argument is valid.

In their current form, LLMs present a nice opportunity for data analysis and visualization. Current models are excellent at generating complex graphs and visualizations from user specifications. For example, in an assignment collecting experimental data, an instructor could ask for a graphical presentation of results in a precise manner. Students could then use an LLM in conjunction with a coding environment like Jupyter to produce and iterate on their output. This assignment is possible even in courses where students do not have advanced programming knowledge, as they can observe the output of the system. Similar approaches are also possible for data science and analysis assignments, where LLMs allow students to query and filter data without having to fully understand code output.

Concerns

While the current generation of LLM systems are not yet capable of solving unseen mathematics problems due to limits on their reasoning and computational power, they are extremely capable of replicating and combining previously seen solutions to commonly used problems. As such, it is critical that instructors are aware not only of the capabilities of current systems but also of their behavior on specific problems.

The most transparent short-term concern for technical classes is the student use of LLMs to shortcut learning objectives on homework assignments. Specifically, if students become accustomed to using LLMs in intro classes where it returns correct, or even partially correct answers, they may become reliant on them in more challenging classes or in the workforce. We caution that the capabilities of these systems, specifically in terms of mathematical reasoning, are increasing rapidly, and that just in the last several months systems have improved greatly in this area. For classes where material is solvable by these systems, and the instructor believes this is problematic, we recommend both openly discussing the benefits of manual computation and course learning objectives with the class, as well as establishing clear guidelines about its use with the course. Additionally, instructors may consider altering assignment specifications or including additional requirements that deviate from standard versions of the problems.

Current LLM systems are more than willing to provide answers for questions that they are nowhere close to being able to solve. For example, when asked to solve a quadratic equation, the current generation of ChatGPT will proceed through the steps of solving by producing hallucinated intermediate values, despite not having access to a calculator. A striking aspect of these responses is that they are written in a tone that mimics the confidence of a textbook. The mismatch between the tone and the technical ability can lead to overconfidence from the reader. For this concern, we recommend an assignment in intro classes that requires students to go through the exercise of understanding these issues. For early proof-based courses, we suggest that students be asked to "provide a plausible proof generated by a language model and to find gaps or mistakes in the proof."

Recommendations

Our overall recommendation is to encourage productive engagement with GAI while highlighting potential pitfalls.

For productive student use, we encourage communicating regularly and clearly to students about when these tools can and should be used. Just like we ask students to solve differential equations by hand when Wolfram Alpha can solve it, instructors should make clear the need to practice and apply concepts, principles, and methods.

Even when LLMs can be used, it should be made explicitly clear to students that they are responsible for their own work, even if they used AI to generate all or part of a solution. If the AI answer is wrong and the student turns it in, the student is still responsible for the submitted answer. Provide examples where they can see how it fails in practice.

For instructors, we encourage understanding the precise abilities and limitations of these models. Submit problems to a LLM before assigning them to students to see what it can solve. What mistakes does it make? What grade would it get? Is this an assignment you still want to give and if so, do you ban the use of LLM, or allow its use with attribution? For example, in an engineering mathematics course where learning to solve certain types of equations is a learning outcome that will be assessed by in-person exams, you may wish to ban the use of GAI. But if the assignment is to use solutions to probe or to understand the physical problem at hand, you might ask students to attribute the use of AI to solve the problem.

Sample assignments

We demonstrate some examples that can be posed to GAI (ChatGPT in this case) and the responses from GAI to help illustrate educational opportunities and issues, (ChatGPT responses edited for brevity). These could be used as written HW assignments or as points of discussion in recitation sections.
1. Conceptual question in physics
Ask students to:

A. Prompt GAI with a question such as: "If I weigh myself at the equator will I weigh more, less or the same as at the North Pole?"

ChatGPT (for example) will return:

B. Then ask students to perform the analysis underlying the qualitative answer above and to compute numerically their weight at the pole and at the equator as a way to gain understanding into the magnitude of this effect.
2. Mathematical proofs
Ask students to:

A. Prompt GAI to "Prove that the solution to the 2D steady-state heat equation is unique".

ChatGPT (for example) will return:

B. Ask students to assess the accuracy of the proof (in the above example, the solution is not unique, but nonetheless ChatGPT "proves" that it is, in this case, by restricting the boundary conditions).

C. As a follow up, and along the lines of teaching students to use GAI prompts, students could be asked to edit the prompt to produce a correct response. For example, given the prompt "Is the solution to the 2D steady-state heat equation unique?",

ChatGPT (for example) will return:

D. Then ask the student to provide examples of problems with and without unique solutions.
3. Understanding assumptions baked into engineering analyses
In engineering problems, there are often many underlying assumptions that students may or may not be aware of. Ask students to:

A. Prompt GAI with a question such as "What are the underlying assumptions in beam theory?"

ChatGPT (for example) will return:

B. Ask students to reflect on and assess the accuracy of the ChatGPT response (in this case the full ChatGPT response is mostly, but not fully correct), or to provide examples of applications where the underlying assumptions (at least those that are correct) are no longer valid.
Back to the Report: Generative Artificial Intelligence for Education and Pedagogy

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Appendix E: Mathematics, physical sciences, and engineering | Center for Teaching Innovation

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Back to the Report: Generative Artificial Intelligence for Education and Pedagogy

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In the social sciences, the advent of GAI raises particular concerns for the written assignments, homework, papers, and exams that are a core component of student work in many courses. Passive reliance on GAI by students to generate literature reviews or written work risks undermining the learning objectives of assignments, producing poor quality work, and violating academic integrity standards. Instructors are also encouraged to explore ways to purposefully incorporate GAI into social science courses in ways that enhance student learning. This can include having students evaluate GAI output and explore ways to test its validity. Appendix D and Appendix B discuss use cases.

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Appendix D: Courses in social sciences | Center for Teaching Innovation

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Back to the Report: Generative Artificial Intelligence for Education and Pedagogy

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Creative fields such as art and music have long engaged in discussions on what is “original work” and how technology can enhance creativity. Practitioners, including students, are highly motivated to develop their skills, but may also be eager to use new technologies to create. While there are many opportunities, concerns exist around ethical attribution of sources and copyright violations. This is an evolving field with companies attempting to add attribution into their processes. Current cases already exist of using GAI for creative brainstorming, or development of final artifacts, as a partner to enable higher level creation by the artist, and many other uses. The rest of the academy might look to creative fields for help working through their own disciplinary considerations of how GAI tools might change notions of original work. Appendix C discusses these considerations in more detail.

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Back to the Report: Generative Artificial Intelligence for Education and Pedagogy
For creative fields such as art and music, AI was a topic of interest and concern before the release of ChatGPT in November 2022. AI tools that "create" artistic images and "compose" music based on existing images and corpi of musical work have been available longer, and artistic communities are already engaging in robust conversations about how AI can contribute to creation. These conversations have expanded since the release of ChatGPT and the greater attention on AI. We use the more general term "AI tools" here, since it is more appropriate than only large language model (LLM)-based tools such as ChatGPT.

Artistic fields have long standing traditions of discussing what it means for an individual to create "original work", although this conversation is constantly shifting as it engages with new technology. Disciplinary practices around ideas of authorship and practices for attribution seem well suited to guide faculty and students in discussing whether, how, and when to integrate AI tools into their creative practice, and how to properly and ethically attribute AI generated work. This is not to say that there is consensus or agreement on how questions of authorship or originality should be answered (see the recent Supreme Court Andy Warhol case), but that such conversations fit into prevailing disciplinary discourses. The rest of the academy might look to creative fields for help working through their own disciplinary considerations of how AI tools might change notions of original work, attribution, and creation.

Additionally, students in artistic disciplines are highly motivated to develop the necessary skills needed to create and interpret art. They generally do not need to be convinced of their need to learn specific skills, or to be warned away from tools that may be able to do that work for them.

Opportunities

In addition to LLMs that write prose and poetry, artificial intelligence tools can generate music, images, and video. This provides many ways in which instructors can embrace these tools, building them into assignments (including examples shared below). One typical way is for AI tools to generate source material for the student to then build upon, riff off, interrogate, or extend in their work.

These tools are developing and improving rapidly. Text to image or text to video tools such as Dall-E, Stable Diffusion, MidJourney, and Adobe Firefly, are easily available, with new versions often in beta environments. Runway ML is a platform that links various AI models for artists to experiment with, generating and combining text, images, video, music, and more. Artbreeder allows users to create original artworks by combining and evolving existing art.

In addition to GPT-3 and GPT-4, there are several writing tools that can generate many types of writing, from marketing copy to short stories and more. For example, for creative writing, LAIKA lets you collaborate with an AI partner. LAIKA can be trained to coach you using your own writing, or you can "collaborate with writers" ranging from Fyodor Dostoevsky to Agatha Christie.

Concerns

Concerns around the use of AI include inequitable access to tools; concerns that overreliance on AI tools built from particular databases can actually limit creative innovation; and risks of appropriation without attribution, including harms to individual creators, such as illustrators and musicians whose individual style is easily co-opted by such tools.

