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Cyborgs and Centaurs: Academic Writing in the Age of Generative AI

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Cyborgs and Centaurs: Academic Writing in the Age of Generative AI

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Cyborgs and Centaurs: Academic Writing in the Age of Generative Artificial Intelligence – Simple Book Publishing

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Artificial Intelligence (AI) in College – Pathways to College Success

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We’re including this section on “AI in College” because it’s clear that generative AI (GenAI) tools, including platforms such as ChatGPT, are beginning to transform what student workflows and class instruction looks like in higher education. This emerging technology is reshaping what it means to be a student and professional, and yet we’re at a rather messy stage right now with little consensus on how these tools should be used—or whether they should be used at all. Some students and faculty actively avoid GenAI in the classroom. Others are not yet familiar with the wide range of tools and capabilities available to students and instructors. And some are embracing these new tools and actively experimenting with them.

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Student Guide to Artificial Intelligence

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Homepage - Student Guide to Artificial Intelligence

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Learn about Artificial Intelligence (AI) and how it’s transforming higher education. This guide explores what AI is, its applications, and how it’s shaping careers.

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Leading Through Disruption: Higher Education Executives… | AAC&U

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This report provides a comprehensive overview of the survey findings, outlines the challenges and opportunities of integrating AI into higher education…

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AI Commons at California State University

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AI Commons at California State University

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Lateral reading can (and should) be applied to all online sources, but you will find fewer pieces of information to assess through lateral reading when working with AI. While you can typically reach a consensus about online sources by searching for a source's publication, funding organization, author or title, none of these bits of information are available to you when assessing AI output. As a result, it is critical that you read several sources outside the AI tool to determine whether credible, non-AI sources can confirm the information the tool returned.

With AI, instead of asking "who's behind this information?" we have to ask "who can confirm this information?" In the video above, lateral reading is applied to an online source with an organization name, logo, URL, and authors whose identities and motivations can be researched and fact checked from other sources. AI content has no identifiers and AI output is a composite of multiple unidentifiable sources. This means you must take a look at the factual claims in AI content and decide on the validity of the claims themselves rather than the source of the claims.

Since AI output is not a single source of information but rather drawn from multiple sources that could be both factual and false, you will find it useful to break apart AI output into smaller components of information that can be evaluated independent of each other. For instance, let's see what happens when we ask ChatGPT to write an essay on Jim Henson's undergraduate studies at The Ohio State University.

Of course, many of you know that Jim Henson completed his undergraduate at UMD, not Ohio State, which illustrates a critical distinction to be made about AI; it will take what you provide it and try to answer your question as best it can, but it will NOT fact check you or spot incorrect assumptions in the prompt you give it. In paragraph 2 from the example above, the AI correctly states that Henson was involved in puppetry activities on campus, but is incorrect in stating this was at Ohio State. If one were to assume AI output is accurate throughout, they would not realize their own error in prompting the AI with the incorrect university.

The issues in this example are not just based on the flawed prompt. In paragraph 3, the AI describes a Dr. Richard Lederer who was a mentor of Henson's. A quick Google search reveals no mentions of a Dr. Richard Lederer at Ohio State or UMD. Further, the only Richard Lederer of note to come up is an author, linguist and speaker who seems to have no connection to Henson himself. This is an example of the AI "hallucinating" a seemingly factual answer that sounds plausible, but is unfounded upon some quick lateral reading.

Here's how to fact-check something you got from ChatGPT or a similar tool:

1. Break down the information. Take a look at the response and see if you can isolate specific, searchable claims. This is called fractionation.

2. Then it's lateral reading time! Open a new tab and look for supporting pieces of information. Here are some good sources to start with:

When searching for specific pieces of information: Google results or Wikipedia

When seeing if something exists: Google Scholar, UMD Discover, or Wikipedia

Tip: Some things to watch out for - is the AI putting correct information in the wrong context (like when it said that Texas A&M's tradition was a UMD one)? Is it attributing a fake article to a real author?

3. Next, think deeper about what assumptions are being made here

What did your prompt assume?

What did the AI assume?

Who would know things about this topic? Would they have a different perspective than what the AI is offering? Where could you check to find out?

4. Finally, make a judgment call. What here is true, what is misleading, and what is factually incorrect? Can you re-prompt the AI to try and fix some of these errors? Can you dive deeper into one of the sources you found while fact-checking? Remember, you're repeating this process for each of the claims the AI made - go back to your list from the first step and keep going!

For an example of this in action, take a look at the video at the bottom of the page.

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Research Guides: Artificial Intelligence (AI) and Information Literacy: Fact-checking AI with Lateral Reading

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Learn about how AI works and how to spot common errors AI tools tend to make. You'll also learn fact-checking and critical thinking strategies for AI, how to cite AI in an academic paper, and how to learn more in-depth about AI tools and issues.

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NJIT

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Responsible Use of Artificial Intelligence | Dean of Students and Campus Life

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Responsible Use of Artificial Intelligence

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Library Guides: Artificial Intelligence: Home

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A resource guide to understanding and learning about generative AI

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Digital Education Council AI Literacy Framework

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Digital Education Council AI Literacy Framework

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Digital Education Council AI Literacy Framework

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The Digital Education Council AI Literacy framework takes a human-centred approach to AI literacy, emphasising the importance of human skills such as critical thinking, creativity, and emotional intelligence.

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The Artificial Intelligence Assessment Scale (AIAS): A Framework for Ethical Integration of Generative AI in Educational Assessment

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The Artificial Intelligence Assessment Scale (AIAS): A Framework for Ethical Integration of Generative AI in Educational Assessment

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Journal of University Teaching & Learning Practice

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View of The Artificial Intelligence Assessment Scale (AIAS): A Framework for Ethical Integration of Generative AI in Educational Assessment

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Return to Article Details The Artificial Intelligence Assessment Scale (AIAS): A Framework for Ethical Integration of Generative AI in Educational Assessment

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AI and College Writing: An Orientation for Students (Draft)

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AI and College Writing: An Orientation for Students (Draft)

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By Anna Mills

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AI and College Writing: An Orientation (Draft)

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Ethical Principles AI Framework for Higher Education

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Ethical Principles AI Framework for Higher Education

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2025 by Wynants, S., Childers, G., De La Torre Roman, Y. , Budar-Turner, D., & Vasquez, P. is licensed under CC BY-NC 4.0

Introduction

The ETHICAL Principles AI Framework for Higher Education is designed to provide a flexible and adaptable foundation for the responsible use of Artificial Intelligence (AI) across diverse academic contexts. This framework recognizes the rapidly evolving nature of AI technologies and the varying needs of different departments, colleges, and institutions.

Our goal is to offer a set of guiding principles that can be readily interpreted and applied to individual circumstances, ensuring relevance and utility as AI continues to advance. The framework intentionally avoids rigid definitions, allowing users to contextualize and adapt these principles to their unique environments, academic settings, and as technologies change.
By providing this framework under a Creative Commons license, we encourage its widespread use, modification, and adaptation. We invite departments, colleges, and other institutions to tailor these principles to their specific needs, fostering a culture of responsible AI use that aligns with their values and objectives.

It is important to note that this framework is not prescriptive. Instead, it serves as a starting point for developing more detailed policies, guidelines, and educational programs. The specific implementation of these principles, including how to address potential AI limitations and biases, should be determined by individual units or institutions based on their unique contexts and evolving understanding of AI technologies.

As laws and regulations surrounding AI-generated content and its use in academia continue to develop, this framework provides a stable ethical foundation that can accommodate future legal and technological changes. We encourage users of this framework to stay informed about current developments and to interpret these principles in light of the most up-to-date information available.

By adopting and adapting this ETHICAL Principles AI Framework, institutions can foster a thoughtful, responsible, inclusive, and innovative approach to AI integration in higher education, promoting ethical considerations while embracing the potential of these transformative technologies.

What follows is an accessible infographic of the framework, and then a more detailed description of each of the ethical principles of the framework, ending with recommended practical application strategies for implementing the framework across an institution.

This image is described in full below.

ETHICAL Principles AI Framework for Higher Education

Exploration and Evaluation

Critically assess and evaluate AI tools and outputs.

Transparency and Accountability

Maintain openness about AI use in academic and administrative contexts

Human-Centered Approach

Prioritize human judgment and decision-making in AI applications.

Integrity and Academic Honesty

Uphold academic and professional standards when using AI and promote responsible AI use in learning and research

Develop and follow clear guidelines for responsible AI use in academic work

Properly attribute AI-generated content in assignments and research

Educate the campus community on citation, attribution, and ethical use methods of AI-generated content and AI tools

Use AI tools in ways that align with institutional values and ethics

Develop best practices for integrating AI tools into curriculum and pedagogy

Encourage faculty to design curriculum and assessments that promote responsible use of AI and enhance student learning, as appropriate

Promote ethical considerations in AI development and deployment on campus

Continuous Learning and Innovation

Foster ongoing education about AI technologies and their implications.

Provide and engage in regular AI literacy training and professional development for the campus community

Integrate AI literacy into curriculum across disciplines

Stay informed about emerging AI technologies and their potential impacts

Adapt AI practices based on new developments and ethical and environmental considerations

Foster an environment that encourages AI-driven innovation in education

Foster a culture of sharing lessons learned from AI implementations

Support the development of novel AI applications tailored to educational needs

Collaborate with industry partners to bring cutting-edge AI technologies to campus

Support faculty and student initiatives to explore innovative AI uses in education

Encourage interdisciplinary collaborations in AI exploration and application

Establish partnerships with other educational institutions to share best practices and lessons learned in AI implementation

Accessibility and Inclusivity

Ensure AI use promotes equity and inclusive opportunities

Legal and Ethical Compliance

Adhere to relevant laws, regulations, and ethical standards in AI use.

Respect data privacy and security regulations when using AI systems

Comply with copyright and intellectual property laws when using AI-generated content

Follow institutional and professional ethical guidelines in AI applications

Stay informed about evolving AI regulations and adjust practices accordingly

Develop ethical guidelines specific to AI use in research, teaching, and administration

Develop clear policies on the ownership and use of AI-generated intellectual property created within the
institution

Establish an AI ethics review board to oversee high-impact AI projects

Practical Application Strategies for the ETHICAL Principles AI Framework

To implement this framework effectively across higher education, we encourage:

1. For Faculty/Instructors:

Apply the framework when incorporating AI tools in curriculum, teaching, and assessment

Engage in peer and campus discussions about ethical AI applications

Participate in AI professional development offered by the institution

2. For Staff:

Apply the framework when considering and using AI tools for administrative tasks

Engage in peer and campus discussions about ethical AI applications

Participate in AI professional development offered by the institution

3. For Students:

Review your syllabus and engage with your instructors to understand how best to integrate AI tools in each specific context. Recognize that AI policies and expectations may vary across courses,
instructors, and even individual assignments

Engage in class discussions about ethical AI applications

Participate in AI literacy programs offered by the institution

4. For Administrators:

Use the framework when creating AI policies/guidelines for the campus, evaluating AI applications
for the campus, and communicating AI information to the campus

Participate in AI professional development

Establish a centralized resource hub for campus AI exploration, experimentation, and innovation
information

Recommendation for Continuous Improvement of this Framework:

Regularly evaluate the effectiveness of the ETHICAL AI framework

Continue to gather feedback from all users to refine and improve the AI framework

Adapt the framework as new AI technologies and challenges emerge

The ETHICAL Principles AI Framework - Project Summary

As part of the 2024-2025 AAC&U Institute on AI, Pedagogy, and the Curriculum , a California State University, Fullerton (CSUF) team pursued this project to advance equitable AI integration in higher education while addressing critical concerns about bias, academic integrity, and technological accessibility. Their work stemmed from a commitment to prepare students for an AI-driven world while upholding CSUF's strategic focus on social justice and inclusive pedagogy.

The team, led by Shelli Wynants, consisted of experts from various CSU Fullerton fields:

Philip Vasquez: Director of Diversity, Inclusion, and Equity Programs

Greg Childers: Director of CSUF's GE Program

Donna Budar-Turner: Director of Student Conduct

Yessica De La Torre: Director of Assessment

Shelli Wynants: Faculty Champion & Quality Online Inclusive Learning Design Coordinator

Overall Purpose of the Framework

The ETHICAL Principles AI Framework for Higher Education aims to provide a flexible and adaptable foundation for the responsible use of Artificial Intelligence across diverse academic contexts. It offers a set of guiding principles that can be readily interpreted and applied to individual circumstances, ensuring relevance and utility as AI continues to advance, while intentionally avoiding rigid definitions to allow users to contextualize and adapt these principles to their unique environments and as technologies change. (See Introduction of the Framework for more information)

Project Development Stages

Foundation through Collaboration (Fall 2024)

The multidisciplinary team engaged with the AAC&U Institute on AI, Pedagogy, and Curriculum to:

Learn about national standards, diverse campus policies and projects

Gather insights from peer institutions

Examine campus survey results to assess campus-wide AI needs

Framework Co-Creation (2024-2025)

Reading a variety of existing literature on ethics and AI in education

Brainstorming with AI to gain insights on consistencies across the literature

Engaging in multiple dialogue sessions with team, institute partners, and our institute advisor/mentor (Jose Bowen)

Multiple drafts, with collected feedback from a variety of campus constituents

Developed through:

Campus-Wide Sharing of Framework (February 2025)

Core output: The ETHICAL Principles Framework with seven pillars.

Case Study Development (examples of how units/departments/campus individuals have used and adapted the framework for their work)

Professional Development Expansion (providing more learning options to address areas on the framework that campus would like to explore more)

Continuous Improvement (learning from others who have used and adapted the framework, as it has a Creative Commons license and will be shared widely)

Our team will be exploring

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ETHICAL Principles AI Framework for Higher Education | CSU AI Commons

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2025 by Wynants, S., Childers, G., De La Torre Roman, Y. , Budar-Turner, D., & Vasquez, P. is licensed under CC BY-NC 4.0

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San Francisco State University

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Crafting Your GenAI & AI Policy: A Guide for Instructors

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Crafting Your GenAI & AI Policy: A Guide for Instructors

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GenAI & AI Policy:

A Guide for Instructors

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The SOLVE platform hosts resources related to best practices for remote instruction as well as toolkits that guide you through the implementation of adaptive or other digital learning tools. All of our resources are created or vetted by our network partners and will help you improve student experiences and outcomes in gateway courses, whether they are in-person or online. Visit SOLVE now to browse or search through our collection of resources and toolkits.

This spring, higher education institutions engaged in a rapid and unprecedented movement to remote teaching and learning that exposed a new cohort of faculty with no prior experience teaching online ...

College students' satisfaction dropped sharply after schools shifted to all-online courses during the COVID-19 pandemic. Undergraduate students struggled to stay motivated and missed receiving feedback from instructors and collaborating with ...

This spring, higher education institutions engaged in a rapid and unprecedented movement to remote teaching and learning that exposed a new cohort of faculty with no prior experience teaching online ...

College students' satisfaction dropped sharply after schools shifted to all-online courses during the COVID-19 pandemic. Undergraduate students struggled to stay motivated and missed receiving feedback from instructors and collaborating with ...

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The SOLVE platform hosts resources related to best practices for remote instruction as well as toolkits that guide you through the implementation of adaptive or other digital learning tools. All of our resources are created or vetted by our network partners and will help you improve student experiences and outcomes in gateway courses, whether they are in-person or online.

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Harvard Business School Academic Standards of Conduct: Using ChatGPT & Artificial Intelligence (AI) Tools

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Harvard Business School Academic Standards of Conduct: Using ChatGPT & Artificial Intelligence (AI) Tools

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2.1.2 Using ChatGPT & Artificial Intelligence (AI) Tools - MBA - Harvard Business School

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The University supports responsible experimentation with generative AI tools, but there are important considerations to keep in mind when using these tools, including information security and data privacy, compliance, copyright and academic integrity. Generative AI is a rapidly evolving technology and HBS' policies may be updated from time to time.

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Stanford University Generative AI Policy Guidance

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Stanford University Generative AI Policy Guidance

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UCLA Guidance for the Use of Generative AI

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UCLA Guidance for the Use of Generative AI

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Guidance for the Use of Generative AI – UCLA Center for the Advancement of Teaching

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Rice University Honor Council Limits Use of ChatGPT

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Rice University Honor Council Limits Use of ChatGPT

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Honor Council limits use of ChatGPT - The Rice Thresher

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The Honor Council sent an email to all undergraduates on April 11, announcing an Honor Code amendment explicitly prohibiting the use of artificial intelligence software such as ChatGPT without proper citation. Additionally, the email clarified professors’ right to ban the use of AI software for thei…

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University of Wisconsin—Madison Considerations for Using AI in the Classroom

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University of Wisconsin—Madison Considerations for Using AI in the Classroom

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Created by Laura Schmidli with contributions from Molly Harris, Alison Caffrey, Antonella Caloro, Jonathan Klein, Luis Loya, David Macasaet, Erika Schock, and Parker Story.
In December 2022, ChatGPT made headlines. This new conversational artificial intelligence (AI) program (or chatbot) has many in higher education discussing AI's potential threat to our work and purpose. It has inspired both awe and fear. To read more, see References and Further Reading.

Like with any new technology, one approach instructors can take in the classroom is to transparently make use of it. This allows you to move beyond stress over whether or not students are using AI, and instead have a voice in when, why, and how you and your class use it. You can even invite students to consider and explore AI with you (Tufekci, 2022). Although this approach may not work or be right for all instructors and courses, this strategy has the advantages of encouraging student participation in a larger societal question and helping students to develop their own ethical grounding and practical understanding of academic integrity related to AI.

Now Available!

ChatGPT has caught the attention of L&S Dean Eric Wilcots. Hear about this and more on the first episode of the L&S Exchange Podcast.

Steps to Consider

1. Reflect on the threats and opportunities you perceive for your teaching and work from AI. People have raised concerns for student academic integrity as well as for the longer term impact on student motivation, engagement, and knowledge retention. However, there are also potential benefits. AI may save time and effort on routine tasks, provide a new perspective on a problem, or generate content that can be analyzed or critiqued.

2. Test AI out for yourself and consider its strengths and weaknesses. If you are able to, chat with ChatGPT during its open testing phase or try other AI language models. (Note: ChatGPT requires creating an account using an email address and phone number, and will only be available for free testing for a limited time.) If provided with a prompt or assessment question from your class, how does the AI perform? What is it good at, and where does it struggle? For example, currently ChatGPT can write with correct grammar and confident flow, but cannot create accurate citations or write with much depth. This is because it creates word patterns, with some variability or randomness, but does not generate meaning (Warner, 2022). ChatGPT is also limited to producing about 5 paragraphs of writing at once, and cannot currently produce complex code or reliably perform accurate mathematical calculations (Wolfram, 2023). However, it can be asked series of questions to iteratively produce better work. See OpenAI's guidance for designing prompts for ChatGPT.