These tools often charge for use beyond a limited amount of free trials. There are also many different AI tools, so keeping up to date with available tools will be a significant time commitment. If Cornell decided to provide AI tools to students to reduce inequity issues, selecting representative AI tools for creative fields would be challenging.

The tools currently available draw from a limited range of human artifacts, skewed toward what has been posted online. Artistic creation that draws only on this content may, over time, limit artistic innovation and narrow the scope of artistic work. Critical engagement with these questions will become an important component of education.

Because this source content is disassociated from the identities of its original creators, it also brings unique risks of appropriation and makes attribution more challenging. AI tools may disrupt how artistic skills are valued and compensated. These tools threaten the livelihood of individual creators (especially illustrators and others whose individual style has been used to inform the development of tools like Dall-E and Midjourney). If used too heavily/without consideration, they can exacerbate imbalances in whose experience is represented by skewing any output toward the existing dataset.

Recommendations

When considering the impact of LLMs, and how to best provide an equitable learning environment for students, it will be important to consider the range of AI tools relevant to creative disciplines, and the extent to which they are integrated into the current workplace.

To allow students time to develop their own design sense/creative perspective, instructors should include assignments that explicitly prohibit the use of GAI tools. Given that students value their individual, creative contributions, instructors should build on this sense of pride and focus on skill-building when encouraging students to avoid overreliance on GAI tools.

Instructors should include critical discussions of the sources of data that are used to build these tools in their pedagogy. There are already conversations around the images that can be elicited from these tools. Such artifacts are interesting in themselves and offer a potential site of critique.

Case studies

We are already seeing the use of GAI in assignments. Some examples of encouraged use in music include:

as a generative tool for images, video, and text as poetic materials to respond to musically. For example, students are invited to generate images from poetic input prompts to Midjourney, using them as inspiration for their compositions.

as parameter mediation in electronic music, where the AI (or more correctly, a neural network) makes lower/mid-level decisions in a given synthesis system so that the performer/composer can operate on a higher, more "instrumental" level of control.

in manipulation of large audio data sets (called a "corpus"), letting AI seek, discover, and organize relationships between and among sounds (or pieces of sounds), so they can then be navigated and accessed by the composer or performer for musical purposes.

When an assignment is designed to teach the use of specific tools, then AI might be prohibited. For example, if a lesson is meant to teach students how to create content "by hand" using a specific digital imaging software, they should not be allowed to use other GAI tools such as text-to-content to produce a similar result.

Attribution is expected in any discussion of the artistic process, and instructors may want to explicitly require students to discuss their experiences interacting with AI tools when used.
Back to the Report: Generative Artificial Intelligence for Education and Pedagogy

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Appendix C: Creative courses for music, literature, and art

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Back to the Report: Generative Artificial Intelligence for Education and Pedagogy For creative fields such as art and music, AI was a topic of interest and concern before the release of ChatGPT in November 2022.

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GAI tools offer opportunities to help students develop their writing skills through assisting in planning, outlining, editing, as well as by providing individualized feedback. However, use of GAI for generating text and editing in the writing process raises major concerns about attribution of work, academic integrity, plagiarism, and failure to develop foundational and advanced writing skills and judgment. Creativity and originality in writing are key learning outcomes that could be threatened by dependency on GAI in the writing process. Guided use of GAI is encouraged as the best approach to further, rather than undermine, learning outcomes. Examples could include the use of GAI to: generate an outline for a written report that students practice revising; summarize themes from a meeting transcript that students organize and prioritize; brainstorm ideas that students then evaluate; or generate lists of sources that students validate and assess. Appendix B outlines detailed examples and scenarios.

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Back to the Report: Generative Artificial Intelligence for Education and Pedagogy
When teaching writing skills and courses in which writing assignments form a key component of the learning objectives, GAI offers both opportunities and concerns. GAI may help students generate and improve outlines, find and evaluate sources, provide equal access to editing suggestions, and build student confidence as they learn to critically evaluate both their own writing and that produced by GAI. However, GAI can also undermine the ability of students to learn fundamental writing skills, such as basic grammar rules, sentence structure, and paragraph coherence, as well as mislead them with fake or biased content. While faculty may choose to prohibit the use of GAI in a course, we would encourage faculty to consider how incorporating some use of GAI, via specific guidelines, supervision, and dialogue, into in-class activities and certain assessments can offer unique and important pedagogical opportunities.

Opportunities

Generative AI tools offer significant opportunities for assisting students with the writing process, from generating outlines to evaluating one's own draft or other written materials; by identifying and evaluating key points for logic, flow, and evidence; to a host of editing capabilities. This technology not only offers a potentially more efficient means of engaging in planning, drafting, and editing, but also a customized and adaptable mechanism for offering personalized feedback to students, helping them to reflect on their writing choices, and to build and improve upon foundational writing skills. Further, these tools can assist faculty in responding to the needs of individual students, offering tailored suggestions, prompts, and resources based on students' strengths and weaknesses.

An essential part of most writing pedagogy and practice involves peer editing. Using GAI as a peer editor can address two problems that typically arise with peer editing: (1) It removes the social comparisons that can keep peers from asking for and offering effective feedback, and (2) It provides access to a "competent" editor that puts in equal effort towards offering suggestions. Students' assessment and application of the feedback that GAI tools can generate may be best maximized, however, when students further analyze the generated suggestions for their efficacy in consultation with faculty. Incorporating dialogue about writing choices, which writing-intensive courses can do through mandatory faculty-student conferences, may both further enhance students' critical thinking and writing skills, as well as ensure that students do not passively over rely on feedback that may not be beneficial, either in the short- or long-term.

Ultimately, a guided approach to using GAI in writing courses can help students develop keener awareness of what makes for effective-as well as ethical-writing choices, and ideally, to spur greater confidence in their own writing abilities, independent of technological assistance.

Concerns

While the opportunities for GAI in the writing process are considerable, the concerns are significant. Using AI for generating text or peer editing raises concerns about academic integrity, plagiarism, authenticity, and the need for human judgment in the writing process.

Beyond the critical ethical considerations at stake, GAI tools offer other concerns that may detract from student learning outcomes in writing courses. Namely, GAI may discourage creativity and original thought in student writing, potentially leading to homogenized output. If GAI output is relied upon solely without additional student analysis, reflection, and modification, students may not learn how to write effectively for a given audience, prioritize content and structure their writing, or learn how to make critical choices to strengthen their written arguments. Moreover, if used as a peer editor, GAI may have difficulty understanding the nuanced requirements of writing assignments, leading to less precise or relevant suggestions.

In addition, students-especially the very students who may feel most hesitant about their writing skills and interacting directly with faculty-may become overly dependent on GAI tools, potentially to the detriment of building foundational critical thinking and writing skills. Use of these tools may further mask challenges that students are experiencing with developing skills, which can become difficult to address later on. Finally, overreliance on GAI tools may also lead to decreased direct interaction between instructors and students, potentially impacting the quality of guidance and mentorship, which is essential in writing-intensive courses.

Recommendations

At the specific writing-intensive class-level, we suggest that faculty engage in a thoughtful discussion with their students and employ a guided use of GAI that can:

Emphasize what constitutes effective and ethical uses and applications of GAI, and thus allow for building of students' critical thinking and writing skills in being able to discern the limitations of the tools in practice;

Teach students the importance of analyzing and evaluating information independently of GAI tools, and how its use can inhibit students' ability to build both foundational and advanced writing skills; and

Clarify why developing certain foundational and advanced writing skills, along with good academic integrity practices, is fundamental to their academic and personal growth.

Prepare students to ethically use these tools in the workforce.

Case Studies

We offer several sample assignments that can be used in writing intensive courses. In addition to the below examples, the Knight Institute offers suggestions on how faculty might use GAI to help students complete research papers for their First-Year Writing Seminar courses.

Sample Editing Assignments
1. Editing Skills - pre-work for class discussion, in-class activity
Provide students with a paragraph of text that is relevant to your course, such as a particularly complex paragraph from a scholarly journal article or from a primary source.

Ask students to:

Edit the paragraph on their own, using a prompt that makes sense for your course (e.g, make this paragraph more concise, strengthen the argument in this paragraph, explain the concept in this paragraph).

Students run the original paragraph through GAI and ask it to do the same thing that students did in the first step.

Have students review their editing choices as compared to the editing choices made by GAI and submit a final revised paragraph.

Students complete a written or in-class reflection on why they included their final edits and how they were able to improve on what GAI provided.

2. Revise for new audience & reflect - graded assignment Turn a two-page proposal into a one-page proposal for a new audience.

Ask students to:

Using an assignment that students wrote earlier in the semester, identify a new audience and rewrite their assignment for this new audience (e.g., proposal was originally written for the manager and will now be rewritten for a vice president; proposal was originally written for a scientific audience and now needs to be rewritten for a lay-person).

Use GAI to help shorten the original proposal.

Critically analyze how GAI shortened the work, paying attention to what the new audience knows and needs to know.

Using edits suggested by GAI and their own critical thinking, shorten the original proposal to one page.

Utilize document design features (e.g., headings, sub-headings, bulleted lists or tables) to make the writing easy for the new audience to read quickly.

Students write a reflection on how and why they modified their original proposal for the new audience, including how they were able to improve on what GAI provided.

3. Reverse outlining to strengthen structure and evidence-in-class or for outside of class

Ask students to:

Use GAI to generate a reverse outline of a written draft: have it identify what the main focus/claim of each paragraph is, and what supporting evidence is within that paragraph.

Reviewing what GAI has generated, students then consider whether the main claim that was identified for each paragraph is the one that they intended to be the topic of that paragraph. Further, does the evidence that GAI identified sufficiently support that claim?

In subsequent written reflection (and/or in class discussion), students should assess what they found to be effective about the process in helping them pinpoint issues with structure, logic, and evidence. Students should also reflect on where GAI was limited or misguided in assisting them with reverse outlining.

Similar to assignment 1, students might first produce a reverse outline on their own, and then compare/contrast what they created with that from GAI, further reflecting on those differences.
4. GAI as a peer editor - in-class activity or homework outside of class
Peer editing can be a valuable tool to help students learn to edit their own and others' work, as well as learn how to respond effectively to feedback.

Ask students to:

Ask GAI to offer constructive feedback on their writing, with specific suggestions for where and how to make improvements in grammar, logic, and flow (tailor this prompt to your desired outcomes).

Students review the GAI feedback and make edits to their original work.

Students respond to several reflection prompts including:

Which suggestions did you find to be effective? Not effective? Why?

As you review the feedback, did GAI help you notice patterns in the kinds of suggestions that you received? If so, identify and reflect on these patterns: were you previously aware of these patterns? Do you feel that GAI pointed out all of the different areas in which you have concern, or did it miss some?

How do you intend to apply the feedback you found effective in future writing?

Content creation assignments
1. Generate an outline for a written report
Ask students to:

Use GAI to assist them in generating an outline for their report.

Emphasize that GAI should be used as a tool for inspiration and guidance, rather than relying solely on its suggestions.

Students modify and improve their outline, then evaluate it based on their logical structure, coherence, and adherence to the assignment requirements.

In-class discussion or written reflection: Students critically analyze the usefulness of AI in the writing process.

2. Summarize arguments and produce themes
Ask students to:

Use GAI to summarize a meeting transcript into key themes (e.g., congressional meetings, central banker transcripts, corporate earnings reports).

Students produce a memo that prioritizes the themes and adds contextual information on power dynamics or other relevant background information.

In-class discussion or written reflection: Students critically analyze the usefulness of AI in the writing process.

3. Brainstorm ideas
Ask students to:

Use GAI to brainstorm ideas for an assignment that is relevant to the course (e.g., topics for a research paper, research study ideas, new business ideas, marketing taglines) .

Students evaluate the ideas based on the established criteria for the assignment.

Students further develop their preferred idea and complete the assignment.

In-class discussion or written reflection: Students critically analyze the usefulness of AI in the brainstorming process.

4. Assess validity of sources for accuracy and bias
Ask students to:

Use GAI to find sources for an assignment relevant to the course.

Students review sources, evaluating the usefulness and relevance to the assignment.

Students conduct a similar search using library resources.

Students produce an annotated bibliography that assesses the quality, relevance, and reliability of GAI and library sources.

In-class discussion or written reflection: Students critically analyze the usefulness of AI to find reliable and unbiased sources.

Classroom discussion assignments
1. Compare/Contrast
Ask students to:

Use GAI to generate content on a particular topic that is relevant to the course (e.g., academic writing vs. business writing, Greek civilization vs. Roman civilization, differential calculus vs. integral calculus).

Assign a reading or book chapter on the same content.

In-class discussion or written reflection: Students critically analyze what GAI got correct, incorrect, or missed completely.

2. Pro/Con debate
Ask students to:

Use GAI to generate pro and con arguments for a debate that is relevant to the course.

Review course material and readings to augment and strengthen these arguments.

Come to class prepared to engage in a debate with their classmates.

After the debate, discuss which arguments were strongest and why.

In-class discussion or written reflection: Students critically analyze what makes a more effective argument.

Back to the Report: Generative Artificial Intelligence for Education and Pedagogy

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Appendix B: Courses that develop writing as a skill

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Back to the Report: Generative Artificial Intelligence for Education and Pedagogy When teaching writing skills and courses in which writing assignments form a key component of the learning objectives, GAI offers both opportunities and concerns.

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"Publishing an intro to generative AI is a challenge as things are moving so quickly. However, we think things have now settled down enough for us to bring together information in a single place, to create a short primer."

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Publishing an intro to generative AI is a challenge as things are moving so quickly. However, we think things have now settled down enough for us to bring together information in a single place, to create a short primer. We aim to publish this as a more formal guide that will be updated regularly, but we are posting an initial version as a blog post to get feedback on whether it is useful and if there is other information you would like included.

Version 1.1 - May 22 2023

Table of Contents
2. An Introduction to the Generative AI Technology 2.2. Microsoft Bing Chat, Google Bard and Anthrompic's Claude 2.3. A summary of key capabilities, limitations, and concerns around ChatGPT and other Large Language Models 2.4 Image Generation 2.5 Microsoft Co-pilot and everyday AI 3. Impact of Generative AI on Education 3.1 Assessment 3.1.1 Guidance on advice to students 3.1.2 The role of AI detectors 3.2 Use in Learning and Teaching 3.2.1 Examples of use by students 3.2.2 Examples of use by teaching staff 3.2.3 Examples of uses to avoid 3.3 Adapting curriculum to reflect the use of AI in work and society.
1. Introduction

Generative AI tools such as ChatGPT are already having a significant impact on education. These tools are posing considerable challenges around assessment and academic integrity, but also present opportunities, for example saving staff time by helping with the creation of learning materials or presenting students with new tools to enhance the way they work. The impact of generative AI is being felt far beyond education, and it is already starting to change the way we work. This presents more challenges and opportunities, in making sure education prepares students for an AI-augmented workplace, and that assessments are authentic yet robust.

The primer is intended as a short introduction to generative AI, exploring some of the main points and areas relevant to education, including two main elements:

An introduction to Generative AI technology

The implications of Generative AI on education

2. An Introduction to the Generative AI Technology

Whilst we do not need a detailed technical understanding of the technology to make use of it, some understanding helps us understand its strengths, weaknesses, and issues to consider. In this section, we will be looking into the technology in a little more detail.

This is a fast-moving space, and the information here is likely to age quickly! This edition was written in May 2023, and we aim to update it regularly to take into account significant developments.

This guide starts by looking at AI text generators, also known as Large Language Models (LLMs).

2.1 ChatGPT

ChatGPT has grabbed most of the headlines since its launch in November 2022. It was created by a company called OpenAI, which started as a not-for-profit research organisation (hence the name) but is now a fully commercial company with heavy investment from Microsoft. It is available as a free version, plus a premium version at $20 a month, which provides faster, more reliable access, as well as access to its latest language models and features, including plugins, which changes its behaviour significantly.

ChatGPT is based on a machine learning approach called 'Transformers', first proposed in 2017, and is pre-trained on large chunks of the internet, which gives it the ability to generate text in response to user prompts, hence the name 'Generative Pre-trained Transformer'. Whilst OpenAI provided some information on the approach for training ChatGPT, they haven't so far, released any information about GPT4, the latest model released in early 2023.

In its standard mode, without plugins, ChatGPT works by predicting the next word given a sequence of words. This is important to understand, as it is not in any sense understanding your question and then searching for a result and has no concept of whether the text it is producing is correct. This leads it to be prone to producing plausible untruths or, as they are often known, hallucinations.

As it stands today, the free version of ChatGPT doesn't have access to the internet, so can't answer questions beyond its training data cut-off date of September 2021. Users paying for the ChatGPT Plus service have access to a version that can access the internet.

ChatGPT Plus customers also have access to plugins which extend ChatGPT's functionality. For example, a Wolfram plugin allows users to ask questions which are answered by Wolfram Alpha, which excels at mathematical and scientific information. Initial testing suggests this might resolve the issue of 'hallucination' in these domains. Many other plugins are available, and more are being developed.

OpenAI makes its service available to other developers, so many other applications make use of it, including many writing tools such as Jasper and Writesonic, as well as chatbots in popular applications such as Snapchat.

2.2. Microsoft Bing Chat, Google Bard and Anthrompic's Claude

Although ChatGPT has got most of the hype, there are other players in this space, and this number is likely to increase.

Microsoft has introduced Bing Chat, which is available for testing, and is based on Open AI's GPT-4. It's focused on searching for information and does have access to the internet. It takes your question, performs one or more web searches based on your question, and will then attempt to summarise and answer, giving references for the sites it uses.

Bard is Google's equivalent and is available for testing. Like Bing Chat, it can access the internet, but unlike Bing Chat, it doesn't provide references for the sites it's used to give its answers.

Claude is more similar to ChatGPT and is produced by Anthropic, and is likely to be built into many applications going forward.