3. Consider revisions to your assignments. Making your questions more personal, reflective, specific, local, or complex (i.e., requiring higher order thinking) may make it more difficult for student to use AI to adequately complete. But more importantly, these changes can make your questions more interesting and valuable to your students.

4. Consider data and privacy policies. Before using ChatGPT or any other third party tool in the classroom, instructors and students should review the data retention and privacy policies. For ChatGPT, the OpenAI FAQ is helpful. Using ChatGPT requires creating an account using an email address and a cell phone number. When designing an assignment where students can use ChatGPT, always provide an alternative for students who don't want to share their data with a third party tool (i.e., students shouldn't have to share their data to be successful). Alternatives could include making part of the assignment optional, the instructor engaging with the AI on behalf of students, students using a different AI language model that does not require personal information, or students using another supplemental source (e.g., interview with a person, responses on social media, review article, etc.) in place of ChatGPT.

5. Identify and communicate opportunities in your course. Some students are already using AI on their own. By acknowledging and working with AI, we can guide students in considering the capabilities of these tools, surface differing opinions among students around fairness, and help them align use of these tools with their own ethical frameworks.

Next, read the crowdsourced ideas below to help you consider opportunities in your own course.

Share Your Ideas for Using AI

We'd love to add your knowledge to this guide. What plans, ideas, questions, or concerns do you have for using AI in your classroom?
Share Your Idea

Join a Discussion on ChatGPT

The Teaching Academy is hosting a conversation about ChatGPT and teaching and learning on Friday, February 24. This conversation is open to all, and will take place via Zoom.
Learn More and Register

Compare Exam Questions: Individually students write one multiple choice exam question and ask text-generating AI to write a second. In a larger group, students analyze which submitted questions are AI-written, which are human-written, and evaluate which provide a better assessment of learning.

Evaluate AI Output: The instructor uses AI to generate work, like a thesis, short analytical paper, theater dialogue, computer code, image, or even musical composition. In groups students analyze the sample work created by AI, with particular attention to evidence, sources, perceived bias, or other important elements for your course. Students can then revise it for improvement in groups, and share back revisions for comparison.

AI Drafting Process: Students use AI to generate a draft of a simple writing assignment. Students then analyze the AI's writing, focusing on accuracy, bias, or other characteristics important in your course. You may also ask students to improve the AI's draft to complete a second draft.

For specific help, Citing AI: If students are using AI in your class assignments, discuss if and how AI should be cited. For example, students may cite it as a source or disclose their use of it in a disclaimer, footnote, or appendix that includes the prompts they created. Ask a Librarian or contact your Subject Librarian.

Creative Writing with Images: Students or instructors use image-generating AI as part of a creative writing process. Students can use AI to quickly visualize creative descriptions from their writing, or students or instructors can use AI to generate images that prompt creative writing elaboration.

Make Instructor Expectations Clear: Whatever your aims for using (or prohibiting) the use of AI within your classroom, make your expectations clear to students at the semester's start. This may include adding a statement to your existing academic integrity statement, and reviewing expectations with students in class. Students should also have the option to not use any tool that requires personal information shared with a third party not contracted by the University.

Connect with Help

We are happy to connect with instructors about using AI in your classroom, designing assignments, or brainstorming solutions for your course.
Schedule a Meeting

AI & Academic Misconduct

We recognize that the advancement of AI creates concern about academic integrity enforcement. While the focus of this guide is different, the following resources may be helpful when you suspect students are using AI to bypass work.

References & Further Reading

Barbaro, M., et al. (2022, December 16). Did Artificial Intelligence Just Get Too Smart? The New York Times. https://www.nytimes.com/2022/12/16/podcasts/the-daily/chatgpt-openai-artificial-intelligence.html

ChatGPT. (2023). In Wikipedia. https://en.wikipedia.org/w/index.php?title=ChatGPT&oldid=1132927055

Jolles, Ward. (2023, January 4). New AI technology writes essays for you-And UW-Madison students are using it. Retrieved January 5, 2023, from https://www.wkow.com/news/new-ai-technology-writes-essays-for-you-and-uw-madison-students-are-using-it/article_4b051b8a-8ca2-11ed-8d15-3b4d9fa4403b.html

Marche, S. (2022, December 6). The College Essay Is Dead. The Atlantic. https://www.theatlantic.com/technology/archive/2022/12/chatgpt-ai-writing-college-student-essays/672371/

Roose, K., Newton, C., Land, D., Szuchman, P., Powell, D., Ittoop, E., Niemisto, R., & Moxley, A. (2022, December 9). Can ChatGPT Make This Podcast? The New York Times. https://www.nytimes.com/2022/12/09/podcasts/can-chatgpt-make-this-podcast.html

Thunström, A. O. (2022, June 10). We Asked GPT-3 to Write an Academic Paper about Itself;Then We Tried to Get It Published. Scientific American. https://www.scientificamerican.com/article/we-asked-gpt-3-to-write-an-academic-paper-about-itself-mdash-then-we-tried-to-get-it-published/

Tufekci, Z. (2022, December 15). Opinion | What Would Plato Say About ChatGPT? The New York Times. https://www.nytimes.com/2022/12/15/opinion/chatgpt-education-ai-technology.html

USING AI TO WRITE ESSAYS ISN'T CHEATING, STUDENT SAYS. (2022, May 16). Retrieved November 13, 2022, from https://www.canta.co.nz/canta/newsarticle/115524?newsfeedid=1453013

Warner, J. (2022, December 11). ChatGPT Can't Kill Anything Worth Preserving. The Biblioracle Recommends. Retrieved January 3, 2023, from https://biblioracle.substack.com/p/chatgpt-cant-kill-anything-worth

Wolfram, S. (2023, January 9). Wolfram|Alpha as the Way to Bring Computational Knowledge Superpowers to ChatGPT. Retrieved January 20, 2023, from https://writings.stephenwolfram.com/2023/01/wolframalpha-as-the-way-to-bring-computational-knowledge-superpowers-to-chatgpt/

Acknowledgements

Thank you to Dorothea Salo, Dave Bloom and other UW Libraries staff, several anonymous commenters, and IDC team members for crowdsourcing and discussing these ideas.
Published January 5, 2023
Last updated January 31, 2023

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Using Artificial Intelligence in the Classroom

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Created by Laura Schmidli with contributions from Molly Harris, Alison Caffrey, Antonella Caloro, Jonathan Klein, Luis Loya, David Macasaet, Erika Schock, and Parker Story. In December 2022, ChatGPT made headlines. This new conversational artificial intelligence (AI) program (or chatbot) has many in higher education discussing AI's potential threat to our work and purpose.

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How AI could save (not destroy) education

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How AI could save (not destroy) education

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Sal Khan believes that artificial intelligence (AI) has the potential to transform education for the better. "We're at the cusp of using AI for probably the biggest positive transformation that education has ever seen," he said in his 2023 TED talk.

Get a glimpse of a new era in education-one where every student has access to an AI-powered personal tutor and every teacher has an AI teaching assistant.

The "Two Sigma Problem" and AI's solution:

Benjamin Bloom's 1984 "Two Sigma" study highlighted the benefits of one-to-one tutoring, which resulted in a two standard deviation improvement in students' performance. Bloom referred to this finding as the "Two Sigma Problem," since providing one-to-one tutoring to all students has long been unattainable due to cost and scalability issues.

Sal shared how AI has the potential to scale this tutoring economically and provide personalized instruction to students on a global level with the help of an AI-powered assistant. During his talk, Sal gave a live demo of Khan Academy's new AI-powered guide, Khanmigo.

Khanmigo: A comprehensive AI tutor

Instead of worrying about students using AI to cheat, Sal said we should focus on the positive use cases. Khanmigo not only detects students' mistakes, but it also identifies misconceptions in their understanding and provides effective feedback. It can help students with math and computer programming exercises and can provide context-aware help for video content.

Khanmigo encourages Socratic dialogue and debate by providing a safe space for students to fine-tune their arguments, which can give them more confidence in class. It has been designed to write with students rather than for them. It encourages collaborative story writing and provides feedback on drafts, which helps students improve their writing abilities.

Want to see Khanmigo in action? Khan Academy invites users based in the United States to explore AI's capabilities by subscribing to Khanmigo.

A teaching assistant, powered by AI

For teachers, Khanmigo acts as a comprehensive teaching assistant by explaining answers and teaching methods, helping with lesson planning, and creating progress reports. This allows teachers to spend more time and energy on one-on-one interactions with their students.

Positive and appropriate interactions

We believe that AI can transform education, but we also understand that it comes with limitations and risks.That's why we clearly communicate these limitations to every parent, teacher, and child who uses Khanmigo. We limit the amount of interaction individuals can have with the AI per day. Additionally, each child's chat history and activities are visible to parents or guardians and teachers so they can stay informed and involved in their child's education. Our platform also uses moderation technology to detect inappropriate interactions, and when it does, it sends an automatic email alert to an adult.

Your role in the future of AI

Sal emphasized that everyone has a role to play in shaping the future of AI in education. "We all have to fight like hell for the positive use cases," he said.

Through responsible and innovative AI applications like Khanmigo, education has the potential to undergo an unprecedented transformation, accelerating learning and fostering human potential like never before.

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Sal Khan's 2023 TED Talk: AI in the classroom can transform education - Khan Academy Blog

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Experience the transformative power of AI in education with Sal Khan's 2023 TED Talk. Discover Khan Academy's new AI tutor, Khanmigo, and how it provides personalized instruction to every student, encouraging positive and appropriate interactions.

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Artificial Intelligence | 60 Minutes Full Episodes

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Guidelines for Syllabus Statements About AI

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Guidelines for Syllabus Statements About AI

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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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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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ChatGPT and other large language models are going to be more important in your life and business than your smartphone, if you use them right. ChatGPT can tutor your child in math, generate a meal plan and recipes, write software applications for your business, help you improve your personal cybersecurity, and that is just in the first hour that you use it. This course will teach you how to be an expert user of these generative AI tools. The course will show amazing examples of how you can tap into these generative AI tools' emergent intelligence and reasoning, how you can use them to be more productive day to day, and give you insight into how they work.

Large language models respond to instructions and questions posed by users in natural language statements, known as "prompts". Although large language models will disrupt many fields, most users lack the skills to write effective prompts. Expert users, who understand how to write good prompts, are orders of magnitude more productive and can unlock significantly more creative uses for these tools. This course introduces students to the patterns and approaches for writing effective prompts for large language models. Anyone can take the course and the only required knowledge is basic computer usage skills, such as using a browser and accessing ChatGPT. Students will start with basic prompts and build towards writing sophisticated prompts to solve problems in any domain. By the end of the course, students will have strong prompt engineering skills and be capable of using large language models for a wide range of tasks in their job, business, personal life, and education, such as writing, summarization, game play, planning, simulation, and programming.

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Offered by Vanderbilt University. ChatGPT and other large language models are going to be more important in your life and business than your ... Enroll for free.

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Generative AI Syllabus Statement Tool (Seaver College) Generative artificial intelligence tools - software that creates new text, images, computer code, audio, video, and other content by following patterns learned from large datasets - burst into international attention in November 2022 when OpenAI made its generative text tool ChatGPT widely available to the public.

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AI in Teaching and Learning at OSU ORG 13951 Canvas Modules

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AI in Teaching and Learning at OSU ORG 13951 Canvas Modules

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A stoplight model for guiding student AI usage

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A stoplight model for guiding student AI usage

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Giving students guidelines for AI use on assignments-via a green, yellow, or red light-provides clarity around this powerful technology.

Artificial intelligence (AI) has the potential to revolutionize the way we teach and learn. However, with great power comes great responsibility. It's crucial for us as educators to establish clear guidelines for students on when and where AI can be used in the classroom. This ensures that the technology serves as an aid to learning, rather than a crutch or a tool of academic dishonesty.

As teachers returned to school with an eagerness to incorporate AI into their instruction, our coaching team began helping them to develop clear guidelines for students on when it's appropriate to use AI and when it is not. We believe that every summative assignment should include AI guidelines as part of the instructions, guidelines, and rubrics that students are already given.

Red Light: AI Usage Is Not Permitted in This Activity

This option is straightforward and leaves no room for interpretation. It is particularly useful for assessments or activities where the primary goal is to evaluate individual student understanding and skills. By explicitly stating that AI collaboration is not allowed, educators can maintain the integrity of the assessment process.

For assignments that fall under the "red light" category, think of traditional quizzes, exams, or short-answer questions that are designed to assess individual knowledge and skills. These are activities where the primary goal is to evaluate each student's understanding of the material. Is the activity designed to assess individual understanding? Is it a formative assessment where collaboration could skew the results?

It's essential to explain the rationale behind this restriction. Students are more likely to comply with rules when they understand the reasoning behind them. In these cases, teachers can have conversations with students about the importance of individual assessment. They can explain how this type of evaluation helps identify areas where improvement is needed and why academic honesty is a skill that will serve them well throughout their educational journey and beyond. Providing context helps students see the bigger picture and appreciate the importance of adhering to guidelines.

Providing clarity is especially important when students are working independently from home. When students are not physically present in the classroom, it can be tempting for them to seek AI assistance when they shouldn't. Thus, it becomes paramount for educators to set clear expectations about when AI collaboration is permitted and when it is not. Just as teachers have guidelines for plagiarism, citing sources, etc., so can they have guidelines around AI use.

While it's true that some students may not always adhere to the guidelines when working from home, it's crucial to recognize that teaching responsible AI usage is a valuable educational opportunity. Instead of simply restricting AI access, we should aim to empower students with the knowledge and understanding they need to make responsible choices.

Yellow Light: permission from teacher required before using AI

This option offers a balanced approach, allowing for the possibility of AI usage while maintaining a level of oversight. The "yellow light" option is particularly useful for long-term projects that require extensive research, group activities where individual contributions are part of a collective grade, or case studies that could benefit from multiple perspectives.

Incorporating AI tools into the classroom, such as Magicschool.ai, Diffit, and Perplexity, can enhance the learning experience in these scenarios. These technologies offer valuable resources that, when used with guidance and permission, can amplify students' abilities to explore, learn, and contribute collectively. It is in these nuanced situations that the "yellow light" approach shines, facilitating the responsible integration of AI into the learning process, promoting teamwork, and fostering a deeper understanding of the subject matter.

When students ask for permission to use AI, it opens the door for a meaningful dialogue. Teachers can ask students to articulate what they hope to achieve with the aid of AI and how it aligns with the objectives of the activity. This conversation also provides an opportunity to discuss the ethical considerations of using AI, such as the importance of citing sources when incorporating external content. By requiring permission, educators can ensure that students have a clear understanding of the activity's objectives and how AI can appropriately aid in achieving them.

Green light: Students are encouraged to use AI software

This option is the most open, encouraging students to explore the capabilities of AI in their learning journey. Assignments that are ideal for the "green light" option include creative writing tasks where AI can serve as a brainstorming tool, research projects that involve gathering and analyzing large sets of data, or activities that encourage innovation, such as coding projects or design tasks.

However, the openness of this option necessitates a robust framework for ethical usage and academic integrity. Before diving in, students are required to discuss their planned approach with the teacher. This ensures that the use of AI complements rather than replaces human effort. The conversation also serves as a checkpoint for discussing how to properly indicate AI collaboration in their work and reinforcing the importance of transparency and ethical conduct.

Managing the "green light" option when dealing with a large number of students will certainly require planning. One strategy that teachers can employ to effectively oversee this open approach is to establish online discussion forums or collaboration spaces where students can share their plans for integrating AI into their work. These platforms can provide students with a space to post their ideas, seek feedback from both peers and the teacher, and foster a sense of collaboration.

Conversations and Considerations

These guidelines serve as a framework for teachers when assigning work. They are communicated explicitly at the outset of any assignment or activity where AI collaboration could be a factor. The advance notice is crucial; students are more likely to adhere to rules when they are aware of them beforehand and understand the reasoning behind them.

The conversation between educator and student during the "permission phase" of the yellow and green light options is invaluable. It serves not just as a checkpoint but as a teachable moment, helping students learn the nuances of academic integrity and ethical technology usage.

A valuable strategy to manage a full roster of students is to schedule regular check-in sessions, either individually or in small groups. During these sessions, teachers can discuss each student's AI usage plan, offer personalized guidance, and ensure that ethical standards are upheld.

The integration of AI in education is not a matter of if but when. As educators, we have the responsibility to guide our students in navigating this new landscape ethically and effectively. By being clear about the guidelines for AI usage in classroom activities, we can foster an environment that encourages innovation while maintaining academic integrity.

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A Stoplight Model for Guiding Student AI Usage

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Teachers can create AI usage guidelines for different activities so students know when and how they can use these tools.

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Debate: Will ChatGPT Do More Harm than Good? With Gary Marcus and Keith Teare

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Debate: Will ChatGPT Do More Harm than Good? With Gary Marcus and Keith Teare

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Debate: Will ChatGPT Do More Harm Than Good? with Gary Marcus and Keith Teare

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Cambridge Launches AI Research Ethics Policy

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Cambridge Launches AI Research Ethics Policy

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by Sophie White and Andrew Scheuber

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CAMBRIDGE, England--()--New guidelines will help researchers use generative AI tools like ChatGPT while upholding academic standards around transparency, plagiarism, accuracy and originality.

The rules are set out in the first AI ethics policy from Cambridge University Press and apply to research papers, books and other scholarly works.

They include a ban on AI being treated as an 'author' of academic papers and books published by Cambridge University Press.

The move provides clarity to academics amid concerns about flawed or misleading use of powerful large language models like ChatGPT in research, alongside excitement about its potential.

Mandy Hill, Managing Director for Academic at Cambridge University Press & Assessment, said: "Generative AI can enable new avenues of research and experimentation. Researchers have asked us for guidance to navigate its use.

"We believe academic authors, peer reviewers and editors should be free to use emerging technologies as they see fit within appropriate guidelines, just as they do with other research tools.

"Like our academic community, we are approaching this new technology with a spirit of critical engagement. In prioritising transparency, accountability, accuracy and originality, we see as much continuity as change in the use of generative AI for research.

"It's obvious that tools like ChatGPT cannot and should not be treated as authors.

"We want our new policy to help the thousands of researchers we publish each year, and their many readers. We will continue to work with them as we navigate the potential biases, flaws and compelling opportunities of AI."

R. Michael Alvarez , Professor of Political and Computational Social Science at the California Institute of Technology, said: "Generative AI introduces many issues for academic researchers and educators. As a series editor for Cambridge University Press, I appreciate the leadership the Press is taking to outline guidelines and policies for how we can use these new tools in our research and writing. I anticipate that we will be having this conversation about the opportunities and pitfalls presented by generative AI for academic publishing for many years to come."

Professor Alvarez and his Caltech collaborators use AI, including LLMs, to detect online harassment, trolling and abusive behaviour on social media platforms and in videogames such as Call of Duty, as well as to combat misinformation.