At the moment, both Google Bard and Claude feel a long way behind ChatGPT in terms of capabilities, although at the time of writing, Google has just announced significant improvements, so this may change quickly.

2.3. A summary of key capabilities, limitations, and concerns around ChatGPT and other Large Language Models.

In considering generative AI, it's important not only to understand its capabilities but also its limitations. We also believe users should have a broader understanding of the societal impact of generative AI. Some of the key themes are summarised here:

It can generate plausible but incorrect information.

ChatGPT is only trained on information up until Sept 2021 (but those with the paid ChatGPT Plus service have access to a version that can access the internet)

Limited ability to explain the sources of information for its responses (this varies between Chatbots)

2.4 Image Generation

It's not all about text - image generation tools have made huge progress too, particularly with Midjourney, DALL-E 2 and Stable Diffusion.

As for the text generators, these have been trained on information scraped from the internet, and there is a lot of concern about the copyright of the training material.

These work in a similar way to text generators - the user gives a prompt and one or more variations of images are produced.

Image generation capabilities are being incorporated into general AI services, so Bing Chat, for example, can also generate images, using OpenAI's DALL-E 2.

2.5 Microsoft Co-pilot and everyday AI

There is a lot more to generative AI than just accessing it via the ChatGPT web service. The developers of major AI services such as OpenAI make their services available to other developers. One of the most significant announcements has been from Microsoft, who are incorporating generative AI across the Microsoft 365 tools, under the name ' Co-pilot'. At the time of writing (May 2023) the licensing terms haven't been confirmed, but it is likely that this will put generative AI directly into the hands of all our staff and students with access to Microsoft 365. Google have made similar announcements about their office tools.

2.6 Beyond Chatbots

Whilst the focus has been on chatbots and image generation, generative AI is finding it its way into a wide range of tools and services that can potentially be used in education. As an example, Teachermatic can create a range of resources for teachers, including lesson plans and quizzes, Gamma can create websites and presentations, and Curipod can create interactive learning resources. Many of these applications are built on tools provided by OpenAI and powered by the same technology as ChatGPT.

3. Impact of Generative AI on Education

The impact of generative AI in education is still unfolding and is likely to for some time to come. Key areas include assessment and academic integrity, its use in teaching and learning, use as a time-saving tool, and use by students.

Jisc has convened a number of working groups with representatives from universities and colleges to help us collate and present more detailed advice in these areas, particularly around sector-level advice, assessment and advice to students, and we'll share more information from these groups over the coming weeks.

Here we will now give an overview of the core themes.

3.1 Assessment

Initial discussions about generative AI have focused on assessment, with the concern that students will use generative AI to write essays or answer other assignments. This has parallels with concerns around essay mills. These concerns are valid, and whilst essays produced wholly by generative AI are unlikely to get the highest marks, their capability is improving all the time. The ability isn't just limited to essays. ChatGPT is also highly capable of answering multiple-choice questions and will attempt most forms of shorter-form questions. It will often fall short, especially when answers are highly mathematical, although this will not be obvious to the student using the chatbot service.

There are three main options, each with their own challenges. There is broad acceptance that 'embrace and adapt' is the best strategy in most instances.

Avoid
Revert to in-person exams where the use of AI isn't possible

This moves away from authentic assessment and creates many logistical challenges.

Outrun
Devise an assessment that AI can't do.

AI is advancing rapidly and given the time between the assessment being set and it being taken, AI might well be able to do the assignment when it is taken.

Embrace and adapt
Embrace the use of AI, discuss the appropriate use of AI with students, and actively encourage its use to create authentic assessments

Balancing authentic assessment and the use of generative AI with academic integrity is a challenge.

The immediate action is for all staff to engage with generative AI and try it themselves, learning how their assessments will be impacted. Alongside this, institutions will need to consider their strategic approach to AI, review, and update policies, and communicate guidance to students.

For higher education, this aligns with guidance provided by QAA, and for both higher education and further education, general guidance has been provided by the Department for Education. In addition, the Joint Council for Qualifications has produced guidance for protecting quality of qualifications.

3.1.1 Guidance on advice to students

We are seeing the first guidance to students appear. Getting the wording on this can be challenging, and is discussed further in a Jisc blog post ' Considerations on wording when creating advice or policy on AI use '. Our key messages are:

Don't try to 'ban' specific technology or AI as a whole.

Describe acceptable and expected behaviours and provide examples.

The detail of advice will vary between subjects, so supplement general advice with tailored advice for the subject.

A Jisc-convened working group is looking at best practices in this area and will be sharing its outcomes shortly.

3.1.2 The role of AI detectors

Discussions about academic integrity inevitably include the role of AI detection software. There are a number of tools in this space, but for most UK universities and colleges, Turnitin's newly released AI detection tool will be the obvious tool to consider. The key things are:

No AI detection tool can conclusively prove that text was written by AI

These tools will produce false positives.

The tools won't be able to differentiate between legitimate and other use of AI writing tools.

Best practice for using such tools is still being considered and developed across our sectors, and we will aim to provide examples as these develop.

A Jisc blog post explores the concepts and considerations around AI detection in more detail.

3.2 Use in Learning and Teaching

We are seeing a wide range of ideas for how to use generative AI in learning and teaching. It's worth remembering at this point that it is a fast-moving space and many of the tools are still under development or at the beta stage. Whilst exploration makes a lot of sense, it's also worth noting that individuals shouldn't feel the need to rush into using it in teaching - things are changing rapidly, but now is definitely the time to explore and start planning.

3.2.1 Examples of use by students

We welcome the fact that student voices have been brought into the discussion around generative AI, for example ' Generative AI: Lifeline for students or threat to traditional assessment?'. Jisc has run a number of student panels and will be reporting the findings in more detail in the near future.

Examples of uses by students.

To formulate ideas, for example creating essay structures

Generative AI tools are generally effective in producing outlines as a starting point for an assignment.

To provide feedback on writing

Generative AI will proofread and correct text for students, in a similar way to grammar tools. It will also provide feedback on style and content. Students will need clear advice on when this should be declared.

As a research tool

A good understanding of the tool and its limitations is crucial here, particularly the tendency for generative AI to give misinformation.

Generating images to include in assignments.

The best image generation tools come at a cost, and students need to be aware of copyright concerns.

In section 2.3 we noted that digital inequality is a concern. For example, students that pay for ChatGPT Plus get faster, more reliable access, as well as access to the latest model, and those that pay, for example, for MidJourney image generation will get more and better images than most of the free options. At the moment there are limited options for licensing these tools institutionally, but we expect this to change, and consideration should then be given to licensing options to avoid inequality.

3.2.2 Examples of use by teaching staff

If used appropriately, generative AI has the potential to reduce staff workload, for example by assisting with tasks such as lesson plans, learning material creation, and possibly feedback. The key words here are 'appropriately' and 'assisting'. In section 2.3 we noted some of the limitations of generative AI, such as incorrect information, so as things stand today, generative AI can assist by, for example, generating initial ideas, but its output should always be carefully reviewed and adapted.

Use of generative AI may be by directly using a general chat app such as ChatGPT, or maybe via an application built on top of generative AI, such as Teachermatic. In the former case in particular staff will need good prompting skills to make the most of generative AI.

Drafting ideas for lesson plans and other activities

The output may be factually incorrect or lack sound pedagogical foundations. Nonetheless, it may be a useful starting point.

Help with the design of quiz questions or other exercises.

Generative AI can quickly generate multiple-choice quizzes and assessment ideas, but they should be reviewed carefully as above.

Customising materials (simplifying language, adjusting to different reading levels, creating tailored activities for different interests)

Generally, when asked to customize material, generative AI won't introduce new concepts, and so is less likely to introduce factually incorrect information.

Providing custom feedback to students.

At the moment, generative AI should not be used to mark student work, but it can be a useful tool for assisting with personalized feedback.

3.2.3 Examples of uses to avoid

In general, you should avoid posting any work that isn't your own, and any personal information into ChatGPT, as it might well be used as part of the training data.

Things get a little more complex when considering tools built with ChatGPT, and it is almost certain that generative AI built into tools like Microsoft 365 will have the same data privacy protection as the rest of Microsoft 365.

Marking student work

There is no robust evidence of good performance, although ChatGPT will confidently do this if you ask.

Detecting whether work is written by AI

ChatGPT might claim it can detect whether it wrote the text, but it can't

Anything involving personal information

You should never put personal information into any system that your University or College hasn't got a proper contract in place with and made a full assessment of its data privacy policies etc. Generative AI services like ChatGPT are no exception.

3.3 Adapting curriculum to reflect the use of AI in work and society.

We are seeing a broad acknowledgement that work will change too, but understandably limited action around this at the moment.

OpenAI has done some initial research on the impact of LLMs and jobs and has estimated that 'around 80% of the U.S. workforce could have at least 10% of their work tasks affected by the introduction of LLMs, while approximately 19% of workers may see at least 50% of their tasks impacted.' A 2021 report by the Department for Business, Energy and Industrial Strategy looks more broadly at jobs affected by AI, both in terms of those likely to be automated and growth areas.