Professor Alvarez is co-editor of Quantitative and Computational Methods for Social Science, published by Cambridge University Press.

The Cambridge principles for generative AI in research publishinginclude that:

AI use must be declared and clearly explained in publications such as research papers, just as scholars do with other software, tools and methodologies.

AI does not meet the Cambridge requirements for authorship, given the need for accountability. AI and LLM tools may not be listed as an author on any scholarly work published by Cambridge.

Any use of AI must not breach Cambridge plagiarism policy. Scholarly works must be the author's own, and not present others' ideas, data, words or other material without adequate citation and transparent referencing.

Authors are accountable for the accuracy, integrity and originality of their research papers, including for any use of AI.

Each year Cambridge University Press publishes tens of thousands research papers in more than 400 peer-reviewed journals and 1,500 research monographs, reference works and higher education textbooks.

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Cambridge Launches AI Research Ethics Policy

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Cambridge launches ChatGPT ethics policy

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Rather than weaken student effort, artificial intelligence can help prepare students for the real world by encouraging critical thinking-with a few caveats. Here's advice from psychology instructors about how to use ChatGPT and other AI technology wisely

Cite this

Abramson, A. (2023, June 1). How to use ChatGPT as a learning tool. Monitor on Psychology, 54(3). https://www.apa.org/monitor/2023/06/chatgpt-learning-tool

As technology advances, psychologists can find new opportunities to innovate in the field and in education. But the introduction of new technologies, such as artificial intelligence (AI)-the simulation of human intelligence by computers-can feel daunting. For psychology instructors in particular, the advent of a rapidly evolving AI software called ChatGPT (and the updated version, GPT-4) has introduced new challenges-but experts say it's also ripe with potential to help students learn in new ways and prepare them for careers after college.

ChatGPT, a chatbot software launched by the AI company OpenAI in November 2022, synthesizes online data and communicates it in a conversational way. Unlike a search engine, ChatGPT can write verse in the style of Shakespeare, dole out dating advice, and-especially concerning to educators-answer test questions and write essays. Early reviews of GPT-4, the next iteration of OpenAI's large language model, indicate increases in the software's capabilities.

A New York Times article from earlier this year highlights instances of cheating with ChatGPT in college courses and instructors redesigning curricula to prevent academic dishonesty. Some schools are blocking the technology altogether: Earlier this year, the New York City Department of Education banned the use of ChatGPT based on concerns that it could hinder student learning ( Huang, K., New York Times, January 16, 2023).

Some educators, however, see ChatGPT as an opportunity rather than a threat. In an LA Times op-ed published in early 2023, psychologist Angela Duckworth, PhD, argues against banning the bot, explaining that it and similar technologies are here to stay-and that instructors should learn how to incorporate it into curricula (Los Angeles Times, January 19, 2023).

Many psychology instructors are already experimenting with the software, recognizing that ChatGPT could be a useful tool to prepare students for the real world where critical thinking is more important than rote memorization.

"We have a choice here to lean in or run away, like we had a choice about whether we wanted to use calculators in statistics class," said Kathy Hirsh-Pasek, PhD, a professor of psychology at Temple University in Philadelphia. "We now know people can learn using calculators, so the question is: How do we increase critical thinking while also embracing what ChatGPT can do?"

Integrating any new tool into the classroom should be done judiciously, and ChatGPT is no exception. Educators must consider ethics, cheating, and equity, just as they would when integrating other technologies into their courses. But with the right approach, ChatGPT can be a useful-and as some psychologists argue, revolutionary-tool to prepare students for their future careers. Here are some insights from psychology instructors about using ChatGPT to help students learn.
[Related: APA Style: How to cite ChatGPT]

Consider course goals

Every psychology course has different learning objectives. When determining whether-and how-to incorporate ChatGPT into your course, think about your specific goals as an instructor. "There are going to be classes for which using GPT would be inappropriate, but in other settings, it could be a useful learning tool," said Daniel Oppenheimer, PhD, a professor of psychology at Carnegie Mellon University in Pittsburgh.

Ask yourself: What are you trying to teach your students, and what abilities are you hoping to measure? It may help to think of ChatGPT like a calculator. If you were teaching simple addition, then a calculator might hinder learning. But in a calculus course, the same tool could free up cognitive resources to help students perform the more advanced skills they need to learn.

"We can do math better and faster now that we have calculators," said Jaclyn Siegel, PhD, a postdoctoral research scholar and an adjunct psychology faculty professor at San Diego State University. "It's harder to imagine a similar scenario happening in psychology, but maybe we'll be able to think more creatively because we have basic facts at our fingertips."

Similarly, if you're teaching an introductory class, ChatGPT might pose a threat to students learning basic information. Spelling and grammar tools, for example, could get in the way of evaluating whether students can write. But if your goal is to encourage good ideas, "the other tools make essays easier to read and help instructors understand if students understand the material," said Oppenheimer.

In classes that you determine would not benefit from using ChatGPT, using online proctors or programs that restrict access to ChatGPT may sometimes be appropriate.

Encourage critical thinking

ChatGPT offers fast and easy access to information, which you may deem inappropriate in certain learning scenarios. But finding ways to incorporate AI tools in your course could actually help prepare students for the real world, where they'll need to apply concepts rather than simply recall facts. "We want to train students how to think like a psychologist rather than how to know what a psychologist knows," Oppenheimer said.

The bot may be best used to help students think critically about topics they're already familiar with, said Gary Lupyan, PhD, a professor of psychology at the University of Wisconsin-Madison. "Like talking with a colleague, it can be an interactive partner that's useful in areas you know a lot about already, so you can critically evaluate the suggestion without blindly copying it," he said.

For example, Oppenheimer teaches a course on human intelligence and stupidity, in which he encourages students to compare GPT-generated text with human-generated text. Hirsh-Pasek actually requires her honors psychology students to use ChatGPT, asking them to use ChatGPT for a first draft and edit a second draft with critiques, corrections, and additions. "A really good essay is not just a synthesis of information, but also the ability to support a thesis," she said. "ChatGPT teaches students to ask better questions and then defend those questions, which could help them become real scientists."

ChatGPT can also be used to incite classroom or lab discussion, said James W. Pennebaker, PhD, a professor of psychology at the University of Texas at Austin. For example, you could ask the bot why one theory is better than another. Then, ask again why the second theory is better. "Students can learn to appreciate other arguments and see both sides objectively before establishing an opinion," he said.

Using ChatGPT in your course not only encourages critical thinking about class concepts, but it also encourages your students to be technologically literate in an increasingly tech-centric world. "We should be thinking about the world we're preparing our students to enter and what habits of thinking we want to instill in them so they can successfully represent the discipline of psychology," said Oppenheimer. "That means figuring out how to incorporate novel technology they will be using in the real world into classroom exercises."

Be aware of the cons

All technology sparks concern about academic dishonesty. If your students have access to computers during an exam, then they theoretically have access to ChatGPT. Be aware ChatGPT can easily answer test questions because of how it synthesizes information. Steering clear of multiple-choice exams can help resolve that concern, but open-ended assessments may be harder to grade, especially with large classes.

Lupyan cautions against overhauling your test questions to trick the system. If you try to design exams with questions ChatGPT can't answer, you may miss questions your students need to be learning. "Questions that require synthesis and contrast are the type you want to be asking students and the type these models are excelling at," he said.

It may be more worthwhile to implement practices shown to reduce all kinds of cheating, said Oppenheimer. Be clear about your expectations for the class and motivate students to be engaged with the material. Breaking assignments and papers into chunks that come together over time can make it more difficult to plagiarize, especially if you regularly provide feedback that students need to incorporate into their work. Just as important, offer support to students who may be struggling with the material.

Take time at the beginning of the semester to explain why academic integrity is so important in your class and beyond. "Tell them that while cheating may temporarily make things easier, it has a short time horizon, because they aren't actually learning skills they need in their careers," Oppenheimer said.

Communicate expectations

As ChatGPT and other AI technologies advance, universities will develop official conduct codes about their use. Until then, it's up to you to implement and communicate expectations about how students can and can't use these tools in your class. In your syllabus, offer specific scenarios in which it's acceptable to use them and when it's not, and be sure your teaching assistants are on the same page.

If you're OK with students using ChatGPT in certain scenarios, Siegel emphasizes the importance of equity. She teaches a large online course for which she can't prevent students from using notes or searching online for answers. As a result, she conducts open-book exams and allows students to use the internet or textbook to answer questions. (She emphasizes forcing students to put their cameras on during tests is inequitable if students don't have sufficient internet bandwidth.)

To prevent savvier students from getting better grades, she introduces the whole class to ChatGPT to ensure that they know how to use it. "Curves are based on averages, so everyone should have knowledge and access to the same tools and materials," she said.

Try it out

Before determining how ChatGPT could help or hinder your students' learning, spend time with it yourself. Test it out and see what it can (and can't) do. If you're not ready to bring the technology to your course, use it for planning or administrative purposes. Lupyan, for example, recently asked ChatGPT to suggest titles for a grant he wrote, and Siegel used it to generate active lessons for teaching about methods in sexology research for her human sexual behavior course.

Whether you run a lab or teach a course, encourage your students to try it out for fun, too. Pennebaker's students, for example, have used ChatGPT to write haikus about psychological theories or rap battles between two well-known scholars. "With education, the more people can get their hands dirty and play with ideas, the better they learn," he said.

Be patient as the technology continues to evolve. As of early 2023, ChatGPT has been intermittently inaccessible due to the overwhelming number of users. Consider signing up for auto-notifications so you know when it's available.

Navigating new technologies like ChatGPT can be time-consuming and even overwhelming, but taking steps to understand them could empower both you and your class. "I get scared of new things, too, but they offer us a chance to grow," said Hirsh-Pasek. "I challenge all of us to be the learners we want our students to be."

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Artificial intelligence can help prepare students for the real world by encouraging critical thinking-with a few caveats.

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AI is Changing Every Aspect of Psychology: Here's What to Watch For

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AI is Changing Every Aspect of Psychology: Here's What to Watch For

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In another study, Bigman and his colleagues analyzed interactions at a hotel in Malaysia employing both robot and human workers. After hotel guests interacted with robot workers, they treated human workers with less respect (working paper).

"There was a spillover effect, where suddenly we have these agents that are tools, and that can cause us to view humans as tools, too," he said.

Many questions remain about what causes people to trust or rely on AI, said Sethumadhavan, and answering them will be crucial in limiting harms, including the spread of misinformation. Regulators are also scrambling to decide how to contain the power of AI and who bears responsibility when something goes wrong, Bigman said.

"If a human discriminates against me, I can sue them," he said. "If an AI discriminates against me, how easy will it be for me to prove it?"

AI in the clinic

Psychology practice is ripe for AI innovations-including therapeutic chatbots, tools that automate notetaking and other administrative tasks, and more intelligent training and interventions-but clinicians need tools they can understand and trust.

While chatbots lack the context, life experience, and verbal nuances of human therapists, they have the potential to fill gaps in mental health service provision.
[Related: Let's Get Technical reviews of apps and tools for practicing psychologists]
"The bottom line is we don't have enough providers," Jackson said. "While therapy should be for everyone, not everyone needs it. The chatbots can fill a need." For some mental health concerns, such as sleep problems or distress linked to chronic pain, training from a chatbot could suffice.

In addition to making mental health support more affordable and accessible, chatbots can help people who may shy away from a human therapist, such as those new to therapy or people with social anxiety. They also offer the opportunity for the field to reimagine itself, Jackson said-to intentionally build culturally competent AIs that can make psychology more inclusive.

"My concern is that AI won't be inclusive," Jackson said. "AI, at the end of the day, has to be trained. Who is programming it?"

Other serious concerns include informed consent and patient privacy. Do users understand how the algorithm works, and what happens to their data? In January, the mental health nonprofit Koko raised eyebrows after it offered counseling to 4,000 people without telling them the support came from ChatGPT-3. Reports have also emerged that getting therapy from generative language models (which produce different text in each interaction, making it difficult to test for clinical validity or safety) has led to suicide and other harms.

But psychology has AI success stories, too. The Wysa chatbot does not use generative AI, but limits interactions to statements drafted or approved by human therapists. Wysa does not collect email addresses, phone numbers, or real names, and it redacts information users share that could help identify them.

The app, which delivers cognitive behavioral therapy for anxiety and chronic pain, has received Breakthrough Device Designation from the United States Food and Drug Administration. It can be used as a stand-alone tool or integrated into traditional therapy, where clinicians can monitor their patients' progress between sessions, such as performance on cognitive reframing exercises.

"Wysa is not meant to replace psychologists or human support. It's a new way to receive support," said Smriti Joshi, PhD, the company's chief psychologist.

AI also has the potential to increase efficiency in the clinic by lowering the burden of administrative tasks. Natural language processing tools such as Eleos can listen to sessions, take notes, and highlight themes and risks for practitioners to review. Other tasks suited to automation include analysis of assessments, tracking of patient symptoms, and practice management.

Before integrating AI tools into their workflow, many clinicians want more information on how patient data are being handled and what apps are safe and ethical to use. The field also needs a better understanding of the error rates and types of errors these tools tend to make, Miner said. That can help ensure these tools do not disenfranchise groups already left out of medical systems, such as people who speak English as a second language or use cultural idioms of distress.

Miner and his colleagues are also using AI to measure what's working well in therapy sessions and to identify areas for improvement for trainees (npj Mental Health Research, Vol. 1, No. 19, 2022). For example, natural language models could search thousands of hours of therapy sessions and surface missed opportunities to validate a patient or failures to ask key questions, such as whether a suicidal patient has a firearm at home. Training software along these lines, such as Lyssn -which evaluates providers on their adherence to evidence-based protocols-is starting to hit the market.

"To me, that's where AI really does good work," Miner said. "Because it doesn't have to be perfect, and it keeps the human in the driver's seat."

Transforming research

For researchers, AI is unlocking troves of new data on human behavior-and providing the power to analyze it. Psychologists have long measured behavior through self-reports and lab experiments, but they can now use AI to monitor things like social media activity, credit card spending, GPS data, and smartphone metrics.

"That actually changes a lot, because suddenly we can look at individual differences as they play out in everyday behavior," said personality psychologist and researcher Sandra Matz, PhD, an associate professor at Columbia Business School.

Matz combines big data on everyday experiences with more traditional methods, such as ecological momentary assessments (EMAs). Combining those data sources can paint a picture of how different people respond to the same situation, and ultimately shape personalized interventions across sectors, for instance in education and health care.

AI also opens up opportunities for passive monitoring that may save lives. Ross Jacobucci, PhD, and Brooke Ammerman, PhD, both assistant professors of psychology at the University of Notre Dame, are testing an algorithm that collects screenshots of patients' online activity to flag the use or viewing of terms related to suicide and self-harm. By pairing that data with EMAs and physiological metrics from a smart watch, they hope to build a tool that can alert clinicians in real time about patients' suicide risk.

"The golden goose is passive sensing," Jacobucci said. "How can that inform, not only who is at risk, but more importantly, when they're at risk?"

Natural language processing models are also proving useful for researchers. A team at Drexel University in Philadelphia has shown that GPT-3 can predict dementia by analyzing speech patterns ( Agbavor, F., & Liang, H., PLOS Digital Health, Vol. 1, No. 12, 2022). Cognitive psychologists are testing GPT's performance on canonical experiments to learn more about how its reasoning abilities compare to humans ( Binz, M., & Schulz, E., PNAS, Vol. 120, No. 6, 2023). Griffiths is using GPT as a tool to understand the limits of human language.

"These models can do a lot of things that are very impressive," Griffiths said. "But if we want to feel safe in delegating tasks to them, we need to understand more about how they're representing the world-and how it might differ from the way we think about it-before that turns into a problem."

With their toolbox for understanding intelligent systems, psychologists are in the perfect position to help. One big question moving forward is how to prepare graduate students to collaborate more effectively with the computer scientists who build AI models.

"People in psychology don't know the jargon in computer science and vice versa-and there are very few people at the intersection of the two fields," Jacobucci said.

Ultimately, AI will present challenges for psychologists, but meeting those challenges carries the potential to transform the field.

"AI will never fully replace humans, but it may require us to increase our awareness and educate ourselves about how to leverage it safely," Joshi said. "If we do that, AI can up the game for psychology in so many ways."

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AI is changing every aspect of psychology. Here's what to watch for

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Psychologists and their skills are irreplaceable, but AI chatbots can make therapy more accessible and less expensive, AI tools can automate administrative tasks, and on the research side, synthetic intelligence is offering new ways to understand human intelligence.

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Application of artificial intelligence chatbots, including ChatGPT, in education, scholarly work, programming, and content generation and its prospects

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Application of artificial intelligence chatbots, including ChatGPT, in education, scholarly work, programming, and content generation and its prospects

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This study aims to explore ChatGPT's (GPT-3.5 version) functionalities, including reinforcement learning, diverse applications, and limitations. ChatGPT is an artificial intelligence (AI) chatbot powered by OpenAI's Generative Pre-trained Transformer (GPT) model. The chatbot's applications span education, programming, content generation, and more, demonstrating its versatility. ChatGPT can improve education by creating assignments and offering personalized feedback, as shown by its notable performance in medical exams and the United States Medical Licensing Exam. However, concerns include plagiarism, reliability, and educational disparities. It aids in various research tasks, from design to writing, and has shown proficiency in summarizing and suggesting titles. Its use in scientific writing and language translation is promising, but professional oversight is needed for accuracy and originality. It assists in programming tasks like writing code, debugging, and guiding installation and updates. It offers diverse applications, from cheering up individuals to generating creative content like essays, news articles, and business plans. Unlike search engines, ChatGPT provides interactive, generative responses and understands context, making it more akin to human conversation, in contrast to conventional search engines' keyword-based, non-interactive nature. ChatGPT has limitations, such as potential bias, dependence on outdated data, and revenue generation challenges. Nonetheless, ChatGPT is considered to be a transformative AI tool poised to redefine the future of generative technology. In conclusion, advancements in AI, such as ChatGPT, are altering how knowledge is acquired and applied, marking a shift from search engines to creativity engines. This transformation highlights the increasing importance of AI literacy and the ability to effectively utilize AI in various domains of life.

Keywords: Artificial intelligence; Literacy; Reproducibility of results; Search engine; Writing

Background

Generative Pre-trained Transformer (GPT) is a natural language generation model developed by OpenAI. It learns to predict the next word in a given text, generating meaningful text that resembles human-written content [1]. The performance of GPT is contingent upon the number of parameters it employs. GPT-3.5, released on November 30, 2022, utilizes approximately 1,500 times more parameters (175 billion) than GPT-1 (117 million), which was first released in 2018.