In the short term, we expect generative AI to be quickly adopted into courses where it quickly becomes the norm in the workplace. For example, visual arts make use of generative AI, and computer coding makes use of generative coding tools.

4. Regulation

Given the pace of change, regulation has struggled to keep up. In the UK, the government has published an AI white paper which aims to balance regulation and enabling innovation. The paper makes explicit reference to generative AI and large language models. The US government have produced a Bill of Rights and the EU's AI act is working its way through the European Parliament. The proposed UK approach will look to regulate the use of AI, rather than AI technology itself, through the work of existing regulators, against a common set of principles. As progress is made in this area we will endeavour to highlight any areas of particular concern for education.

5. Summary

Generative AI is progressing rapidly and is likely to have a significant impact on education for the foreseeable future. Keeping up with the advances is a challenge, and balancing authentic assessment and academic integrity is increasingly complex. Nonetheless, with care and an increase in staff and student knowledge, there are substantial gains to be made. This guide aimed to give a broad introduction to generative AI. Much more has been written on the topic, and for those that wish to explore further, we have included a range of resources for further reading.
Keeping updated:
To keep up to date with the work of Jisc's National Centre of AI in Tertiary Education, join our Jisc mail list: aied@jiscmail.ac.uk.
Further Reading:
Generative artificial intelligence in education - Department For Education (March 2023)

Maintaining Quality and Standards in the ChatGPT Era: QAA Advice on the Opportunities and Challenges Posed by Generative Artificial Intelligence - QAA (May 2023)

ChatGPT and Artificial Intelligence in higher education - Unesco (April 2023)

Artificial Intelligence (AI) Use in Assessments: Protecting the Integrity of Qualifications - JCQ (March 2023)
Relevant Jisc Blog Posts:
Staff and student use:
Generative AI: Lifeline for students or threat to traditional assessment? Means. Motive, Opportunity: A Composite Narrative about Academic Misconduct Considerations on wording when creating advice or policy on AI use
AI Detection:
AI writing detectors - concepts and considerations A short experiment in defeating a ChatGPT detector
Bias and other ethical considerations:
Hidden Workers powering AI Exploring the potential for bias in ChatGPT
Using Generative AI:
Getting started with ChatGPT Part 1 - Understanding ChatGPT How to Explore AI Image Generation
Change log

V1.1 - 22 May 2023

We've added a table of contents.

Section 2.1 (ChatGPT) has been updated to reflect the fact that all ChatGPT Plus users have access to plugins and web browsing.

We've added a CC BY-NC-SA license

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A Generative AI Primer - National centre for AI

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The primer is intended as a short introduction to generative AI, exploring some of the main points and areas relevant to education, including two main elements: an introduction to Generative AI technology and the implications of generative AI on education

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Discipline-specific Generative AI Teaching and Learning Resources

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Discipline-specific Generative AI Teaching and Learning Resources

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Faculty Help: Generative AI Resource Guide: Sample Syllabus Policies

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Faculty Help: Generative AI Resource Guide: Sample Syllabus Policies

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Repository of info about impact of Generative AI on/in higher education.

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Effective fall 2024, all credit-bearing course syllabi must include a Generative AI policy, where faculty may either choose one from a list of four policy statements (ranging from complete prohibition to liberal use of Generative AI) or craft their own policy with approval of their Dean/Assistant Dean. These policy choices will appear in the institutional master Simple Syllabus template. If you do not see these choices appear or need assistance contact Victoria Schweizer at victoria.schweizer@sfcc.edu.

Prohibited Use- The use of Generative Artificial Intelligence (GAI) tools/programs (e.g. ChatGPT, Claude, Gemini, Grammarly Pro, etc.) to complete any phase or stage of work for this course is strictly prohibited. All forms of work you produce in this course are expected to be wholly your own. This restriction includes the creation or revision of your work by GAI. Examples of activities for which you are not permitted to use GAI include but are not limited to: brainstorming ideas, narrowing a topic, refining a research question, drafting an outline, checking grammar, or composing or revising a paper (draft or final) or discussion board post or response.

Limited Use- The use of Generative Artificial Intelligence (GAI) tools/programs (e.g. ChatGPT, Claude, Gemini, Grammarly Pro, etc.) to complete work for this course is generally not allowed, except for specific instances that I will clearly indicate. There may be some activities or assignments for which I will allow limited use of GAI tools and programs. However, you are not to interpret this limited use as permission to use GAI in any other aspect of this course.

Moderately Permissive Use- The use of Generative Artificial Intelligence (GAI) tools/programs (e.g. ChatGPT, Claude, Gemini, Grammarly Pro, etc.) to complete work for this course is generally permitted for early work phase activities such as brainstorming ideas, narrowing a topic, refining a research question, drafting an outline, and checking grammar. However, you may not use it to compose or generate wholesale something I have asked you to produce, such as, but not limited to: a draft (rough or final) of a paper, a discussion board post or response, or sources of information. Any use of GAI must be explicitly disclosed, and you will bear ultimate responsibility for the integrity and accuracy of the work.

Open Use- The use of Generative Artificial Intelligence (GAI) tools/programs (e.g. ChatGPT, Claude, Gemini, Grammarly Pro, etc.) to complete work for this course is permitted. You are free to use it during any phase or stage of work for this course. This allowance includes the creation or revision of your work by GAI. Examples of activities for which you are permitted to use GAI may include: brainstorming ideas, narrowing a topic, refining a research question, drafting an outline, checking grammar, or composing or revising a paper (draft or final) or discussion board post or response. However, any use of GAI must be explicitly disclosed, and you will bear ultimate responsibility for the integrity and accuracy of the work.

Instructor-Generated Course-Specific Use- If this choice is elected, the Instructor-generated course-specific policy on use of Generative Artificial Intelligence (GAI) tools/programs (e.g. ChatGPT, Claude, Gemini, Grammarly Pro, etc.) to complete work for this course can be found below.

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Research Guides: Faculty Help: Generative AI Resource Guide: Sample Syllabus Policies

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Syllabi Policies for AI Generative Tools

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This resource is created by Lance Eaton for the purposes of sharing and helping other instructors see the range of policies available by other educators to help in the development of their own for navigating AI-Generative Tools.

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Syllabi Policies for AI Generative Tools

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This article by McKinsey focuses on specific algorithms like ChatGPT and DALL-E, explaining how they create new content.

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In the months and years since ChatGPT burst on the scene in November 2022, generative AI (gen AI) has come a long way. Every month sees the launch of new tools, rules, or iterative technological advancements. While many have reacted to ChatGPT (and AI and machine learning more broadly) with fear, machine learning clearly has the potential for good. In the years since its wide deployment, machine learning has demonstrated impact in a number of industries, accomplishing things like medical imaging analysis and high-resolution weather forecasts. A 2022 McKinsey survey shows that AI adoption has more than doubled over the past five years, and investment in AI is increasing apace. It's clear that generative AI tools like ChatGPT (the GPT stands for generative pretrained transformer) and image generator DALL-E (its name a mashup of the surrealist artist Salvador Dalí and the lovable Pixar robot WALL-E) have the potential to change how a range of jobs are performed. The full scope of that impact, though, is still unknown-as are the risks.

Still, organizations of all stripes have raced to incorporate gen AI tools into their business models, looking to capture a piece of a sizable prize. McKinsey research indicates that gen AI applications stand to add up to $4.4 trillion to the global economy-annually. Indeed, it seems possible that within the next three years, anything in the technology, media, and telecommunications space not connected to AI will be considered obsolete or ineffective.

But before all that value can be raked in, we need to get a few things straight: What is gen AI, how was it developed, and what does it mean for people and organizations? Read on to get the download.

To stay up to date on this critical topic, sign up for email alerts on "artificial intelligence" here.
Learn more about QuantumBlack, AI by McKinsey.

What's the difference between machine learning and artificial intelligence?

Artificial intelligence is pretty much just what it sounds like-the practice of getting machines to mimic human intelligence to perform tasks. You've probably interacted with AI even if you don't realize it-voice assistants like Siri and Alexa are founded on AI technology, as are customer service chatbots that pop up to help you navigate websites.

Machine learning is a type of artificial intelligence. Through machine learning, practitioners develop artificial intelligence through models that can "learn" from data patterns without human direction. The unmanageably huge volume and complexity of data (unmanageable by humans, anyway) that is now being generated has increased machine learning's potential, as well as the need for it.

What are the main types of machine learning models?

Machine learning is founded on a number of building blocks, starting with classical statistical techniques developed between the 18th and 20th centuries for small data sets. In the 1930s and 1940s, the pioneers of computing-including theoretical mathematician Alan Turing-began working on the basic techniques for machine learning. But these techniques were limited to laboratories until the late 1970s, when scientists first developed computers powerful enough to mount them.

Until recently, machine learning was largely limited to predictive models, used to observe and classify patterns in content. For example, a classic machine learning problem is to start with an image or several images of, say, adorable cats. The program would then identify patterns among the images, and then scrutinize random images for ones that would match the adorable cat pattern. Generative AI was a breakthrough. Rather than simply perceive and classify a photo of a cat, machine learning is now able to create an image or text description of a cat on demand.

How do text-based machine learning models work? How are they trained?

ChatGPT may be getting all the headlines now, but it's not the first text-based machine learning model to make a splash. OpenAI's GPT-3 and Google's BERT both launched in recent years to some fanfare. But before ChatGPT, which by most accounts works pretty well most of the time (though it's still being evaluated), AI chatbots didn't always get the best reviews. GPT-3 is "by turns super impressive and super disappointing," said New York Times tech reporter Cade Metz in a video where he and food writer Priya Krishna asked GPT-3 to write recipes for a (rather disastrous) Thanksgiving dinner.

The first machine learning models to work with text were trained by humans to classify various inputs according to labels set by researchers. One example would be a model trained to label social media posts as either positive or negative. This type of training is known as supervised learning because a human is in charge of "teaching" the model what to do.

The next generation of text-based machine learning models rely on what's known as self-supervised learning. This type of training involves feeding a model a massive amount of text so it becomes able to generate predictions. For example, some models can predict, based on a few words, how a sentence will end. With the right amount of sample text-say, a broad swath of the internet-these text models become quite accurate. We're seeing just how accurate with the success of tools like ChatGPT.

What does it take to build a generative AI model?

Building a generative AI model has for the most part been a major undertaking, to the extent that only a few well-resourced tech heavyweights have made an attempt. OpenAI, the company behind ChatGPT, former GPT models, and DALL-E, has billions in funding from bold-face-name donors. DeepMind is a subsidiary of Alphabet, the parent company of Google, and even Meta has dipped a toe into the generative AI model pool with its Make-A-Video product. These companies employ some of the world's best computer scientists and engineers.

But it's not just talent. When you're asking a model to train using nearly the entire internet, it's going to cost you. OpenAI hasn't released exact costs, but estimates indicate that GPT-3 was trained on around 45 terabytes of text data-that's about one million feet of bookshelf space, or a quarter of the entire Library of Congress-at an estimated cost of several million dollars. These aren't resources your garden-variety start-up can access.

What kinds of output can a generative AI model produce?

As you may have noticed above, outputs from generative AI models can be indistinguishable from human-generated content, or they can seem a little uncanny. The results depend on the quality of the model-as we've seen, ChatGPT's outputs so far appear superior to those of its predecessors-and the match between the model and the use case, or input.

ChatGPT can produce what one commentator called a " solid A-" essay comparing theories of nationalism from Benedict Anderson and Ernest Gellner-in ten seconds. It also produced an already famous passage describing how to remove a peanut butter sandwich from a VCR in the style of the King James Bible. Image-generating AI models like DALL-E 2 can create strange, beautiful images on demand, like a Raphael painting of a Madonna and child, eating pizza. Other generative AI models can produce code, video, audio, or business simulations.

But the outputs aren't always accurate-or appropriate. When Priya Krishna asked DALL-E 2 to come up with an image for Thanksgiving dinner, it produced a scene where the turkey was garnished with whole limes, set next to a bowl of what appeared to be guacamole. For its part, ChatGPT seems to have trouble counting, or solving basic algebra problems-or, indeed, overcoming the sexist and racist bias that lurks in the undercurrents of the internet and society more broadly.

Generative AI outputs are carefully calibrated combinations of the data used to train the algorithms. Because the amount of data used to train these algorithms is so incredibly massive-as noted, GPT-3 was trained on 45 terabytes of text data-the models can appear to be "creative" when producing outputs. What's more, the models usually have random elements, which means they can produce a variety of outputs from one input request-making them seem even more lifelike.

What kinds of problems can a generative AI model solve?

The opportunity for businesses is clear. Generative AI tools can produce a wide variety of credible writing in seconds, then respond to criticism to make the writing more fit for purpose. This has implications for a wide variety of industries, from IT and software organizations that can benefit from the instantaneous, largely correct code generated by AI models to organizations in need of marketing copy. In short, any organization that needs to produce clear written materials potentially stands to benefit. Organizations can also use generative AI to create more technical materials, such as higher-resolution versions of medical images. And with the time and resources saved here, organizations can pursue new business opportunities and the chance to create more value.

We've seen that developing a generative AI model is so resource intensive that it is out of the question for all but the biggest and best-resourced companies. Companies looking to put generative AI to work have the option to either use generative AI out of the box or fine-tune them to perform a specific task. If you need to prepare slides according to a specific style, for example, you could ask the model to "learn" how headlines are normally written based on the data in the slides, then feed it slide data and ask it to write appropriate headlines.

What are the limitations of AI models? How can these potentially be overcome?

Because they are so new, we have yet to see the long tail effect of generative AI models. This means there are some inherent risks involved in using them-some known and some unknown.

The outputs generative AI models produce may often sound extremely convincing. This is by design. But sometimes the information they generate is just plain wrong. Worse, sometimes it's biased (because it's built on the gender, racial, and myriad other biases of the internet and society more generally) and can be manipulated to enable unethical or criminal activity. For example, ChatGPT won't give you instructions on how to hotwire a car, but if you say you need to hotwire a car to save a baby, the algorithm is happy to comply. Organizations that rely on generative AI models should reckon with reputational and legal risks involved in unintentionally publishing biased, offensive, or copyrighted content.

These risks can be mitigated, however, in a few ways. For one, it's crucial to carefully select the initial data used to train these models to avoid including toxic or biased content. Next, rather than employing an off-the-shelf generative AI model, organizations could consider using smaller, specialized models. Organizations with more resources could also customize a general model based on their own data to fit their needs and minimize biases. Organizations should also keep a human in the loop (that is, to make sure a real human checks the output of a generative AI model before it is published or used) and avoid using generative AI models for critical decisions, such as those involving significant resources or human welfare.

It can't be emphasized enough that this is a new field. The landscape of risks and opportunities is likely to change rapidly in coming weeks, months, and years. New use cases are being tested monthly, and new models are likely to be developed in the coming years. As generative AI becomes increasingly, and seamlessly, incorporated into business, society, and our personal lives, we can also expect a new regulatory climate to take shape. As organizations begin experimenting-and creating value-with these tools, leaders will do well to keep a finger on the pulse of regulation and risk.
Articles referenced include:

"Implementing generative AI with speed and safety," March 13, 2024, Oliver Bevan, Michael Chui, Ida Kristensen, Brittany Presten, and Lareina Yee

"Beyond the hype: Capturing the potential of AI and gen AI in tech, media, and telecom," February 22, 2024, Venkat Atluri, Peter Dahlström, Brendan Gaffey, Víctor García de la Torre, Noshir Kaka, Tomás Lajous, Alex Singla, Alex Sukharevsky, Andrea Travasoni, and Benjamim Vieira

"As gen AI advances, regulators-and risk functions-rush to keep pace," December 21, 2023, Andreas Kremer, Angela Luget, Daniel Mikkelsen, Henning Soller, Malin Strandell-Jansson, and Sheila Zingg

"The economic potential of generative AI: The next productivity frontier," June 14, 2023, Michael Chui, Eric Hazan, Roger Roberts, Alex Singla, Kate Smaje, Alex Sukharevsky, Lareina Yee, and Rodney Zemmel

"What every CEO should know about generative AI," May 12, 2023, Michael Chui, Roger Roberts, Tanya Rodchenko, Alex Singla, Alex Sukharevsky, Lareina Yee, and Delphine Zurkiya

"Exploring opportunities in the generative AI value chain," April 26, 2023, Tobias Härlin, Gardar Björnsson Rova, Alex Singla, Oleg Sokolov, and Alex Sukharevsky

"The state of AI in 2022-and a half decade in review," December 6, 2022, Michael Chui, Bryce Hall, Helen Mayhew, Alex Singla, and Alex Sukharevsky

"McKinsey Technology Trends Outlook 2023," July 20, 2023, Michael Chui, Mena Issler, Roger Roberts, and Lareina Yee

"An executive's guide to AI," Michael Chui, Vishnu Kamalnath, and Brian McCarthy

"What AI can and can't do (yet) for your business," January 11, 2018, Michael Chui, James Manyika, and Mehdi Miremadi

This article was updated in April 2024; it was originally published in January 2023.

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Generative artificial intelligence (AI) describes algorithms (such as ChatGPT) that can be used to create new content, including audio, code, images, text, simulations, and videos. Recent breakthroughs in the field have the potential to drastically change the way we approach content creation.

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There’s More to AI Bias Than Biased Data, NIST Report Highlights

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There’s More to AI Bias Than Biased Data, NIST Report Highlights

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Link to NIST publication: Towards a Standard for Identifying and Managing Bias in Artificial Intelligence (NIST Special Publication 1270)

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As a step toward improving our ability to identify and manage the harmful effects of bias in artificial intelligence (AI) systems, researchers at the National Institute of Standards and Technology (NIST) recommend widening the scope of where we look for the source of these biases - beyond the machine learning processes and data used to train AI software to the broader societal factors that influence how technology is developed.