ChatGPT is a chatbot built upon GPT-3.5. It comprehends sentences entered by users, generates relevant responses, and communicates with users utilizing everyday language, akin to human-to-human conversation. While GPT-3.5 has parameters equivalent to GPT-3 (175 billion), engendering negligible performance discrepancies, it was optimized for conversation by applying reinforcement learning from human feedback (RLHF). Understanding ChatGPT, which boasts a diverse spectrum of applications encompassing education, research, and programming, and discerning its domains of applicability and limitations will be instrumental in effectively harnessing this artificial intelligence (AI) chatbot.

Objectives

This review aims to present perspectives on topics that merit attention by examining various use cases and limitations of ChatGPT. Specifically, it explains ChatGPT's reinforcement learning and describes its use cases, limitations, and perspectives. This information can be helpful when utilizing AI-based natural language processing models, such as ChatGPT, in education and academic fields. It will also aid in understanding new models as they emerge.

Ethics statement

Since this was not a human population study, neither approval by the institutional review board nor obtainment of informed consent was required.

Improvement of ChatGPT's performance through reinforcement learning

In contrast to supervised or unsupervised learning, which occurs within a static framework where the data for learning are predetermined and unchanging, acquiring proficiency in a specific task through dynamic interactions with an uncertain environment constitutes reinforcement learning. This form of learning involves an agent situated within an environment, discerning the present state and ascertaining the action that maximizes potential rewards from a set of possible choices. The operational sequence of reinforcement learning is as follows: first, an agent, defined within an environment, observes the current state and executes an action; second, the agent adapts as the environmental state evolves, thereby altering the reward; third, the agent, guided by the modified rewards, learns to prioritize actions that yield more significant benefits, identifying the optimal course of action [2].

Additionally, the RLHF technique has been incorporated into ChatGPT [3]. When augmented with human feedback, reinforcement learning captures human linguistic patterns and cultural nuances. Through the integration of RLHF, ChatGPT achieves a level of sentence construction so naturally human-like that it becomes virtually indistinguishable from human communication (Fig. 1) [3].

Why are people enthusiastic about ChatGPT?

The genuine public interest in ChatGPT, an AI chatbot designed by OpenAI, is attributed to its multifaceted potential revealed following its release on November 30, 2022. Remarkably, the user base of ChatGPT reached 1 million within just 5 days of its launch and 2 million users within 2 weeks. This rapid adoption rate is significantly shorter than that of other primary services such as Netflix, which took 3.5 years; AirBnB, which took 2.5 years; and Facebook, which took 10 months to achieve the same milestone. This phenomenon represents an unprecedented achievement in the realm of AI services. In comparison, Copilot (https://github.com/features/copilot), an AI tool for coding assistance on GitHub, reached the 1 million user mark in six months, while DALL-E 2 (https://openai.com/dall-e-2/), OpenAI's AI service for image creation, achieved this feat in approximately 2.5 months.

Upon its release, the explosive interest in ChatGPT can be credited to its robust performance across various applications, including the development of chatbots, essay composition, translation, content generation, and text summarization. Additionally, its capability to respond naturally to interactive queries has been a significant factor. The distinctions between ChatGPT and conventional search engines are delineated in Table 1.

ChatGPT is rapidly gaining a large and growing user base online, as the free service makes it easy for anyone interested in AI to try it out in chat and share their results on social media. In the future, OpenAI will offer a paid service called "ChatGPT Plus"[4]-a pilot subscription plan for ChatGPT, a conversational AI that can chat with users, answer follow-up questions and challenge incorrect assumptions.

Education

AI can create assignments and presentations and provide in-class discussions, questions, and personalized feedback. A major AI service like ChatGPT has the potential to be a great educational tool if utilized well. For example, in a parasitology exam, although ChatGPT's knowledge and interpretation ability were not comparable to medical students in Korea, its performance was excellent, with a correct answer rate of 60.1% [5]. ChatGPT's performance on the United States Medical Licensing Exam, consisting of Step 1, Step 2 CK, and Step 3, was at or near the cut score for all 3 examinations [6]. Of course, the best way to use AI is to use it as a complement rather than a replacement for human teachers. To appropriately adapt to an increasingly AI-enabled learning environment, students need digital training to recognize information sources, learn how to use automated AI models, and understand the limitations of automatic text.

Simultaneously, concerns exist about plagiarism and ghostwriting, the unreliability of results, copyright issues, educational gaps due to technological advancement, and decreased learning ability. Educators are concerned about students' reliance on AI. Using ChatGPT to solve writing or computer coding assignments may hinder students' learning, and New York City public schools have blocked access to ChatGPT on campus [7]. In response to these issues, OpenAI launched the AI Text Classifier-"a fine-tuned GPT model that predicts how likely it is that a piece of text was generated by AI from a variety of sources, such as ChatGPT" [8]. Teachers can use this tool to assess the students' essays or reports. Furthermore, it can be used by scholarly journal editors to screen for AI-generated texts.

Writing research articles and journal publishing

A variety of tasks can be performed from the research design stage to research writing using ChatGPT, including summarizing abstracts into a certain number of words, suggesting creative research titles, discussing experimental results, creating a research table of contents, recommending future research ideas, writing articles on specific topics, grammar correction of written content, and translation. Gao et al. [7] tested whether a scientist could detect 50 medical research abstracts produced by ChatGPT and found that a plagiarism checker found 0% of the abstracts, an AI output detector found 66%, and the scientist found 68%. In a review of the role of AI in drug discovery, Blanco-Gonzalez et al. [8] described the caveats of using ChatGPT for scientific writing. They said, "As an assistant to write scientific papers, ChatGPT has several advantages, including its capacity to generate and optimize text quickly, as well as to help users with several tasks, including organizing information or even connecting ideas in some cases. However, this tool is in no way ideal for generating new content. Our revision of the text generated by the AI, following our instructions, required major edits and corrections, including the replacement of nearly all the references since the ones provided by the software were clearly incorrect" [8]. A paper by O'Connor [9] on AI platforms in nursing education stated that the first five paragraphs were written by ChatGPT, and it also listed ChatGPT as a co-author. ChatGPT's co-author inclusion later sparked a debate about ChatGPT's authorship [10].

OpenAI said that "while ChatGPT's ability to generate text in multiple languages may be useful for certain language-specific applications, such as content creation or language learning, it is not recommended to use ChatGPT for translation purposes. For accurate and reliable translations, it is recommended to use specialized translation tools and technologies" [11]. However, ChatGPT shows a significant level of performance compared to existing translators and can be used for teaching various foreign languages, including English, because it goes beyond simple translation and explains corrections and grammatical errors. It can also perform some level of translation and proofreading for academic publications. Later, more complete proofreading by a professional should be commissioned [12].

Programming

ChatGPT can perform various programming tasks, such as writing simple program code, annotating, finding errors in the code (typos or undefined code), checking the reason for an error code, fixing the error code, guiding the user to install a program, and providing guidance on how to update a program. An example of programming with ChatGPT is shown in Supplement 1.

Content creation

ChatGPT can go beyond simply answering users' questions and create various content in creative forms, such as movie screenplays, novels, song lyrics, product flyers, advertising scripts, financial reports, contracts, proposals, and course curricula.

Implementing creative ideas

ChatGPT can be used in endless ways depending on what one asks it, and in addition to the above uses, one can engage in the following kinds of creative tasks and documents. Tasks that can be delivered include the following: cheer up a depressed person, suggest a new product name, get podcast guest suggestions, make sure writing is free of racial bias, get dating tips, get gift ideas, make a workout plan, analyze text for sentiment, automatically comment on a restaurant blog, generate a list of questions for hiring, explain a complex concept (e.g., quantum physics to an elementary school student), and so forth.

The following types of text can be created: essays, news articles, blog posts, technical reports, business plans, funding applications, product manuals, user manuals, legal documents, contracts, resumes, cover letters, poems, novels, short stories, speeches, sermons, travel guides, textbooks, diaries, letters of recommendation, job ads, applications, brochures, and so on.

Functional limitations: Is ChatGPT fair and accurate?

ChatGPT can generate creative and professional answers not only to engineering problems, but also to literary, philosophical, and aesthetic problems, depending on how the user asks the question, and it provides results different from existing AI chatbot services. Through the application of RLHF, ChatGPT is recognized as a "game changer" that will change the search engine market with its smooth conversation and excellent answer performance.

However, ChatGPT still has some imperfections and is subject to unintentional bias, which poses a risk. It is trained on data from before 2021 and may respond inaccurately to events after 2022. Because ChatGPT bases its answers on a large training data set, it has the potential to generate misinformation or biased content. Because it has more English data than other languages, it is more accurate for English queries. We are concerned that the application of RLHF may easily mimic human flaws and mistakes (Fig. 2) [13]. In particular, ChatGPT's answers sometimes seem fairly logical, but it may give nonsensical answers (hallucination issue) or inaccurate answers, like seeing a hallucination that does not exist. Because of ChatGPT's high rate of incorrect answers and generally plausible but poor-quality answers, the developer of the Q&A site Stack Overflow has banned ChatGPT-generated answers from being posted for the time being.

Service limitations: Can ChatGPT generate sustainable revenue?

While ChatGPT has attracted 1 million users in a very short period compared to other services and is estimated to have over 500 million users, it is limited by the fact that it is a free service and does not have a significant revenue model. ChatGPT was able to reach 1 million users in a short period because it is a free service; anyone can quickly sign up for it, and users share and spread interesting results online due to the simplicity of the question method and excellent answer performance. The significance of ChatGPT lies not in its short-term user acquisition, but rather in the fact that it marks the beginning of generative AI. ChatGPT Plus, a paid service version, will be released in the future, and various customized services can be provided, so it will be necessary to develop a top-tier service that can ensure sustainability.

Can ChatGPT be a tool for innovation?

Technology is a tool that is neither good nor evil, and it is up to the user to decide how they want to use it. Therefore, for an AI service like ChatGPT to serve as a tool for innovation, it is necessary to always keep in mind the risks and side effects of using AI and make efforts to minimize them.

Overcoming limitations will help democratize AI: ChatGPT's inaccurate and biased responses and errors in logically describing misinformation or nonsensical responses are expected to improve through repeated trial-and-error interactions. ChatGPT already uses the Moderation API, an AI-powered moderation system, to block discriminatory and hate speech. Thus, if a question is asked that is not appropriate, it will refuse to respond to discriminatory, offensive, or inappropriate questions, including racist, sexist, homophobic, transphobic, or otherwise discriminatory or hateful questions. ChatGPT is a language model, and its accuracy can be improved with further training and professional validation.

The future release of GPT-4 will accelerate the progress of super-scale AI: It has been predicted that GPT-4 will have 100 trillion parameters (571 times the number of GPT-3 parameters), equivalent to the number of human synapses (Note: the release of GPT-4 has already taken place on March 14, 2023).

Less effort to gain knowledge

Knowledge refers to "a clear perception or understanding of an object gained through learning or practice," and human knowledge is accumulated by learning from one's own experiences and the experiences of others transmitted through various media. Therefore, learning is a set of processes that must be performed to build one's knowledge, and knowledge cannot exist without learning or experience. The advent of search engines has dramatically reduced the time and cost of learning. AI services such as ChatGPT have revolutionized the definition of knowledge by eliminating the human learning process for knowledge acquisition.

From the search engine era to the creativity engine era

Until now, AI has been dominated by discriminative AI rather than generative AI due to technical issues. Discriminative models are suitable for analytical tasks such as image recognition based on supervised learning, while generative models are suitable for creative tasks such as image generation based on unsupervised learning. Generative AI has been limited by several factors over the years, including the difficulty of running models, the need for sophisticated workload balancing to manage computer resources and avoid bottlenecks, and the prohibitive cost of using cloud computing. However, with new technologies, more data, and cheaper computing power, it is now easier than ever to build generative AI. More than 180 AI tools exist for different types of creativity, including language, visual and art, audio and music, and science (Fig. 3) [14]. The emergence of powerful AI tools is expected to impact the lives of billions of workers, with widespread unemployment, some jobs being replaced, and others being expanded or reinvented in unexpected ways.

The role of humans will change as technology advances. "The internet, with its easy access to information, robs us of the concentration and memory that comes from reading books, and while we may excel at information processing and decision-making, the brain's habit of focusing on something and remembering it is declining" [15]. The advent of the automobile has made human mobility easier, the advent of computers has augmented our problem-solving abilities, and the advent of the internet may have reduced our attention span. Still, it has also created new value through connectivity. For example, with the advent of smartphones, it has become less important to remember a large number of phone numbers, and possession of information is not a power in itself. Nonetheless, the ability to find and connect scattered information is even more necessary. In the future, children will become AI natives who use AI to solve their questions. As AI services become more ubiquitous, "how well you handle AI" will become an essential competitive advantage for future generations, who will experience AI in every aspect of their lives and feel comfortable asking AI when they have questions.

Editors' note

This narrative review is an invited one. I found Dr. Tae Won Kim's report in Korean with this title from the research report database of the National Information Society Agency of Korea. I cordially asked him to publish this report in English in the Journal of Educational Evaluation for Health Professions. I appreciate his accepting my proposal. It is a short but insightful introductory review of the application of ChatGPT to education and other fields. I hope that readers, mainly health professions educators, will comprehend ChatGPT more easily and benefit from this new platform for their works while understanding its advantages and limitations. Out of above descriptions, the release of GPT-4 has already taken place on March 14, 2023.

Supplement 1. ChatGPT's (2023 Jan 9 version) answer to the inquiry, "Could you generate code for machine learning in Python?" [cited 2023 Jan 23, 8:30 PM (Seoul time)].

jeehp-20-38-suppl.txt

Fig. 1.

Fig. 2.

A diagram illustrating the 3 steps of the training method: (1) supervised fine-tuning (SFT), (2) reward model (RM) training, and (3) reinforcement learning via proximal policy optimization (PPO) on this reward model. Blue arrows indicate that this data is used to train one of the models. In Step 2, boxes A-D are samples from the models that get ranked by labelers. From Ouyang L, et al. Training language models to follow instructions with human feedback. arXiv [Preprint] 2022 Mar 4. https://doi.org/10.48550/arXiv.2203.02155 [ 13].

Fig. 3.

Table 1.

Distinctions between ChatGPT and conventional search engines

Artificial intelligence technology

Uses language modeling, an artificial intelligence technology, to generate answers to user questions

Provides information through keyword searching

Generative

Can generate new information about user questions, providing more generative answers than traditional search engines

Unable to create new information

Interaction

User-friendly interactions can help the user understand, as it can answer questions

No interaction with users, as it provides information through keyword searches

Questions and context comprehension

Natural language processing technology understands users' questions to provide results that fit their intentions, remembers users' previous questions, and answers flexibly considering their associations.

Provides information through keyword search rather than understanding and answering user questions, and provides information independently for each search

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This study aims to explore ChatGPT's (GPT-3.5 version) functionalities, including reinforcement learning, diverse applications, and limitations. ChatGPT is an artificial intelligence (AI) chatbot powered by OpenAI's Generative Pre-trained Transformer (GPT) model. The chatbot's applications span education, programming, content generation, and more, demonstrating its versatility.

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Charting the Terrain of Artificial Intelligence: a Multidimensional Exploration of Ethics, Agency, and Future Directions

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Charting the Terrain of Artificial Intelligence: a Multidimensional Exploration of Ethics, Agency, and Future Directions

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Abstract

This comprehensive analysis dives deep into the intricate interplay between artificial intelligence (AI) and human agency, examining the remarkable capabilities and inherent limitations of large language models (LLMs) such as GPT-3 and ChatGPT. The paper traces the complex trajectory of AI's evolution, highlighting its operation based on statistical pattern recognition, devoid of self-consciousness or innate comprehension. As AI permeates multiple spheres of human life, it raises substantial ethical, legal, and societal concerns that demand immediate attention and deliberation. The metaphorical illustration of humans as "shepherds" of AI, coupled with a thought-provoking examination of "agency without intelligence," engenders deep reflections on the future of human-AI interplay and the associated philosophical, ethical, and practical ramifications. The paper further elucidates critical challenges associated with AI, such as ethical dilemmas, "agency without intelligence," the role of human oversight, and the environmental impact of AI technologies. In the pursuit of solutions, the paper maps prospective directions, emphasizing the need for robust AI policies, improved AI literacy, sustainable AI practices, and a continued dialogue on the multifaceted issues surrounding AI. Through a holistic exploration of AI, this paper underlines the necessity for a multidisciplinary, collaborative approach to chart an ethical, beneficial, and sustainable AI future, enabling society to harness the potential of AI responsibly while mitigating the associated risks.

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Sikkim University, Gangtok, India

Partha Pratim Ray & Pradip Kumar Das

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Ray, P.P., Das, P.K. Charting the Terrain of Artificial Intelligence: a Multidimensional Exploration of Ethics, Agency, and Future Directions. Philos. Technol. 36, 40 (2023). https://doi.org/10.1007/s13347-023-00643-6
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Charting the Terrain of Artificial Intelligence: a Multidimensional Exploration of Ethics, Agency, and Future Directions - Philosophy & Technology

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This comprehensive analysis dives deep into the intricate interplay between artificial intelligence (AI) and human agency, examining the remarkable capabilities and inherent limitations of large language models (LLMs) such as GPT-3 and ChatGPT. The paper traces the complex trajectory of AI's evolution, highlighting its operation based on statistical pattern recognition, devoid of self-consciousness or innate comprehension.

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How Generative AI Tools Help Transform Academic Research

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How Generative AI Tools Help Transform Academic Research

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How Generative AI Tools Help Transform Academic Research

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Generative AI tools can strengthen and streamline every facet of academic research from finding articles and drafting of literature review to formatting of citations.

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Syllabi Policies for AI Generative Tools

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Florida's Instructional Designer Network AI Webinar Series

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Florida's Instructional Designer Network AI Webinar Series

# data[].attributes.attachment_link.readable_text
Professional Development Webinars

Collaborating with our colleagues, partners, friends and supporters across the state, The IDN hosts or co-hosts monthly professional development webinars in Spring and Fall terms of the academic year. The webinars cover various popular topics and are presented by guest speakers who are experts in the field. The webinar recordings and related resources are archived and shared with the community as well as on this website.

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Collaborating with our colleagues, partners, friends and supporters across the state, The IDN hosts or co-hosts monthly professional development webinars in Spring and Fall terms of academic year.

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Open Access Critical Media Literacy and Civic Learning Resource

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Open Access Critical Media Literacy and Civic Learning Resource

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Critical media literacy means critical inquiry. Much of the work of critical media literacy is to ask questions of the media texts that we make use of and study. Critical media literacy focuses on both the content of the media (e.g., what we watch, read, or listen to) and, possibly more important, on the power behind the construction of the content (e.g., the ownership, production, and distribution of media texts).