The recommendation is a core message of a revised NIST publication, Towards a Standard for Identifying and Managing Bias in Artificial Intelligence (NIST Special Publication 1270), which reflects public comments the agency received on its draft version released last summer. As part of a larger effort to support the development of trustworthy and responsible AI, the document offers guidance connected to the AI Risk Management Framework that NIST is developing.

According to NIST's Reva Schwartz, the main distinction between the draft and final versions of the publication is the new emphasis on how bias manifests itself not only in AI algorithms and the data used to train them, but also in the societal context in which AI systems are used.

"Context is everything," said Schwartz, principal investigator for AI bias and one of the report's authors. "AI systems do not operate in isolation. They help people make decisions that directly affect other people's lives. If we are to develop trustworthy AI systems, we need to consider all the factors that can chip away at the public's trust in AI. Many of these factors go beyond the technology itself to the impacts of the technology, and the comments we received from a wide range of people and organizations emphasized this point."

Bias in AI can harm humans. AI can make decisions that affect whether a person is admitted into a school, authorized for a bank loan or accepted as a rental applicant. It is relatively common knowledge that AI systems can exhibit biases that stem from their programming and data sources; for example, machine learning software could be trained on a dataset that underrepresents a particular gender or ethnic group. The revised NIST publication acknowledges that while these computational and statistical sources of bias remain highly important, they do not represent the full picture.

A more complete understanding of bias must take into account human and systemic biases, which figure significantly in the new version. Systemic biases result from institutions operating in ways that disadvantage certain social groups, such as discriminating against individuals based on their race. Human biases can relate to how people use data to fill in missing information, such as a person's neighborhood of residence influencing how likely authorities would consider the person to be a crime suspect. When human, systemic and computational biases combine, they can form a pernicious mixture - especially when explicit guidance is lacking for addressing the risks associated with using AI systems.

"If we are to develop trustworthy AI systems, we need to consider all the factors that can chip away at the public's trust in AI. Many of these factors go beyond the technology itself to the impacts of the technology." -Reva Schwartz, principal investigator for AI bias

To address these issues, the NIST authors make the case for a "socio-technical" approach to mitigating bias in AI. This approach involves a recognition that AI operates in a larger social context - and that purely technically based efforts to solve the problem of bias will come up short.

"Organizations often default to overly technical solutions for AI bias issues," Schwartz said. "But these approaches do not adequately capture the societal impact of AI systems. The expansion of AI into many aspects of public life requires extending our view to consider AI within the larger social system in which it operates."

Socio-technical approaches in AI are an emerging area, Schwartz said, and identifying measurement techniques to take these factors into consideration will require a broad set of disciplines and stakeholders.

"It's important to bring in experts from various fields - not just engineering - and to listen to other organizations and communities about the impact of AI," she said.

NIST is planning a series of public workshops over the next few months aimed at drafting a technical report for addressing AI bias and connecting the report with the AI Risk Management Framework. For more information and to register, visit the AI RMF workshop page.

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There's More to AI Bias Than Biased Data, NIST Report Highlights

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As a step toward improving our ability to identify and manage the harmful effects of bias in artificial intelligence (AI) systems, researchers at the National Institute of Standards and Technology (NIST) recommend widening the scope of where we look for the source of these biases - beyond the machine learning processes and data used to train AI software to the broader societal factors that influence how technology is developed.

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How AI misrepresents culture through a facial expression

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Imagine a time traveler journeyed to various times and places throughout human history and showed soldiers and warriors of the periods what a "selfie" is.

This is the premise for a series of AI-generated images posted on r/midjourney. Below are a few examples of the images this prompt produced:

There are 18 images in the Reddit slideshow and they all feature the same recurring composition and facial expression. For some, this sequence of smiling faces elicits a sense of warmth and joyousness, comprising a visual narrative of some sort of shared humanity (so long as one pays no attention to the incongruousness of Spanish Conquistadors smiling happily next to Aztec warriors. Awkward.) But what immediately jumped out at me is that these AI-generated images were beaming a secret message hidden in plain sight. A steganographic deception within the pixels, perfectly legible to your brain yet without the conscious awareness that it's being conned. Like other AI " hallucinations," these algorithmic extrusions were telling a made up story with a straight face - or, as the story turns out, with a lying smile.

Why do you smile the way you do? A silly question, of course, since it's only "natural" to smile the way you do, isn't it? It's common sense. How else would someone smile?

As a person who was not born in the U.S., who immigrated here from the former Soviet Union, as I did, this question is not so simple. In 2006, as part of her Ph.D. dissertation, "The Phenomenon of the Smile in Russian, British and American Cultures," Maria Arapova, a professor of Russian language and cross-cultural studies at Lomonosov Moscow State University, asked 130 university students from the U.S., Europe, and Russia to imagine they had just made eye contact with a stranger in a public place - at the bus stop, near an elevator, on the subway, etc.

Which, she asked the participants, would you do next?

A) smile and then look away
B) look away
C) gaze at his eyes, then look away

90% of Americans and Europeans chose the option with a smile in it. Only 15% of Russians did.

How we smile, when we smile, why we smile, and what it means is deeply culturally contextual. In the 2018 Nautilus essay, "What a Russian Smile Means," French-American journalist Camille Baker writes about how the meaning of a smile differs across societies.

In 2015 Kuba Krys, a researcher at the Polish Academy of Sciences, studied the reactions of more than 5,000 people from 44 cultures to a series of photographs of smiling and unsmiling men and women of different races. He and his colleagues found that subjects who were socialized in cultures with low levels of "uncertainty avoidance" - which refers to the level at which someone engages with norms, traditions, and bureaucracy to avoid ambiguity - were more likely to believe that smiling faces looked unintelligent. These subjects considered the future to be uncertain, and smiling - a behavior associated with confidence - to be inadvisable. Russian culture ranks very low on uncertainty avoidance, and Russians rate the intelligence of a smiling face significantly lower than other cultures.

Krys's team also found that people from countries with high levels of government corruption were more likely to rate a smiling face as dishonest. Russians - whose culture ranked 135 out of 180 in a recent worldwide survey of corruption levels - rated smiling faces as honest with less frequency than 35 of the 44 cultures studied. Corruption corrupts smiling, too.

Russians interpret the expressions of their officials and leaders differently from Americans. Americans expect public figures to smile at them as a means of emphasizing social order and calm. Russians, on the other hand, find it appropriate for public officials to maintain a solemn expression in public, as their behavior is expected to mirror the serious nature of their work. A toothy "dominance smile" from an important American public figure inspires feelings of confidence and promise in Americans. Russians expect, instead, a stern look from their leaders meant to demonstrate "serious intentions, validity, and reliability."

Which is how an AI trained on a dataset dominated by a culture that takes photos like this:

Would insist that "Native American warriors" posing for a photo would have looked like this:

Rather than how actual historical photos look like:

Native American chiefs, 1865

Group of Apache Men, with Chief Al-Che-Zay (Middle Row, Dark Vest)

(Worth noting that these photos themselves were taken by photographers like Edward Curtis and others whose own colonizing point of view influenced how Native peoples were represented, so these images too come with an imposed, outside perspective.)

Or that "Ancient Polynesian Warriors" would have taken a selfie like this:

When the traditional Māori Haka ceremony looks like this:

Or that Soviet soldiers posing for a selfie would have looked like this:

When Eastern European soldiers posing for an actual selfie in 2023 look like this:

Every American knows to say "cheese" when taking a photo, and, therefore, so does the AI when generating new images based on the pattern established by previous ones. But it wasn't always like this. More than a century after the first photograph was captured, a reference to "cheesing" for photos first appeared in a local Texas newspaper in 1943. "Need To Put On A Smile?" the headline asked, "Here's How: Say 'Cheese.'" The article quoted former U.S. ambassador Joseph E. Davies who explained that this influencer photo hack would be "Guaranteed to make you look pleasant no matter what you're thinking [...] it's an automatic smile." Davies served as ambassador under Franklin D. Roosevelt to the U.S.S.R.

As the old Soviet joke goes, how can you tell that someone is an American in Russia?

They're smiling.

But how does AI tell when someone is most likely lying? They're smiling like an American.

In 2018, researchers at the University of Rochester conducted an experiment to see how deception is connected to facial expressions. Participants were paired up into describer and interrogator roles. The describer was shown an image and told to memorize it with as much detail as possible. They were then instructed to either lie or tell the truth about what they'd just seen to the interrogator, who was unaware of the instructions given to the describer. The recorded exchanges between 151 pairs of individual participants yielded 1.3 million frames of facial expressions. The researchers then used machine learning to automatically find patterns. Without any predetermined labels or categories, the results identified the expression most frequently associated with lying: a "high intensity version" of the Duchenne smile - a smile that extends to both the cheek/eye and mouth muscles.

The taut, grimacing, duplicitous rictus - the modern American smile - rose out of a great emotional shift in the 18th century, theorizes Christina Kotchemidova, who teaches theory, gender, and intercultural communication at Spring Hill College in Alabama. But it is also based on a lie.