# data[].attributes.headline
Oregon State University's AI Readiness Playbook

# data[].attributes.subject
Oregon State University's AI Readiness Playbook

# data[].attributes.attachment_link.readable_text
AI readiness playbook

We acknowledge that generative artificial intelligence (AI) has profound implications for teaching and learning across all disciplines, all levels, and all modalities in higher education, and that the process of adapting to this new technology will take time. We believe that adjusting teaching and learning practices in light of this new technology will be hard work, will be a moving target, and will necessitate faculty development and support.

We also believe that OSU students want to learn and that this new technology will not reduce their value of learning. Furthermore, we know that students value the relationships they form with faculty and believe that those relationships can be leveraged to guide students to the appropriate uses of AI technology. Finally, we believe that open and continuing conversations between staff, faculty, and students are an important part of the process of adapting to this new technology.

This guide is intended to help program leaders facilitate conversations with faculty during the year; resources provided here may also help guide policy formation, the selection of pedagogical approaches, and possible curricular adjustments in light of this new technology.

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

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Montgomery College's Library Guide to AI

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Welcome to the MC Library guide on artificial intelligence! The purpose of this guide is to help students understand and explore AI. Browse the tabs above to investigate some common questions about AI, such as:

What concerns about AI should I be aware of?

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Disclaimer: Always check with your professor before using AI for an academic assignment. If you are allowed to use it for coursework, be sure to use proper attribution and citation.

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MC Library Research Guides: Artificial Intelligence: Home

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MC Library Research Guides: Artificial Intelligence: Home

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Casey Wilhelm & Cheri Kittrell's Generative AI Resources and Example Rubrics

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Casey Wilhelm & Cheri Kittrell's Generative AI Resources and Example Rubrics

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The AI Guidance for Schools Toolkit is designed to help education authorities, school leaders and teachers create their own thoughtful guidance to achieve potential benefits of incorporating artificial intelligence in primary and secondary K-12 education while mitigating risks.

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Common Sense Education

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Quick, grab-and-go lessons to help your students think critically about AI and its impact.

Artificial intelligence (AI) is rapidly transforming the world we live in. From using generative AI for schoolwork to dealing with misinformation and befriending AI chatbots, students have a lot to consider when it comes to this ever-evolving technology.

This collection of quick lessons (20 minutes or less!) provide an introduction to AI and help address its social and ethical impacts. Through these lessons, students will:

Understand what AI is and how it works

Consider some of its potential benefits and risks

Think critically about how we can be responsible and ethical users of AI

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Free Resources to Explore and Use ChatGPT and AI

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Help your students understand the impacts and uses of AI.

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AI Literacy Lessons for Grades 6-12 | Common Sense Education

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Quick, grab-and-go lessons to teach your students how to be ethical and empowered users of artificial intelligence.

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A series of instructional web pages for instructors and teaching teams about using AI tools for teaching and learning.

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AI for Good isn't Good Enough with James Landay

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AI for Good isn't Good Enough with James Landay

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AI for Good Isn't Good Enough with James Landay

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COACHes SLO Talk AI and the Changing Landscape of Theories of Assessment, Relevant Assignments and Rubrics

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Danny Pittaway, December 4, 2023

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Friday SLO Talk (December 1, 2023)

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Dive into the intersection of AI and student learning assessment. Explore how faculty wield AI to shape assignments, while students use it to craft responses. Uncover the blurred lines where AI-drafted assignments may be presented as student work, raising questions about true learning outcomes versus enhanced model training.

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Unveiling the Dance: Navigating Learning Taxonomy Literacy in Higher Education Through ChatGPT and Bloom's Revised Taxonomy

Artificial Intelligence chatbots have become the whistleblowers on poor assessment design in higher education and unmasked the unscholarly approach that is at the order of the day. We explore the dance between the learning taxonomy literacy of the subject matter expert compared to that of the AI chatbot. The session will begin with an overview of the truth behind the revised Bloom's taxonomy (RBT), how it ought to be used, followed by a demonstration of how to prompt ChatGPT towards effective assessment design using the dimensions of the RBT. Attendees will have the opportunity to practice using Bloom's revised taxonomy to interpret a learning outcome and related question item in their own context using a provided template.

Mrs Tho-Marie Stoltz is an academic e-learning advisor as well as a lecturer in the Postgraduate Diploma in Taxation and BComHons (Business Management) programme lecturing Research Methodology at Akademia, a private higher education institution in South Africa. She obtained a BCom and BComHons degree in Tourism Management, as well as a Postgraduate Certificate in Higher Education. She recently submitted her master's thesis in Education - Information, Technology, and Communication. She has a passion for interactive learning and optimal use of technology in the learning process. Her educational philosophy is to take her candle (knowledge and passion), and light others' candles too, to create a bright future in Higher Education.

Mrs Elmien van Amerom, is Manager of the Department of Teaching, Learning and Assessment at Akademia, a private higher education institution in South Africa. She obtained a BCom, BComHons and MCom degree in Business Management. She is currently busy completing her PhD in Informatics to be submitted in March 2024, titled "The design and development of scholarly assessment instruments in higher education". She is a certified South African Qualifications Authority Assessor, Moderator and Skills development facilitator. She has 21 years' experience as higher education lecturer and 9 years' experience as a professional staff developer specialising in training lecturers in effective rubric design, how to teach students to ask questions using the Question Formulation technique, as well as using the revised Bloom's Taxonomy in assessment design.

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AI and the Changing Landscape of Theories of Assessment, Relevant Assignments and Rubrics

# data[].attributes.attachment_link.description
Friday SLO Talk (December 1, 2023) Dive into the intersection of AI and student learning assessment. Explore how faculty wield AI to shape assignments, while students use it to craft responses. Uncover the blurred lines where AI-drafted assignments may be presented as student work, raising questions about true learning outcomes versus enhanced model training.

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US Department of Education guide on Artificial Intelligence and the Future of Teaching and Learning

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US Department of Education guide on Artificial Intelligence and the Future of Teaching and Learning

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Additional guides from the US Department of Education

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Additional guides from the US Department of Education

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Develops national educational technology policy and establishes the vision for how technology can be used to transform teaching and learning

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Code.org Artificial Intelligence isn't Magic: It's Just Code!

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Demystify artificial intelligence (AI) by learning how it's changing the ways we live, work, and learn.

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This lesson also gave my students the opportunity to see that there is an element of opinion in classification systems. Later, when we started to see how bias can end up in AI, I was able to pull them back and remind them that the data used to train their models was based on choices.

Seneca Hart * CS Discoveries Teacher

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Tools to help students and teachers bring AI into the classroom.

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How Chatbots and Large Language Models Work

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The Mounting Human and Environmental Costs of Generative AI

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Dr. Sasha Luccioni is a Researcher and Climate Lead at Hugging Face, where she studies the ethical and societal impacts of AI models and datasets. She is also a director of Women in Machine Learning (WiML), a founding member of Climate Change AI (CCAI), and chair of the NeurIPS Code of Ethics committee. The opinions in this piece do not necessarily reflect the views of Ars Technica.

Over the past few months, the field of artificial intelligence has seen rapid growth, with wave after wave of new models like Dall-E and GPT-4 emerging one after another. Every week brings the promise of new and exciting models, products, and tools. It's easy to get swept up in the waves of hype, but these shiny capabilities come at a real cost to society and the planet.

Downsides include the environmental toll of mining rare minerals, the human costs of the labor-intensive process of data annotation, and the escalating financial investment required to train AI models as they incorporate more parameters.

Let's look at the innovations that have fueled recent generations of these models-and raised their associated costs.

Bigger models

In recent years, AI models have been getting bigger, with researchers now measuring their size in the hundreds of billions of parameters. "Parameters" are the internal connections used within the models to learn patterns based on the training data.

For large language models (LLMs) like ChatGPT, we've gone from around 100 million parameters in 2018 to 500 billion in 2023 with Google's PaLM model. The theory behind this growth is that models with more parameters should have better performance, even on tasks they were not initially trained on, although this hypothesis remains unproven.

Bigger models typically take longer to train, which means they also need more GPUs, which cost more money, so only a select few organizations are able to train them. Estimates put the training cost of GPT-3, which has 175 billion parameters, at $4.6 million-out of reach for the majority of companies and organizations. (It's worth noting that the cost of training models is dropping in some cases, such as in the case of LLaMA, the recent model trained by Meta.)

This creates a digital divide in the AI community between those who can train the most cutting-edge LLMs (mostly Big Tech companies and rich institutions in the Global North) and those who can't (nonprofit organizations, startups, and anyone without access to a supercomputer or millions in cloud credits). Building and deploying these behemoths requires a lot of planetary resources: rare metals for manufacturing GPUs, water to cool huge data centers, energy to keep those data centers running 24/7 on a planetary scale... all of these are often overlooked in favor of focusing on the future potential of the resulting models.

Planetary impacts

A study from Carnegie Melon University professor Emma Strubell about the carbon footprint of training LLMs estimated that training a 2019 model called BERT, which has only 213 million parameters, emitted 280 metric tons of carbon emissions, roughly equivalent to the emissions from five cars over their lifetimes. Since then, models have grown and hardware has become more efficient, so where are we now?

In a recent academic article I wrote to study the carbon emissions incurred by training BLOOM, a 176-billion parameter language model, we compared the power consumption and ensuing carbon emissions of several LLMs, all of which came out in the last few years. The goal of the comparison was to get an idea of the scale of emissions of different sizes of LLMs and what impacts them.

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The mounting human and environmental costs of generative AI

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Op-ed: Planetary impacts, escalating financial costs, and labor exploitation all factor.

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IBM SkillsBuild: Power Your Future in Tech with Job Skills, Courses, and Credentials for Free

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IBM SkillsBuild: Power Your Future in Tech with Job Skills, Courses, and Credentials for Free

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Free Skills-Based Learning From Technology Experts | IBM SkillsBuild

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Get free, online, skills-based learning and support no matter where you are in your career journey, developed by technology experts.

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Center for Curriculum Redesign: Education for the Age of AI

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Center for Curriculum Redesign | Making Education MORE Relevant

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Learn more about, “Education for the Age of Artificial Intelligence,” a comprehensive analysis of the evolving landscape of education and AI’s impact on it.

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Artificial Intelligence in Education: A Reading Guide Focused on Promoting Equity and Accountability in AI

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Artificial Intelligence in Education: A Reading Guide Focused on Promoting Equity and Accountability in AI

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by Pati Ruiz

As a former Spanish and computer science teacher, I think a lot about emerging technologies and how they apply in learning contexts. Recently, I have been focused on artificial intelligence (AI) and how it affects students, their families, and communities. I am particularly interested in the consequences (intended and unanticipated) of these emerging AI technologies on students who experience exclusion, specifically Latinx, Black, Indigenous, and students with disabilities.

Working with the Center for Integrative Research on Computing and Learning Sciences (CIRCLS), I have seen work ranging from Intelligent Tutors and systems designed to adapt and personalize learning, including some that are developing pedagogical agents and robots. I've also seen work that seeks to minimize bias and promote equity in AI, projects using computer vision, natural language processing and speech technologies. Of all of these efforts, I consider the work to promote equity and accountability in AI to be the most important and I created this list to focus on those issues. You can also check out this glossary of AI terms written specifically for educators.
First published: March 2020. Last updated: March 2024 A Good Place to Start Power, Privilege, Ethics, and Justice in Artificial Intelligence (AI)

Advancing Racial Equity Through Technology Policy by Julia Rhodes Davis, Eliza McCullough, Sarah Treuhaft, and Rachel Gichinga

Datasheets for Datasets by Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, Kate Crawford (Open Access)

The Steep Cost of Capture by Meredith Whittaker

Responsible Language in Artificial Intelligence & Machine Learning by Genevieve Smith, Ishita Rustagi, Alicia Sheares, and Julia Nee with support from Kellie McElhaney

Algorithmic Bias in Education by Ryan S. Baker and Aaron Hawn

Exploring the Ethics of AI (Website Resource)

Algorithmic Fairness in Education by René F. Kizilcec and Hansol Lee (Open Access)

Auditing Algorithms for Discrimination by Pauline Kim (Open Access)

Facial Recognition Technologies: A Primer by Joy Buolamwini, Vicente Ordóñez, Jamie Morgenstern, and Erik Learned-Miller (Open Access)

Facial Recognition Technologies in the Wild: A Call for a Federal Office by Erik Learned-Miller, Vicente Ordóñez, Jamie Morgenstern, and Joy Buolamwini (Open Access)

The Whiteness of AI by Stephen Cave and Kanta Dihal (Open Access)

AI and Accessibility By World Institute on Disability (Open Access)

The Problems AI has Today Go Back Centuries by Karen Hao (Open Access)

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Hard Choices in Artificial Intelligence by Roel Dobbea, Thomas Krendl Gilbert, and Yonatan Mintz (Open Access)

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Ethics and Computing Repository from EngageCSEdu originally developed by the National Center for Women & Information Technology (NCWIT) and now operated by the Association for Computing Machinery, EngageCSEdu publishes high-quality, engaging, classroom-tested Open Educational Resources (OER's) for computer science education with a focus on materials for introductory courses in computing.

Design and Artificial Intelligence (AI) (Dis)ability and Artificial Intelligence (AI)

No, large language models aren't like disabled people (and it's problematic to argue that they are) by Emily M. Bender (Open Access)

Disability, Bias, and AI from AI Now (Open Access)

Unintended Machine Learning Biases as Social Barriers for Persons with Disabilities by Ben Hutchinson, Vinodkumar Prabhakaran, Emily Denton, Kellie Webster, Yu Zhong, and Stephen Denuyl (Open Access) Note on title: Please see the article (The problems AI has today go back centuries) about using unanticipated when talking about the consequences of AI.
"When people talk about unintended consequences, it sounds like they're saying the consequences couldn't have been predicted. But AI's unintended consequences are in fact highly predictable if you just look back at history." -Marie-Therese Png

Toward Fairness in AI for People with Disabilities: A Research Roadmap by Anhong Guo, Ece Kamar, Jennifer Wortman Vaughan, Hanna Wallach, Meredith Ringel Morris (Open Access)

Artificial Intelligence (AI) Literacy and AI in PreK-12 Education Resources

Towards social generative AI for education: theory, practices and ethics (Open Access) by Mike Sharples

CrashCourse: Artificial Intelligence (Video Series: 21 Videos)

Conceptualizing AI literacy: An exploratory review by Davy Tsz Kit Ng, Jac Ka Lok Leung, Samuel Kai Wah Chu, and Maggie ShenQiao (Open Access)

Critically Conscious Computing: Methods for Secondary Education by Amy J. Ko, Anne Beitlers, Brett Wortzman, Matt Davidson, Alannah Oleson, Mara Kirdani-Ryan, Stefania Druga (Open Access)

Curriculum: AI Plus Ethics for Middle School by Blakely Payne (Open Access Curriculum)

AI4ALL Open Learning a program designed to equip educators and community members to empower high school students with relevant and approachable AI education.

Artificial Intelligence (AI) for K-12 (AI4K12) an initiative that is developing (1) national guidelines for AI education for K-12, (2) an online, curated resource Directory to facilitate AI instruction, and (3) a community of practitioners, researchers, resource and tool developers focused on the AI for K-12 audience.

History of Artificial Intelligence (AI) Guidelines and Frameworks for Artificial Intelligence (AI)

Emerging Technology Adoption Framework: For PK-12 Education by Pati Ruiz, Eleanor Richard, Carly Chillmon, Zohal Shah, Adam Kurth, Andy Fekete, Kip Glazer, Megan Pattenhouse, Judi Fusco, Rita Fennelly-Atkinson, Lin Lin Sheryl Arriola, David Lockett, Valerie Crawford-Meyer, Sana Karim, Sarah Hampton, Belinda Beckford

K-12 Generative Artificial Intelligence (Gen AI) Readiness Checklist by CoSN and Council of the Great City Schools

The Ethical Framework for AI in Education (Open Access)

Universal Guidelines for Artificial Intelligence (Website Resource) by The Public Voice

UNICEF Policy guidance on AI for children. Recommendations for building AI policies and systems that uphold child rights

Policy and Advocacy Groups Critical Race Theory and Background Readings Research Centers EducatorCIRCLS on Artificial Intelligence (AI) Additional High-quality Reading Lists Other Resources
Do you have reading recommendations? Let us know @EducatorCIRCLS.

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Artificial Intelligence in Education: A Reading Guide Focused on Promoting Equity and Accountability in AI

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by Pati Ruiz As a former Spanish and computer science teacher, I think a lot about emerging technologies and how they apply in learning contexts. Recently, I have been focused on artificial intelligence (AI) and how it affects students, their families, and communities.

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Glossary of Artificial Intelligence Terms for Educators

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Glossary of Artificial Intelligence Terms for Educators

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by Pati Ruiz and Judi Fusco

This glossary was written for educators to reference when learning about and using artificial intelligence (AI). We will start with a definition of artificial intelligence and then provide definitions of AI-related terms in alphabetical order. This glossary was last updated on July 19, 2023

Artificial Intelligence (AI): AI is a branch of computer science. AI systems use hardware, algorithms, and data to create "intelligence" to do things like make decisions, discover patterns, and perform some sort of action. AI is a general term and there are more specific terms used in the field of AI. AI systems can be built in different ways, two of the primary ways are: (1) through the use of rules provided by a human (rule-based systems); or (2) with machine learning algorithms. Many newer AI systems use machine learning (see definition of machine learning below).

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Algorithm: Algorithms are the "brains" of an AI system and what determines decisions in other words, algorithms are the rules for what actions the AI system takes. Machine learning algorithms can discover their own rules (see Machine learning for more) or be rule-based where human programmers give the rules.

Artificial General Intelligence (AGI): Artificial general intelligence has not yet been realized and would be when an AI system can learn, understand, and solve any problem that a human can.

Artificial Narrow Intelligence (ANI): AI can solve narrow problems and this is called artificial narrow intelligence. For example, a smartphone can use facial recognition to identify photos of an individual in the Photos app, but that same system cannot identify sounds.

Chat-based generative pre-trained transformer (ChatGPT): A system built with a neural network transformer type of AI model that works well in natural language processing tasks (see definitions for neural networks and Natural Language Processing below). In this case, the model: (1) can generate responses to questions ( Generative); (2) was trained in advance on a large amount of the written material available on the web ( Pre-trained); (3) and can process sentences differently than other types of models ( T ransformer).

Transformer models: Used in ChatGPT (the T stands for Transformer), transformer models are a type of language model. They are neural networks and also classified as deep learning models. They give AI systems the ability to determine and focus on important parts of the input and output using something called a self-attention mechanism to help.

Self-attention mechanism: These mechanisms, also referred to as attention help systems determine the important aspects of input in different ways. There are several types and were inspired by how humans can direct their attention to important features in the world, understand ambiguity, and encode information.

Computer Vision: Computer Vision is a set of computational challenges concerned with teaching computers how to understand visual information, including objects, pictures, scenes, and movement (including video). Computer Vision (often thought of as an AI problem) uses techniques like machine learning to achieve this goal.

Critical AI: Critical AI is an approach to examining AI from a perspective that focuses on reflective assessment and critique as a way of understanding and challenging existing and historical structures within AI. Read more about critical AI.

Data: Data are units of information about people or objects that can be used by AI technologies.

Training Data: This is the data used to train the algorithm or machine learning model. It has been generated by humans in their work or other contexts in their past. While it sounds simple, training data is so important because the wrong data can perpetuate systemic biases. If you are training a system to help with hiring people, and you use data from existing companies, you will be training that system to hire the kind of people who are already there. Algorithms take on the biases that are already inside the data. People often think that machines are "fair and unbiased" but this can be a dangerous perspective. Machines are only as unbiased as the human who creates them and the data that trains them. (Note: we all have biases! Also, our data reflect the biases in the world.) ¹ ​​

Foundation Models: Foundation Models represent a large amount of data that can be used as a foundation for developing other models. For example, generative AI systems use large language foundation models. They can be a way to speed up the development of new systems, but there is controversy about using foundation models since depending on where their data comes from, there are different issues of trustworthiness and bias. Jitendra Malik, Professor of Computer Science at UC Berkeley once said the following about foundation models: " These models are really castles in the air, they have no foundation whatsoever. "

Human-centered Perspective: A human-centered perspective sees AI systems working with humans and helping to augment human skills. People should always play a leading role in education, and AI systems should not replace teachers.

Intelligence Augmentation (IA): Augmenting means making something greater; in some cases, perhaps it means making it possible to do the same task with less effort. Maybe it means letting a human (perhaps teacher) choose to not do all the redundant tasks in a classroom but automate some of them so they can do more things that only a human can do. It may mean other things. There's a fine line between augmenting and replacing and technologies should be designed so that humans can choose what a system does and when it does it.

Intelligent Tutoring Systems (ITS): A computer system or digital learning environment that gives instant and custom feedback to students. An Intelligent Tutoring System may use rule-based AI (rules provided by a human) or use machine learning under the hood. By under the hood we mean the underlying algorithms and code that an ITS is built with. ITSs can support adaptive learning.

Adaptive Learning: Subject or course material is adjusted based on the performance of the learner. The difficulty of material, the pacing, sequence, type of help given, or other features can be adapted based on the learner's prior responses.

Interpretable Machine Learning (IML): Interpretable machine learning, sometimes also called interpretable AI, describes the creation of models that are inherently interpretable in that they provide their own explanations for their decisions. This approach is preferable to that of explainable machine learning (see definition below) for many reasons including the fact that we should understand what is happening from the beginning in our systems, rather than try to "explain" black boxes after the fact.

Black Boxes: We call things we don't understand, "black boxes" because what happens inside the box cannot be seen. Many machine learning algorithms are "black boxes" meaning that we don't have an understanding of how a system is using features of the data when making their decisions (generally, we do know what features are used but not how they are used)There are currently two primary ways to pull back the curtain on the black boxes of AI algorithms: interpretable machine learning (see definition above) and explainable machine learning (see definition below).

Machine Learning (ML): Machine learning is a field of study with a range of approaches to developing algorithms that can be used in AI systems. AI is a more general term. In ML, an algorithm will identify rules and patterns in the data without a human specifying those rules and patterns. These algorithms build a model for decision making as they go through data. (You will sometimes hear the term machine learning model.) Because they discover their own rules in the data they are given, ML systems can perpetuate biases. Algorithms used in machine learning require massive amounts of data to be trained to make decisions.

It's important to note that in machine learning, the algorithm is doing the work to improve and does not have the help of a human programmer. It is also important to note three more things. One, in most cases the algorithm is learning an association (when X occurs, it usually means Y) from training data that is from the past. Two, since the data is historical, it may contain biases and assumptions that we do not want to perpetuate. Three, there are many questions about involving humans in the loop with AI systems; when using ML to solve AI problems, a human may not be able to understand the rules the algorithm is creating and using to make decisions. This could be especially problematic if a human learner was harmed by a decision a machine made and there was no way to appeal the decision.

Neural Networks (NN): Neural networks also called artificial neural networks (ANN) and are a subset of ML algorithms. They were inspired by the interconnections of neurons and synapses in a human brain. In a neural network, after data enter in the first layer, the data go through a hidden layer of nodes where calculations that adjust the strength of connections in the nodes are performed, and then go to an output layer.

Deep Learning: Deep learning models are a subset of neural networks. With multiple hidden layers, deep learning algorithms are potentially able to recognize more subtle and complex patterns. Like neural networks, deep learning algorithms involve interconnected nodes where weights are adjusted, but as mentioned earlier there are more layers and more calculations that can make adjustments to the output to determine each decision. The decisions by deep learning models are often very difficult to interpret as there are so many hidden layers doing different calculations that are not easily translatable into English rules (or another human-readable language).

Natural Language Processing (NLP): Natural Language Processing is a field of Linguistics and Computer Science that also overlaps with AI. NLP uses an understanding of the structure, grammar, and meaning in words to help computers "understand and comprehend" language. NLP requires a large corpus of text (usually half a million words).

NLP technologies help in many situations that include: scanning texts to turn them into editable text (optical character recognition), speech to text, voice-based computer help systems, grammatical correction (like auto-correct or grammarly), summarizing texts, and others.

Robots: Robots are embodied mechanical machines that are capable of doing a physical task for humans. "Bots" are typically software agents that perform tasks in a software application (e.g., in an intelligent tutoring system they may offer help). Bots are sometimes called conversational agents. Both robots and bots can contain AI, including machine learning, but do not have to have it. AI can help robots and bots perform tasks in more adaptive and complex ways.

User Experience Design/User Interface Design (UX/UI): User-experience/user-interface design refers to the overall experience users have with a product. These approaches are not limited to AI work. Product designers implement UX/UI approaches to design and understand the experiences their users have with their technologies.

Explainable Machine Learning (XML) or Explainable AI (XAI): Researchers have developed a set of processes and methods that allow humans to better understand the results and outputs of machine learning algorithms. This helps developers of AI-mediated tools understand how the systems they design work and can help them ensure that they work correctly and are meeting requirements and regulatory standards.

It is important to note that the term "explainable" in the context of explainable machine learning or explainable AI, refers to an understanding of how a model works and not to an explanation of how the model works. In theory, explainable ML/AI means that an ML/AI model will be "explained" after the algorithm makes its decision so that we can understand how the model works. This often entails using another algorithm to help explain what is happening as the "black box." One issue with XML and XAI is that we cannot know for certain whether the explanation we are getting is correct, therefore we cannot technically trust either the explanation or the original model. Instead, researchers recommend the use of interpretable models.

Thank you to Michael Chang, Ph.D., a CIRCLS postdoctoral scholar, for reviewing this post. We appreciate your work in AI and your work to bring educators and researchers together on this topic.

Educator CIRCLS posts are licensed under a Creative Commons Attribution 4.0 International License. If you use content from this site, please cite the post and consider adding: "Used under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/)."
Suggested citation format: [Authors] ([Year]). [Title]. Educator CIRCLS Blog. Retrieved from [URL]

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¹​​​​Fusco, J. (2020). Book Review: You Look Like a Thing and I Love You. CIRCLEducators Blog. Retrieved from https://circleducators.org/review-you-look-like-a-thing/

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Glossary of Artificial Intelligence Terms for Educators

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by Pati Ruiz and Judi Fusco This glossary was written for educators to reference when learning about and using artificial intelligence (AI). We will start with a definition of artificial intelligence and then provide definitions of AI-related terms in alphabetical order.

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AI or Intelligence Augmentation for Education?

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AI or Intelligence Augmentation for Education?

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On December 7, 1968, Douglas Engelbart presented a legendary demonstration that showed how newly emerging computing technologies could help people work together. More generally, Engelbart devoted his professional life to articulating his view of the role of computing in addressing societal problems. He emphasized the potential for technology to augment human intelligence. Since that time, many others have developed the concept of intelligence augmentation (IA).

For example, the field of healthcare sees IA as a more ethical framing. One report defines IA as "...an alternative conceptualization that focuses on AI's assistive role, emphasizing a design approach and implementation that enhances human intelligence rather than replaces it." This report argues "health care AI should be understood as a tool to augment professional clinical judgment."

In education, applications of Artificial Intelligence are now rapidly expanding. Not only are innovators developing intelligent tutoring systems that support learning how to solve tough Algebra problems. AI applications also include automatically grading essays or homework, as well as early warning systems that alert administrators to potential drop-outs. We also see AI products for online science labs that give teachers and students feedback. Other products listen to classroom discussions and highlight features of classroom talk that a teacher might seek to improve or observe the quality of teaching in videos of preschool children. A recent expert report about AI and education all uncovered visions for AI that would support teachers to orchestrate classroom activities, extend the range of student learning outcomes that can be measured, support learners with disabilities, and more.

In colloquial use, the term AI calls forth images of quasi-human agents that act independently, often replacing the work of humans, who become less important. AI is usually faster and based on more data, but is it smarter? In addition, there are difficult problems of privacy and security-society has an obligation to protect children's data. And there are even more difficult issues of bias, fairness, transparency, and accountability. Here's our worry: a focus on AI provides the illusion that we could obtain the good (super-human alternative intelligences) if only we find ways to tackle the bad (ethics and equity). We believe this is a mirage. People will always be intrinsic to learning, no matter how fast, smart, and data-savvy technological agents become. People are why agents exist. We think it's important to always have the human in the loop to understand if things are working and if not, to understand why and make creative plans for change.

Today, students and teachers are overwhelmed by the challenges of teaching and learning in a pandemic. The problems we face in education are whole child problems. Why are parents clamoring to send children back to school? It's not just so they can get some work done! Learning is fundamentally social and cultural; enabling the next generation to construct knowledge, skills, and practices they'll need to thrive is work that requires people working together in a learning community. Schools also provide needed social and emotional support. We are simultaneously at a critical juncture where the need to address ethics and equity are profound. In addition to trust and safety considerations, prioritizing the impact, and understanding how it changes interactions and what those implications are for students and teachers is essential when evaluating AI or any technology.

Thus, we recommend a focus on IA in education that would put educators' professional judgement and learners' voice at the center of innovative designs and features. An IA system might save an educator administrative time (for example, in grading papers) and support their attention to their students' struggles and needs. An IA system might help educators notice when a student is participating less and suggest strategies for engagement, perhaps even based on what worked to engage the student in a related classroom situation. In this Zoom era, we've also seen promising speech recognition technologies that can detect inequities in which students have voice in classroom discussions over large samples of online verbal discourse. In some forward-looking school districts, teachers have instructional coaches. In those situations, the coach and teacher could utilize an IA tool to examine patterns of speaking in their teaching and make plans to address inequities. Further, the IA tool might allow the coach and teacher to specify smart alerts to the teacher-for example, for expected patterns in future classroom discussions that would signal a good time to try a new and different instructional move. Later, the IA tool might make a "highlights reel" that the coach and teacher could review to decide whether to stay with that new instructional move, or to try another.

The important difference between AI and IA may be when an educator's professional judgement and student voice are in the loop. The AI perspective typically offers opportunities for human judgement before technologies are adopted or when they are evaluated; the IA perspective places human judgement at the forefront throughout teaching and learning and should change the way technologies are designed. We worry that the AI perspective may encourage innovators to see ethics and equity as a barrier they have to jump over once, and then their product is able to make decisions for students autonomously. Alas, when things go wrong, educators may respond with backlash that takes out both the bad and the good. We see the IA perspective as acknowledging ethics and equity issues in teaching and learning as ongoing and challenging.

By beginning with the presumption that human judgement will always need to be in the loop, we hope that IA for education will focus attention on how human and computational intelligence could come together for the benefit of learners. With IA, restraint is built into the design and technology isn't given power to fully make decisions without a diverse pool of humans participating. We hope IA for education will ground ethics and equity not in a high stakes disclosure/consent/adoption decision but rather in cycles of continuous improvement where the new powers of computational intelligence are balanced by the wisdom of educators and students.

Jeremy Roschelle is Executive Director of Learning Sciences Research at Digital Promise and a Fellow of the International Society of the Learning Sciences. Pati Ruiz (pruiz@digitalpromise.org) is a Computer Science Education Researcher at the non-profit Digital Promise. Judi Fusco (jfusco@digitalpromise.org) is a Senior Researcher focusing on STEM teaching and learning at Digital Promise.

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AI or Intelligence Augmentation for Education?

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On December 7, 1968, Douglas Engelbart presented a legendary demonstration that showed how newly emerging computing technologies could help people work together. More generally, Engelbart devoted his professional life to articulating his view of the role of computing in addressing societal problems. He emphasized the potential for technology to augment human intelligence.

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Speak Up Snapshot on GenAI in K-12 Education

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Speak Up Snapshot on GenAI in K-12 Education

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Speak Up Snapshot on GenAI in K-12 Education

GenAI offers both opportunities and challenges. It promises to foster immersive and interactive learning experiences. At the same time there are valid concerns regarding the ethics of incorporating GenAI into schools. To learn more about the pressing needs related to GenAI in school districts, Project Tomorrow partnered with Advanced Learning Partnerships (ALP) to survey educators and administrators. Upon completion of the study, we will distribute a report on the national findings.

Select states have a unique URL, designed to simplify the process of participating as a district. If you are an educator or administrator in these states, please use the link provided below:
About the GenAI in K-12 Education Speak Up Snapshot:

Design: Can be taken by individuals or as a district through May 31, 2024.

Audience: All district and school administrators, instructional coaches, specialists, and teachers are invited to participate.

Confidentiality: All responses are confidential and collected anonymously.

Time: It takes less than 14 minutes to complete.

Format: Primarily multiple-choice questions with a few open-ended questions for narrative response.

If you choose to participate as a district:

We will send you a communications toolkit with sample messaging for sharing this opportunity with your stakeholders.

Participating school districts will receive the collected data from their stakeholders for local planning.

Districts can set their own survey window and Project Tomorrow will share your local results when you have completed distribution.

As appropriate, leverage the data findings being collected via the current Speak Up surveys with grades 6-12 students.

You will gain a more complete understanding of GenAI and the views, attitudes, and concerns of your staff.

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GenAI offers both opportunities and challenges. It promises to foster immersive and interactive learning experiences. At the same time there are valid concerns regarding the ethics of incorporating GenAI into schools. To learn more about the pressing needs related to GenAI in school districts, Project Tomorrow partnered with Advanced Learning Partnerships (ALP) to survey educators and administrators.

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Revealing an AI Literacy Framework for Learners and Educators

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Revealing an AI Literacy Framework for Learners and Educators

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What is AI Literacy?

AI literacy builds on years of work in digital readiness, media literacy, and computational thinking. Just like these more familiar domains, AI literacy applies common 21st century skills such as communication, collaboration, critical thinking, and creativity. Building on definitions of AI and generative AI from the U.S. government, Digital Promise defines AI literacy as follows:
AI literacy includes the knowledge and skills that enable humans to critically understand, use, and evaluate AI systems and tools to safely and ethically participate in an increasingly digital world.
Our AI Literacy framework, pictured below, includes three components: Understand, Use, and Evaluate.

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Revealing an AI Literacy Framework for Learners and Educators - Digital Promise

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AI Literacy framework for educators to apply in K-12 contexts.

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The introduction of generative artificial intelligence (AI) tools (such as ChatGPT, DALL-E and Bard) are changing the ways we work, communicate, and learn. It is both an exciting and challenging time, with thrilling possibilities that may also carry serious risks. We at Ecampus acknowledge that there remains a great deal of uncertainty in how to approach the use of these new tools for building online courses, creating new online pedagogies, and communicating with students in higher education.

And as these technologies rapidly improve, we recognize that we will need to adjust our approaches more quickly than we might find comfortable. However, if we can center our approach to the use of AI tools with Ecampus' core values, then we can engage with these technologies in ways that may maximize benefits for faculty and students, while minimizing possible risks.

What follows is not a prescriptive tool, but rather a set of guidelines and best practices for the ethical and responsible adoption of AI tools into online courses. These guidelines are grounded in our core values here at Ecampus. In the spirit of creating high-quality learning experiences, we encourage the prioritization of the following principles based on the unique circumstances associated with a particular use case: 1) fostering student-centered approaches to learning; 2) demonstrating transparency; 3) promoting quality and practicing integrity; 4) safeguarding privacy and security; 5) avoiding potential bias and other social harms; and 6) ensuring equitable access.

The guidelines are intended to inform our work and the work of faculty. We hope they will help inform the necessary discussion, collaboration, and decision-making that enables the responsible and ethical use of AI tools in online courses.

Establish accountability - Regardless of how or whether AI is used, emphasize that the human author is accountable for all content produced.

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Oregon State Ecampus offers 110+ accredited online degrees, undergraduate and graduate, including business, computer science, natural resources and fisheries.

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Purdue Global Exploring AI Core Concepts and Applications

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Purdue Global Exploring AI Core Concepts and Applications

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Synopsis

Explore the emerging field of Artificial Intelligence (AI) including the history, landscape, ethics and applications within the professional field.

In this micro-credential, you will explore the landscape of artificial intelligence (AI), generative AI, ethical considerations about the use of AI, and the use of AI applications in various professions..
Course at a Glance
This course is divided into the following modules:

Module One: AI Technology Landscape

Module Two: Generative AI

Module Three: AI Ethics

Module Four: AI Applications
Price:
This course is free.
Earning College Credit for this Course
College credit is not currently available for taking this course.

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Exploring AI: Core Concepts and Applications

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Explore the emerging field of Artificial Intelligence (AI) including the history, landscape, ethics and applications within the professional field. In this micro-credential, you will explore the landscape of artificial intelligence (AI), generative AI, ethical considerations about the use of AI, and the use of AI applications in various professions..

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Generative AI aggregates human knowledge and makes it accessible in unprecedented ways. If used carefully and strategically, it could be a powerful tool, allowing us to be more efficient, more creative, and help us reach new audiences with our work. If used carelessly, though, it could degrade the quality of our work, disempower our staff, and inadvertently plagiarize others. In order to gain the benefits of generative AI while avoiding its pitfalls, we have created this Generative AI Usage Policy to provide guidance to our staff, external vendors, and the public about how our organization will and will not use generative AI systems.

Generally speaking, we are open to and interested in our staff experimenting with generative AI. We want to do our work as well as possible, and generative AI may help us do that. In addition, experimentation can improve our understanding of the technology and thereby strengthen our advocacy about AI. However, we also want to safeguard against the temptation to use generative AI tools to produce low-effort, low-quality work, and ensure that we use this technology in a way that is consistent with our values as an organization. Furthermore, we remain dedicated to being a human-first organization. We want to ensure that we use generative AI to support our staff doing their jobs, not replace them; accordingly, we will never require staff to use generative AI to complete their work.