As Baker writes:

Prior to this shift, [Kotchemidova] believes, the American emotional landscape revolved around negative emotions like sadness and melancholy, which were seen as indicative of compassion and nobleness. Informed by ideas from pre- and early Reformation European Christianity, both Americans and Europeans saw earthly suffering as noble and necessary for a happy afterlife. Literature, visual art, and theater in this period aimed to provoke sadness, and crying in public was commonplace in Europe. Diderot and Voltaire, Kotchemidova writes, were seen crying repeatedly.

The Age of Enlightenment pushed the culture in a different direction. As thinkers and artists embraced reason, they also began to believe that happiness was permissible during our earthly life as well as the afterlife. The culture of sadness began to be supplanted by one of cheerfulness, which in turn influenced a changing class structure. The emerging middle class took the ability to manage emotions as key to its identity. Business failures and sickness were linked to failures of emotional control, and cheerfulness to prosperity. Eventually, cheerfulness became a prerequisite for employment.

"The expectation was, you have to smile eight hours a day," a woman Baker calls Sofiya tells her. A 41-year-old Russian émigré who had been living in the United States for the past decade, Sofiya "was a proficient English speaker," Baker writes, but it was in her job as a bank teller that she "came face-to-face with her deficiency in speaking 'American.' This other English language, made up of not just words but also facial expressions and habits of conversation subtle enough to feel imagined. Smiling almost constantly was at the core of her duties as a teller. As she smiled at one customer after another, she would wince inwardly at how silly it felt. There was no reason to smile at her clients, she thought, since there was nothing particularly funny or heartwarming about their interactions. And her face hurt."

This confrontation with the culture clash of smiling for an Eastern European immigrant in America hits close to home. Which is why seeing the relentless parade of toothy, ahistorical, quintessentially American, "cheese" smiles plastered on the faces of every civilization in the world across time and space was immediately jarring. It was as if the AI had cast 21st century Americans to put on different costumes and play the various cultures of the world. Which, of course, it had.

In her groundbreaking book, How Emotions are Made: The Secret Life of the Brain, Lisa Feldman Barrett, a neuroscientist and psychology professor at Northeastern University writes:

Most scientific research on emotion is conducted in English, using American concepts and American emotion words (and their translations). According to noted linguist Anna Wierzbicka, English has been a conceptual prison for the science of emotion. "English terms of emotion constitute a folk taxonomy, not an objective, culture-free analytic framework, so obviously we cannot assume that English words such as disgust, fear, or shame are clues to universal human concepts, or to basic psychological realities." To make matters even more imperialistic, these emotion words are from twentieth-century English, and there's evidence that some are fairly modern. The concept of "Emotion" itself is an invention of the seventeenth century. Before that, scholars wrote about passions, sentiments, and other concepts that had somewhat different meanings.

Different languages describe diverse human experience in different ways - emotions and other mental events, colors, body parts, direction, time, spatial relations, and causality. The diversity from language to language is astonishing.... Not all cultures understand emotions as mental states. The Ifaluk of Micronesia consider emotions transactions between people. To them, anger is not a feeling of rage, a scowl, a pounding fist, or a loud yelling voice, all within the skin of one person, but a situation in which two people are engaged in a script - a dance, if you will - around a common goal. In the Ifaluk view, anger does not "live" inside either participant.

In the same way that English language emotion concepts have colonized psychology, AI dominated by American-influenced image sources is producing a new visual monoculture of facial expressions. As we increasingly seek our own likenesses in AI reflections, what does it mean for the distinct cultural histories and meanings of facial expressions to become mischaracterized, homogenized, subsumed under the dominant dataset? In the AI-generated visual future, will we know that Native Americans didn't smile for photos like WW2 U.S. Navy Officers?

Did U.S. WW2 Navy officers even for that matter?

Every 2 weeks a language is spoken by a human on Earth for the last time. A third of the world's languages have fewer than 1,000 speakers left. By the end of this century, 50% to 90% of languages are predicted to disappear. "When humanity loses a language, we also lose the potential for greater diversity in art, music, literature, and oral traditions," says Bogre Udell co-founder of the nonprofit, Wikitongues. And with them, we lose vital concepts for making sense of ourselves and our inner experiences. In the future, how will we know how to be who the algorithm doesn't show us we can be? Would we even dare to want to? And what would that kind of totalizing assimilation mean for global mental health, wellbeing, and the human experience in general?

It's so much easier for a machine to fabricate a new language (out of the raw material created by humans) -

Than for the diversity of human expression to survive algorithmic hegemony.

"The concept of a facial 'expression,'" writes Barrett, "Implies an internal feeling that seeks release in a set of facial movements." In flattening the diversity of facial expressions of civilizations around the world AI had collapsed the spectrum of history, culture, photography, and emotion concepts into a singular, monolithic perspective. It presented a false visual narrative about the universality of something that in the real world - where real humans have lived and created culture, expression, and meaning for hundreds of thousands of years - is anything but uniform.

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Imagine a time traveler journeyed to various times and places throughout human history and showed soldiers and warriors of the periods what a "selfie" is. This is the premise for a series of...

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Want to really understand large language models? Here’s a gentle primer.

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A jargon-free explanation of how AI large language models work

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A jargon-free explanation of how AI large language models work

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This article delves into the basics of word vectors and the transformer architecture, key components in these models. It also discusses the training process and the need for large datasets. (The article is designed to make complex topics accessible to a broad audience without using technical jargon.)

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When ChatGPT was introduced last fall, it sent shockwaves through the technology industry and the larger world. Machine learning researchers had been experimenting with large language models (LLMs) for a few years by that point, but the general public had not been paying close attention and didn't realize how powerful they had become.

Today, almost everyone has heard about LLMs, and tens of millions of people have tried them out. But not very many people understand how they work.

If you know anything about this subject, you've probably heard that LLMs are trained to "predict the next word" and that they require huge amounts of text to do this. But that tends to be where the explanation stops. The details of how they predict the next word is often treated as a deep mystery.

One reason for this is the unusual way these systems were developed. Conventional software is created by human programmers, who give computers explicit, step-by-step instructions. By contrast, ChatGPT is built on a neural network that was trained using billions of words of ordinary language.

As a result, no one on Earth fully understands the inner workings of LLMs. Researchers are working to gain a better understanding, but this is a slow process that will take years-perhaps decades-to complete.

Still, there's a lot that experts do understand about how these systems work. The goal of this article is to make a lot of this knowledge accessible to a broad audience. We'll aim to explain what's known about the inner workings of these models without resorting to technical jargon or advanced math.

We'll start by explaining word vectors, the surprising way language models represent and reason about language. Then we'll dive deep into the transformer, the basic building block for systems like ChatGPT. Finally, we'll explain how these models are trained and explore why good performance requires such phenomenally large quantities of data.

Word vectors

To understand how language models work, you first need to understand how they represent words. Humans represent English words with a sequence of letters, like C-A-T for "cat." Language models use a long list of numbers called a "word vector." For example, here's one way to represent cat as a vector:

[0.0074, 0.0030, -0.0105, 0.0742, 0.0765, -0.0011, 0.0265, 0.0106, 0.0191, 0.0038, -0.0468, -0.0212, 0.0091, 0.0030, -0.0563, -0.0396, -0.0998, -0.0796, ..., 0.0002]

(The full vector is 300 numbers long-to see it all, click here and then click "show the raw vector.")

Why use such a baroque notation? Here's an analogy. Washington, DC, is located at 38.9 degrees north and 77 degrees west. We can represent this using a vector notation:

Washington, DC, is at [38.9, 77]

New York is at [40.7, 74]

London is at [51.5, 0.1]

Paris is at [48.9, -2.4]

This is useful for reasoning about spatial relationships. You can tell New York is close to Washington, DC, because 38.9 is close to 40.7 and 77 is close to 74. By the same token, Paris is close to London. But Paris is far from Washington, DC.

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A jargon-free explanation of how AI large language models work

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Want to really understand large language models? Here's a gentle primer.

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Bryan Alexander's Substack is an ongoing investigation into how AI might impact higher education.

"My goal here is to help academics and the academically-adjacent or -curious better understand where AI might be taking colleges and university."

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Exploring where emerging AI might take colleges and universities. Click to read Bryan's Substack, a Substack publication with hundreds of subscribers.

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Ethan Mollick is an Associate Professor at the Wharton School of the University of Pennsylvania, where he studies and teaches innovation and entrepreneurship, and also examines the effects of artificial intelligence on work and education. He also leads Wharton Interactive, an effort to democratize education using games, simulations, and AI.

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One Useful Thing | Ethan Mollick | Substack

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Translating academic research into mostly useful insights, with some ephemera on the side. Mostly AI stuff recently. By Prof. Ethan Mollick. Click to read One Useful Thing, by Ethan Mollick, a Substack publication with tens of thousands of subscribers.

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This resource comes directly from OpenAI, the organization behind many AI technologies like ChatGPT. It provides foundational guidelines for educators on how to responsibly use AI tools in educational settings.