In this policy, we delineate between original works, in which we express new content and ideas, and derivative works, in which we summarize our own original works for different contexts or audiences. With original works, we more strictly limit ourselves in our use of generative AI, since the risks posed by inadvertent plagiarism, factual misstatements, and bland prose are higher. With derivative works, we are more open to teams using unaltered or minimally altered (though not unreviewed) outputs from generative AI systems, so long as the input primarily comes from original CDT work. This delineation reflects what we see as limitations in current technology. As generative AI evolves, we will continue to update this policy accordingly to reflect its capabilities and potential use cases.

In producing original works (e.g. reports, amicus briefs, public statements), we may potentially use generative AI as:

A brainstorming tool, to find inspiration, generate starter ideas, or overcome fear of the blank page.

A research tool, to find new sources and summarize information. The onus remains on the author to ensure the accuracy of these outputs.

An editor, to improve or condense the phrasing of drafted text.

A super-charged thesaurus, to figure out a word or sentence to phrase an idea or concept.

A rote worker, to complete routine tasks such as formatting citations or adhering to style guidelines.

A tool to create information visualizations. Any AI-generated visualization will always be explicitly credited as such.

In producing derivative works (e.g. social media posts, newsletters, grant reports), we may also potentially use generative AI to:

Create short descriptions or summaries of our original work for outputs, such as campaign summaries and solicitation materials.

Suggest report titles, article headlines, or text for social media content describing our work.

Create slideshows and other presentation materials for our work.

CDT staff may also explore using generative AI for CDT's internal processes, such as meeting notes summarization or internal document organization.

Any uses of generative AI to produce external-facing CDT outputs beyond those mentioned above will be considered on a case-by-case basis. At a minimum, such uses must be discussed with and approved in advance by a manager, and outputs should be appropriately labeled as AI-generated. This includes AI-generated illustrations, which should not be used to substitute for the work of a human illustrator except in exceptional cases.

Externally or internally, we will adhere to certain universal practices in all of our uses of generative AI:

CDT staff are fully responsible for the quality and accuracy of their work product. We expect staff to thoroughly review all AI-generated content they use in any outputs, including verifying factual statements and citations.

Contractors that work with CDT must comply with this policy. In addition, we will ask contractors to document and disclose to CDT staff how they used generative AI in their work so we can ensure their compliance with our policy.

We will not input private, sensitive, or confidential information into any generative AI system absent specific guarantees regarding the system's privacy and data security practices that have been reviewed and approved by management. This includes any data collected through human subjects research (surveys, interviews, etc.) or information provided under NDA. As a rule of thumb, any information that would violate confidentiality obligations or cause embarrassment or reputational harm to CDT if it were publicly released should not be entered into a generative AI system.

This Generative AI Usage Policy v1.0 was published on February 21, 2024. If this policy is updated, you will be able to find a link(s) to previous versions on this page. A permanent link to this version of the policy is available: https://perma.cc/BEQ6-8RL8.

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Generative AI aggregates human knowledge and makes it accessible in unprecedented ways. If used carefully and strategically, it could be a powerful tool, allowing us to be more efficient, more creative, and help us reach new audiences with our work. If used carelessly, though, it could degrade the quality of our work, disempower our staff, [...]

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AI-Generated Virtual Instructors Based on Liked or Admired People Can Improve Motivation and Foster Positive Emotions for Learning

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AI-Generated Virtual Instructors Based on Liked or Admired People Can Improve Motivation and Foster Positive Emotions for Learning

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Project Overview ‹ AI-Generated Virtual Instructors Based on Liked or Admired People Can Improve Motivation and Foster Positive Emotions for Learning – MIT Media Lab

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This research presents the results of a study with 134 participants to explore the effects of learning from an AI-generated virtual instructor that resemb…

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Navigating the Conundrum: Academic Honesty in the Era of Generative AI

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Navigating the Conundrum: Academic Honesty in the Era of Generative AI

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Navigating the Conundrum: Academic Honesty in the Era of Generative AI

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The rise of generative artificial intelligence (AI) is reshaping the educational landscape, challenging traditional notions of academic honesty. As these advanced technologies begin integration in our daily lives, it is imperative for higher education institutions to reassess their stance on academi

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Teacher Devises Ingenious Method to Catch Students that Cheat with AI

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Teacher Devises Ingenious Method to Catch Students that Cheat with AI

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How To Catch Students That Cheat With AI | Digg

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Checking to see if the text is AI-written or not is an uphill battle, so a teacher decided to slip in a trojan horse - and that made life so much easier.

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Promoting Authenticity of Student Work in the Age of Artificial Intelligence: A Faculty Guide

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Promoting Authenticity of Student Work in the Age of Artificial Intelligence: A Faculty Guide

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U of MI Flint Generative AI Prompt Literacy free self-paces

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U of MI Flint Generative AI Prompt Literacy free self-paces

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Generative AI Prompt Literacy is a Course

Generative AI Prompt Literacy

Self-paced

Enroll

Full course description

In our AI-powered future, mastering the art of AI prompting is not just useful - it's essential. Like learning to search Google efficiently two decades ago, effective AI prompting is the key to unlocking high-quality, productive outcomes from AI tools. Our online course, Generative AI Prompt Literacy, aims to equip you with this essential skill set, putting you in command of the AI revolution.

This course isn't just about the 'how' of AI prompts. It explores the 'why,' highlighting the impact of well-crafted prompts on the quality and usefulness of AI output. Learn to construct prompts that make AI a practical, potent tool to accomplish your objectives, from drafting detailed reports to generating content to help you accelerate your learning.

In the spirit of equal opportunity, we firmly believe in equitable access to this transformative knowledge. AI is already reshaping how we live and work, and no one should be left behind in this rapidly changing landscape. That's why we've designed this course to be accessible, engaging, and enlightening for learners at all levels, so everyone has the opportunity to build proficiency in this critical area.

Through five comprehensive modules and self-guided assessments, we'll guide you on a journey of AI skill building, ensuring everyone has the opportunity to succeed.

Key Terms and Natural Language Processing

Prompting for Educational Purposes

Diverse and Inclusive Prompt Development

Best Practices

Prompt Examples

Check out our introduction video to get a sneak peek into the course experience. Harness the power of AI with us and ensure your place in our technology-driven future. Let's make AI prompt literacy universal - together!

Sign up for this course today!

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Generative AI Prompt Literacy - Office of Online and Digital Education

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Generative AI Prompt Literacy is a Course Self-paced Enroll In our AI-powered future, mastering the art of AI prompting is not just useful - it's essential. Like learning to search Google efficiently two decades ago, effective AI prompting is the key to unlocking high-quality, productive outcomes from AI tools.

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EU AI Act: First Regulation on Artificial Intelligence

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EU AI Act: First Regulation on Artificial Intelligence

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EU AI Act: first regulation on artificial intelligence | Topics | European Parliament

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The use of artificial intelligence in the EU will be regulated by the AI Act, the world’s first comprehensive AI law. Find out how it will protect you.

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D2L Open Courses Introduction to AI Ethics and Governance

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D2L Open Courses Introduction to AI Ethics and Governance

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Introduction to AI Ethics and Governance

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Artificial Intelligence

Generative AI is artificial intelligence that can create new content such as text, visual images, code, audio, or video because its neural networks have been trained on a gigantic amount of data. Outputs might include digital art, essays, short answers, blog posts, computer code, press releases, and other types of novel content. One form of generative AI, large language models, has especially received attention with the release of ChatGPT. Using the chat interface, users can make a seemingly endless array of queries of GPT 3.5 that result in often stunning responses. The relative ease in getting the program to create everything from poems to short answers to complete essays has led many faculty and administrators to worry about the academic integrity of coursework. Others are urging faculty to rethink assessment practices and find ways to integrate generative artificial intelligence, especially ChatGPT, into coursework.

WCET created a member-only AI toolkit in December 2023. If you are a current member, contact us to receive access. If you are not a member, join us.

WCET has recently launched a new community on Generative AI. The community is currently open to the public, but may become member-only in the future.

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Artificial Intelligence Generative AI is artificial intelligence that can create new content such as text, visual images, code, audio, or video because its neural networks have been trained on a gigantic amount of data. Outputs might include digital art, essays, [...]

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GenAI in Computer Science Education: Friend or Foe?

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GenAI in Computer Science Education: Friend or Foe?

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Dr. Andrew DeOrio's recent trial of a custom GenAI chatbot in one of his courses revealed that AI can be a helpful tool, instead of just a threat, in the classroom.

Ask ChatGPT to generate a line of code for you, and it will, often with uncanny accuracy. Generative AI's ability to create code and other content with surprising ease has given rise to widespread concerns about the danger of this technology in the classroom. Little is stopping students from relying on these platforms to complete assignments for them, rather than actually engaging with and learning the material. For many educators, in computer science and beyond, this has meant instituting a complete ban on GenAI for help with homework and projects, although methods to actually detect or regulate its use are often unreliable.

One instructor at the University of Michigan, teaching faculty and Associate Chair for Undergraduate Affairs Andrew DeOrio, is taking a different approach. Rather than fighting the AI revolution, he has decided to embrace it.

"A lot of the rhetoric right now is how to prevent students from using GenAI," said DeOrio, "but what would happen if, instead, we told them to use it?"

To explore GenAI's potential as an educational aid, DeOrio and a team of teaching assistants in his EECS 485 Web Systems course, including Yutong Ai, Maya Baveja, Akanksha Girdhar, and Melina O'Dell, designed a chatbot using U-M's Maizey AI toolkit. Rather than replacing traditional course materials or completing assignments for them, the goal was to train and deploy a chatbot that could augment existing resources and serve as a genuinely useful tool for students.

The team created a custom chatbot based on OpenAI's GPT-4 architecture to help students as they worked on a project in small groups. For the assignment, students were tasked with designing a system using sockets, processes, and threads to run MapReduce parallel programs.

As DeOrio put it, "Say you are running a large website and need to crunch statistics on who is visiting your website. You might have too many log files for a standard computer program, so you would need a parallel program to read all of them. A MapReduce program can help you do that."

DeOrio selected this type of assignment for the chatbot trial in part because of its complexity and open-endedness. He and his students wanted to create a tool that would not just generate answers, but could serve as a resource by helping to define concepts or explain project steps and goals. Building it around an advanced, multifaceted project like this one was part of that goal.

"While ChatGPT might excel at generating a line of basic code, when it comes to designing a complex framework such as this project entails, it falls short," he said. "We wanted to design something that wouldn't just give students easy answers but would instead facilitate their learning."

DeOrio and his students trained the bot using course materials related to the project, including lecture PDFs and transcripts, tutorials, the project specification, and Piazza forum posts related to the project from previous semesters. He then deployed the bot to the more than 700 students in EECS 485 and asked them to use it at least three times as they worked on the project.

The results of this trial were encouraging, to say the least. In a survey completed by almost 300 student groups after completing the project, respondents reported that the majority (77%) of their interactions with the GenAI chatbot were helpful.

The team also found that students made use of the chatbot more than expected. While they were required to use the bot at least three times, the average number of interactions was 4-10 per group, with some using it a dozen times or more.

This rate of use on a single project indicates that the chatbot provided students with useful information as they worked on the assignment. In particular, students reported that the bot performed best when answering questions related to concepts and project specifications, while being less reliable in explaining test cases relevant to the project.

The survey responses reflect this:

"I found it very helpful, it was succinct and answered my question exactly as I had hoped," said one student, after asking the bot to provide a conceptual explanation.

According to another student, the chatbot was "very helpful at providing background context for concepts in code we weren't familiar with [and] good for syntax errors or odd/specific configurations that we weren't used to." This same student noted that the bot was "not a replacement for coding," but that "it was overall incredibly useful, especially for debugging."

Another respondent reported, "It gave us helpful clarification on the spec and helped with debugging. Having a similar bot for other projects would be helpful."

A big concern of DeOrio and his student team was accuracy, particularly considering GenAI's troubling tendency to "hallucinate," that is, to generate false or misleading information and present it as fact. This is especially concerning in an educational setting, where misinformation could interfere with student learning.

Fortunately, the bot that DeOrio and his students designed was surprisingly accurate. Upon instructor review, the team found that 92% of the chatbot's responses were correct. And, interestingly, of the 8% incorrect answers provided by the chatbot, students were able to identify them as incorrect over 96% of the time.

"While the goal is to build as robust and accurate a tool as possible," said DeOrio, "even in this early iteration, only a very small percentage of chatbot mistakes were able to slip through the cracks."

Also promising is the custom chatbot's efficacy compared to a general purpose bot, in this case ChatGPT. DeOrio and his team found that their chatbot outperformed ChatGPT on several fronts, providing more detailed and relevant responses that directly related to the project specifications, whereas ChatGPT's answers were more generic.

While survey results clearly demonstrated the utility of the chatbot, most students reported that they still preferred interacting with instructors directly via office hours or on the course forum on Piazza.

To DeOrio, however, this is actually an encouraging sign. "It shows that students aren't using the bot to replace traditional learning avenues, but to augment the resources they already have," he said.

With undergraduate enrollment and class sizes booming and teaching capacity stretched thin across the computer science discipline, any resource that can further enhance learning and support students is a welcome addition.

For DeOrio, the positive results of his group's chatbot trial give him hope for the future of GenAI in computer science education. "GenAI is another way for students to get feedback on their work that is quick and low stakes," he said. "Based on the feedback we've received in this early iteration, I think this kind of tool has great potential."

Going forward, DeOrio plans to continue to explore and refine GenAI tools to support student learning, starting with an expansion of the current version of his course chatbot. "We hope to expand the scope of our bot to cover the whole class to see if it continues to be useful," he said.

Another challenge to address going forward is whether a similar tool could be designed for lower-level classes, where GenAI could provide easy answers for more straightforward coding questions. "We want to avoid the pitfall of students asking the bot to just do their homework for them," said DeOrio.

By demonstrating AI as a useful educational aid rather than a shortcut to answers, DeOrio's experiment offers a glimpse into a future where AI can be successfully integrated into the classroom. As educators and institutions grapple with the challenges and opportunities presented by GenAI, the success of this experiment serves as a testament to its potential benefits, when harnessed correctly, in supporting future generations of computer scientists.

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GenAI in computer science education: Friend or foe?

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Dr. Andrew DeOrio's recent trial of a custom GenAI chatbot in one of his courses revealed that AI can be a helpful tool, instead of just a threat, in the classroom.

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The ChatGPT Effect and Transforming Nursing Education

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The ChatGPT Effect and Transforming Nursing Education

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The Impact of AI Assistance on Student Agency

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The Impact of AI Assistance on Student Agency

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Digital Trust Guidelines for Generative Artificial Intelligence Use

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Digital Trust Guidelines for Generative Artificial Intelligence Use

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Digital Trust Guidelines for Generative Artificial Intelligence Use

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Getting started with prompts for text-based Generative AI tools

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Getting started with prompts for text-based Generative AI tools

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Generative AI

It's very important to keep in mind that AI-generated content can be inaccurate, misleading, entirely fabricated, or offensive, so be sure to carefully review any work containing AI content before you use or publish it.

You don't have to get everything into your first prompt. Try starting with a basic question and adding to it over time. Change the wording or tone or add more context and specificity to guide the AI toward the output you're looking for.

Chat with the AI as if it's a colleague or teammate and you're working on a project together. Give feedback - tell it which parts of the output were useful and which parts could be improved. If you notice it got something wrong, tell it so it can correct its mistake.

Please note HUIT is not responsible for content on third-party websites:

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Getting started with prompts for text-based Generative AI tools

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The information, sentences, or questions that you enter into a Generative AI tool ("prompts") are a big influence on the quality of outputs you receive. After you enter a prompt, the AI model analyzes your input and generates a response based on the patterns it has learned through its training.

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From Turnitin's website.

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Getting started with AI writing at Turnitin - for Administrators

This guide for administrators introduces resources to support instructors' use of AI writing detection capabilities.

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Getting started with AI writing at Turnitin - for Administrators This guide for administrators introduces resources to support instructors' use of AI writing detection capabilities.

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Curating the latest AI advancements in education into digestible insights, this blog empowers educators and administrators by demystifying AI and inspiring innovative teaching and learning strategies for the AI era.

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AI x Education | Johnny Chang | Substack

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Curating the latest AI advancements in education into digestible insights, this blog empowers educators and administrators by demystifying AI and inspiring innovative teaching and learning strategies for the AI era. Click to read AI x Education, a Substack publication with thousands of subscribers.

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ChatGPT and Artificial Intelligence in Higher Education

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ChatGPT and Artificial Intelligence in Higher Education

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NoodleTools Citing ChatGPT, Bard and Other AI Tools

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NoodleTools Citing ChatGPT, Bard and Other AI Tools

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Hope you've all had a great summer and are looking forward to a productive 2023-24 school year! Over the past several months, there are two

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Generative AI and K-12 Education: An MIT Perspective

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Generative AI and K-12 Education: An MIT Perspective

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In November of 2022, a Silicon Valley company launched an invention that could complete students' homework for them. Available only to subscribers at first, by the spring of 2023 OpenAI's ChatGPT-3.5 was available to millions of students. As of January 2023, anyone with internet service can access the next generation, GPT-4, using Microsoft's Bing, for free. ChatGPT and other emerging models like it are a form of generative AI, and its widespread availability poses new challenges and opportunities for schools. The response from educators falls along a spectrum, of enthusiasm and optimism on one side, and fatigue, bitterness, and pessimism on the other, and commonly, a mix of positive and negative attitudes. The optimists, the pessimists, and the ambivalent all agree that the sudden, widespread availability of generative AI has been a jolt that has left them scrambling to adapt for the past year.

-Steve Ouellette, Director of Technology, Westwood Public Schools

AI hasn't provided ANY benefits to me as an English teacher. And it hasn't forced me to change much. It's just another thing I have to deal with. One of too many.

-"Mike," High School English Teacher, California

Generative artificial intelligence takes in prompts in many forms, often natural human language, and creates new content such as images, texts, or audio. This "new" content is generated from statistical patterns found in vast datasets, resulting in images, text, and media that replicate features found in the dataset. While generative AI is not new, powerful applications like GPT-4 have in the past year become widely accessible to users without computer programming knowledge. A handful of educators have been thinking about AI's impact on education for years. The advent of widely available, super-powerful generative AI and its capacity to help students complete assignments has forced many more educators to pay close attention to it, whether they want to or not.

In education, AI optimists envision a future in which students develop their creativity and higher-order thinking, supported by AI, with a deliberate balance of new and traditional classroom practices, freeing teachers to focus on personal interactions. AI pessimists see a future in which many students use AI to bypass cognition or to cheat, while teachers waste time and energy on cat and mouse games or digital surveillance. Pessimists also imagine a scenario in which generative AI is over-adopted, particularly in resource-constrained schools, reducing the teacher workforce, and widening existing divides.

Generative AI is an "arrival" technology. Unlike laptop computers, its presence in schools is not the result of a policy of adoption. Like smartphones, students are using generative AI on school assignments regardless of whether the schools encourage or forbid it. In many respects, the stakes of "arrival" technologies in schools are much higher than the stakes of technologies brought in by school leadership. If schools did not address tablets around 2013, they may have missed opportunities, but they were not harmed. Arrival technologies, on the other hand, can harm school environments if not effectively managed. And more affluent students have an advantage since they are more likely to have higher-quality access to AI, as well as have access to additional staff managing the application of the technologies.

As a class of technologies, generative AI has a "jagged technological frontier": AI can be shockingly good and then comically bad at very similar tasks. It will take time to determine how AI can be useful in the scholarly disciplines, in the working world, and in the civic sphere. While generative AI may become a tool educators can deploy toward better outcomes, adapting to it will be challenging. Without the needed time and resources, educators will struggle to adapt, and the pessimistic predictions will be more likely to come true.

Here at MIT, we exist in a technology-rich environment that prizes testing and exploration. Our institution has expertise in AI and is actively shaping its future. To that expertise, we also add our extensive experience working with educators and students. We are aware that this is a particularly challenging time for schools, emerging from a devastating and contentious pandemic, with historic levels of burnout and turnover in school personnel.

As we consider generative AI in schools, we are guided by constructionism, the idea that people learn best through experiences and through making and sharing things. Constructionism prizes making connections to prior knowledge and building understanding through coaching rather than lecturing or drilling skills. It also values the sharing of knowledge and artifacts between peers.

In this article, we will provide a glimpse of what's happening in schools right now, contextualize experimentation with generative AI, explore key concerns around the implementation in schools, and provide a roadmap for adapting to generative AI.

We recommend that schools pursue considered, limited experimentation without making undue pedagogical or financial commitments, that schools facilitate access to AI with thoughtful guardrails, that educators consider what productive thought students should engage in, and that industry, researchers, and policymakers work together to support educators and students as they adapt to this disruptive technology.

The path will become more evident as generative AI develops. In the meantime, schools should build familiarity with generative AI, explore pedagogical possibilities, and address immediate challenges around assessment and academic integrity.

Educators are well-acquainted with technology hype cycles. In the past twenty years, they have weathered the rise of smartboards, 1:1 (one device per student), "freemium" apps, and virtual reality. While individual teachers have done amazing things with recent tech, none of these technologies has lived up to the wide-scale "disruptive" promises made by their proponents, and educators know that simply implementing technology does not guarantee better learning outcomes.

At this moment, educators are unprecedentedly beleaguered. The COVID-19 pandemic, the conflicts over opening school buildings, the challenges of teaching remotely, and the conflict over "divisive speech laws" have combined to lead to historic levels of burnout and turnover. Teachers who might otherwise energetically experiment with generative AI technology are holding on by their fingernails, with little energy for experimentation. For many, generative AI affords another unwelcome chore next to too many parents' emails to answer, too many papers to grade, and too many memos to read.

We need time. Teachers are going to need time to experiment with AI and learn how they can leverage it in their practice. We all need time to talk about how we can get the most out of it.

-Greg Schwanbeck, Physics and Astronomy Teacher, Massachusetts.

We talked with teachers and administrators to learn how schools are responding now. Each school is unique; each is grappling with its own set of challenges and opportunities in terms of resources, student population, regulatory environment, and more. Schools' approaches range from inaction, outright banning, informal and sporadic experimentation, and sanctioned experimentation. One example of a thoughtful approach to generative AI comes from our neighbors in Westwood, Massachusetts.

In December of 2022, Steve Ouellette, the director of technology at Westwood, read about ChatGPT on an email list. The post included a video showing the chatbot writing an essay about the play A Raisin in the Sun. Ouellette says, "The sub caption was 'Buckle up, here it comes.'"

Westwood is a well-resourced school district with a track record of experimenting with technology. Ouellette urged the superintendent to send a memo that weekend, notifying the faculty about a tool that could potentially complete students' assignments, allowing them to bypass productive thought, or, in other words, to "cheat." Ouellette enjoys experimenting, so he asked ChatGPT to draft the memo. Its first draft was dry and officious, so he asked it to rewrite with a joke about France winning the World Cup final, "and the way it did it was magnificent."

ChatGPT could be used to create personalized lesson plans or homework assignments, provide instant feedback on student writing, or even help generate entire lesson plans for teachers. But that's not all. ChatGPT could also be used to help students learn other languages, such as Spanish or French (which, by the way, I think will win the 2022 World Cup). Imagine being able to have a conversation with ChatGPT in French and receiving instant corrections and feedback on your pronunciation and grammar.

-Excerpt from Westwood Public Schools Superintendent Emily Parks's Memo.

Immediately, teachers, particularly English teachers, expressed concerns about academic integrity issues, and they rely heavily on writing assignments and saw the introduction of ChatGPT as a potentially devastating development. Ouellette says, "I've referred to the English department as 'ground zero'. It's unfortunate, because their first experience with it was just intensely negative. I don't think that they've recovered from that."

Ouellette invited an expert on generative AI to present at a meeting with department heads. Then in the summer of 2023, the district convened an AI working group to create recommendations for moving forward. Students were not included in the working group, but Ouellette did speak with students, many of whom expressed concerns about how AI might affect the college application process. Training opportunities were added for the 2023-2024 school year. The working group also created a resource for staff called an "AI Idea Bank," which offered suggestions of how teachers could use ChatGPT.

Westwood blocked the ChatGPT service through grade 7 because its terms of service do not allow children younger than 13 to use it. (Children 13-18 require parental consent.) The service is open for grades 8 and beyond. In November 2023, Westwood hosted a panel that allowed students and community members to voice concerns, and included insight from AI experts.

We think Westwood's example is a useful model for open communication, participation from different stakeholders, and an informed approach to adapting to generative AI. According to our scan of teachers, Westwood has done more to help teachers and students adapt to AI technology than most districts. But Ouellette says the district has been scrambling to adapt, and that he does not think they have done enough: "We've done a superficial effort at professional development, because we just haven't had a lot of time."

According to a Forbes Advisor survey, 60 percent of surveyed teachers have experimented with integrating the technology. Here are a few approaches that we found worth considering:

Curriculum generation and personalization. Teachers described creating new homework prompts, instructions for lab experiments, and visual aids. Teachers are using generative AI to add references to pop culture or classroom inside jokes to instructional materials. One teacher described using generative AI to revise assignments, tailoring the difficulty level for students with varying reading levels:

If I wrote XY assignment for them, I have students who are high achievers and love challenges. But on the other side I have other students that are struggling with reading. I can ask the AI to challenge [the high achievers] or to modify it, so it will be easier for my students to understand.

-Maria Montoya Mejia, Middle School Science Teacher, North Carolina

Rapid prototyping and starting points. Generative AI can quickly generate a wide variety of content, even though quality can vary. One teacher asked her students to generate a creative work (a poem, song, story, etc.) about the water cycle. She encouraged her students to use AI to create a first draft of a song. The students then rewrote the song, changing some of the lyrics, and recorded their own performance. Teachers report success when they encourage students to use the AI output as a starting phase in their project, and then critique, edit, or build upon that output.

Tutoring/study buddy. Steve Ouellette described a science teacher who used generative AI to prompt his students who were conducting an experiment. "The prompt was, 'You are my lab assistant. You need to provide me with the steps to do this lab, one step at a time. And wait for me to tell you that I'm ready to move on." The teacher observed and helped the students as needed, while monitoring for bad outputs from the AI. Ouellette observed that this approach could be helpful for students with executive function challenges.

Computer science teacher Chad McGowan described how he incorporated AI as an assistant when he assigned his students the challenge of writing software:

The goal is that they learn how to break down their problems so that they can ask focused questions into [ChatGPT], so they can feed a chunk of code that they've written into it and say, "Why isn't this working?" And the responses that they are getting are going to make more sense as opposed to "Write my entire program for me."

-Chad McGowan, Computer Science Teacher, Massachusetts.

Engaging students/community building. Some teachers report that they have strengthened their relationships with students while experimenting with creative prompts for AI.

I had one student who just asked ChatGPT to write their own obituary. She wasn't being morbid. I think she wanted to know about herself. I think that students want to know that they would be missed. They want to know what people think of them. They're yearning for this emotional kind of narrative."

-Richard Scullin, High School English Teacher, Massachusetts.

Many of these examples highlight one of the most important reasons to play with AI: it is fun! These tools sometimes generate content that is funny, weird, and surprising. They bring a spark of the unknown into the routines of school, and that playful spirit has a place alongside the search for efficiency and critical perspectives.

Cheating vs. "bypass productive thinking." There are valid concerns regarding how generative AI can be used to write essays, solve math problems, write code, and more. However, we want to reframe the question from " How will students use generative AI to cheat?" to "How does technology bypass productive thinking? " Generative AI is not the first technology that can be used to bypass learning; over recent decades, educators have confronted calculators, book summaries, homework websites, and internet searches. They all created initial consternation, and in most cases, educators adapted and sometimes benefitted from including these new tools. Outright banning has not worked well with calculators or internet searches, and likely will not work well with AI. Some educators may point to the use of anti-plagiarism software to detect cheating, but these solutions are often not sophisticated enough or not updated frequently enough to keep up with generative AI technology and bear the significant cost of false positives.

Data collection. All stakeholders need to be aware of how "free" generative AI platforms may be collecting data and ensure that sensitive information is not shared. Since each generative AI tool has its own collection methods and policies, educators should stay informed on how to evaluate these tools.

Garbage in, garbage out. Because generative AI models are trained on existing online data, they may reinforce harmful stereotypes. A recent Bloomberg study to understand bias in generative AI found that the majority of images generated to represent people with high-paying jobs such as "CEO," "politician," "lawyer," and so on had lighter skin and were men, while low-paying jobs such as "fast-food worker" and "social worker" were commonly people with darker skin. Educators might inadvertently promote stereotypes like these or send discouraging messages about career paths to students.

Students using generative AI for academic support may also face the challenge of filtering information produced by the models. Although the outputs from the models may look believable, they are not always accurate. The inner workings of the models are complex and opaque. Students will need to learn tools for verifying accuracy and recognizing bad outputs.

Expense and equitable access. There are monetary costs to using certain generative AI products and services. Students who can afford a personal device may have an advantage over students with fewer resources. And students who attend schools with better-prepared teachers, who have benefitted from more planning time and professional development, will have an advantage over students whose teachers have not had those opportunities.

Computer science teacher Chad McGowan told us his district initially blocked ChatGPT at the server level. He argued for them to unblock it, since the wealthier students would have access at home, using tablets and computers. The poorer students only had their school-issued Chromebooks, and he feared that they would fall behind in learning about the strengths and weaknesses of AI. The district agreed with him and unblocked the service.

Concerns beyond education. Other authors have noted concerns about the energy consumed by generative AI applications, the potential to fuel baseless conspiracy theories, proliferations of misinformation and deep fakes, and the threat to sectors of professional work. Those are discussed more in some of the sources we link to in our appendix.

We have to have a heart for kids. We have to embrace what our kids are really, really excited about. And that's the different technologies that are going to lead to great jobs for them. And so we have to stay abreast of what's going on.

-Kandiest Martin-Brock, District Coordinator for Innovation and School Improvement, Texas.

I think our purpose is to help them become the best human beings and contribute to a better world. And so if we're just talking about things like academic integrity, then we're not doing our job to prepare them for a future where the pace of change is just getting faster.

-Colleen Worrell, Director of the Center for Innovation, Teaching, and Learning, Massachusetts.

Begin with the Big Questions

What has NOT changed in education?

Most of the products marketed at you are going to not be useful. Anybody who tells you they have a groundbreaking tool... I don't think they're groundbreaking. Everybody says they're changing the game. The game doesn't change.

-Justin Reich to Connecticut Association of Boards of Education

In the past few months, companies, institutions, and researchers have been filling in the gaps with reports, policy recommendations, curricula suggestions, and training videos. Almost every EdTech company is racing to launch products around generative AI. The flurry of guidance both helps and confounds, as it can be difficult to glean the most useful information. With that flurry of activity as a backdrop, it is important to focus on what has not changed in education.

Education is a social endeavor. For many students, the primary motivation for learning is social: maintaining relationships and earning the esteem of teachers. For any new learning technology, a productive question to ask is "what human-to-human interaction does this technology support?"

Good teaching will still be needed. Students will still need teachers to guide, support, help, share valuable content, give feedback, and more. Young people will always need the mentorship of caring adults in their lives.

Foundational skills, such as problem-solving, critical thinking, and knowledge in the disciplines still matter. In fact, they may matter even more now. More information is at our fingertips than ever before, but we will need good evaluation and decision-making skills to make use of it.

Students will still need to learn how to make and create. Our world runs on creativity, from science and engineering to history and the humanities. It is crucial to foster the next generation of artists, scientists, entrepreneurs, lawyers, and leaders. We need students to be creators, not just consumers, especially as technology evolves rapidly.

Reviewing and revising what and how we teach is beneficial. The rise of new technology can be a great opportunity to look at curricula critically. Are graduates gaining the skills they need for further studies, work, or civic life? Are they on a path to be able to take jobs available in the community? Do curricula reflect the values of key stakeholders?

Not everything must change immediately. Some educators may be excited about experimenting right now, while others may not. That is okay; schools can be thoughtful and measured in adapting to generative AI. One English teacher expressed that he did not have time to experiment or monitor his students for cheating, so he simply has students write essays in class in order to practice critical skills. This is a reasonable response to the moment, but we hope teachers will find the time and support they need to explore the strengths of generative AI.

What New Opportunities and Challenges Do AI Tools Afford?

Question: What activities spark productive thinking that enhances learning? Educators should consider what kind of thinking they want their students to do. This question can be applied at big strategic levels, and it can also be applied to a specific assignment with the following question: What would happen if the student asked the AI to do the assignment? In some cases, it is possible that evaluating, critiquing, or building upon the AI's response will engage that useful thinking, indicating a good assignment in the age of generative AI. But perhaps the AI will allow the student to bypass useful thinking. In that case, the teacher can monitor the assignment in class, keeping AI unavailable, quiz students after completing the assignments, or think of another assignment. (We know. Easier said than done.)

Question: How is AI changing a discipline or subject? AI is already changing the fields of software development and graphic design. Teachers of those disciplines will likely want to teach students to integrate AI into their projects immediately. For social studies, history, and English language arts, AI cannot yet produce high-quality work. It might be able to overcome "initiation fatigue" or "writer's block" to help provide inspiration, but in these fields, the purpose of student writing is to refine reasoning, evidence, and argumentation. When used creatively, AI can play a role in supporting or inspiring student thought and writing, without doing the writing for them.

Question: What repetitive tasks can AI support? Teachers, on average, work more than fifty hours per week, but less than half of that time is spent interacting with students. The rest of their work time is filled with countless small tasks, many of which might be automated with AI. Initial reports from teachers and administrators suggest that generative AI might help draft memos, guidelines, rubrics, or emails. The technology cannot yet (and may never) generate a final draft, and educators will need time to learn to use it efficiently.

ChatGPT is really helpful with is what I call "initiation fatigue", where I'm just trying to get a kick..., I need to provide a letter of recommendation about an employee and here are some qualities that make this person really good. It will spit back an output, and I will rewrite 75% of it, but it gives me just a start.

-Steve Ouellette, Director of Technology, Westwood Public Schools

Engage and Experiment with AI

Support teacher and student exploration outside class. It is worth making time to play with AI tools, to learn their capabilities and weaknesses. Greg Schwanbeck, a physics and astronomy teacher at Westwood, asked AI to generate a song in the style of Taylor Swift that explained Newton's Three Laws of Motion. He says the output was not great, but it was a fun experiment that helped him understand the strengths and weaknesses of the tool.

One fun part of new technologies is being able to share the experience of being a novice with our students. They are as likely to invent and discover useful and creative approaches as adults.

Be skeptical of AI-powered products and tools. So far, we have not seen AI tutors, chatbots, or other helpers with the potential to "change the game" of education, though companies like Khan Academy are working to put better guardrails on their products, despite claims from some techno-enthusiasts. Most education will still happen in classrooms and be driven by the social expectations of teachers' and students' relationships. We recommend caution in adopting products, especially expensive options, until more research about efficacy emerges.

However, some of those tools will likely have some utility, and assigning or encouraging faculty and students to experiment with new tools is a great idea and can help schools evaluate which tools might best fit their needs and culture.

Recognize that adapting to generative AI will be iterative. As teachers experiment and learn how to effectively deploy, balance, and limit use of this technology, the technology will change. The handheld calculator, the scientific calculator, and the graphing calculator appeared at roughly ten-year intervals, which allowed educators to adapt to each in turn. New generations of more powerful AI may appear on much shorter time frames, such that educators might still be struggling to adapt to one, when another appears.

We do not know how quickly this technology will develop. The growth of generative AI technology does not correspond to a predictable rate or pattern as with Moore's Law. Some AI experts think that it is possible we will soon develop very powerful AI technology, sometimes referred to as AGI (artificial general intelligence). Such AGI might supplant human work in many spheres, transforming human social life in ways that are hard to imagine. Other experts think such developments will not happen, or are many decades or centuries away. It is useful to consider that there will likely be future developments in the technology, but we do not know enough about the future to design curricula or strategies around those predictions.

Put coherent policies in place. School policies can help teachers and students recognize boundaries and key principles around AI ethics, authorship, academic integrity, equity, and access, while also highlighting areas where exploration is encouraged. We cannot expect teachers to be able to conduct and lead all this experimentation in their own free time. A signature challenge that education faces, in a moment of postpandemic exhaustion, is finding the resources to pay for teachers' time to conduct this important reflection, experimentation, and refinement of their practice.

Support

Schools will not be able to move forward effectively without systematic support. History tells us that the schools with the most resources-with reasonable classroom sizes, with enough substitutes, with clean and safe buildings, with technologists and instructional coaches, with professional development budgets-will adapt to disruptive technologies most effectively. Some teachers and schools in less-resourced districts will do brilliant work, but on the whole, wealthier schools in wealthier communities will have the greater advantage when it comes to realizing the benefits of generative AI. Without additional support and investment, less-resourced schools in poorer neighborhoods are more likely to encounter the detrimental aspects of AI without the same benefits realized by wealthier schools, thus widening the disparity in educational experiences.

School districts have some resources to deploy, but many are strained. They will need curricula, guidelines, and advice but also financial resources to support professional development, new devices, technical support, adequate staffing, and an adequate substitute pool. (Many teachers' planning periods are routinely "stolen" as they are compelled to substitute for sick or absent colleagues). Teachers will need more planning time to thoughtfully adapt. We need to make sure access, training, and curriculum is made available to increase equity. Who can pitch in? Education technology companies? AI companies? Governments? NGOs? This requires a team effort.

Guidance for Policymakers and Industry Leaders

There are many papers and resources with direct recommendations to policymakers (included in our appendix). To those, we add:

MIT is only one of several excellent research institutions standing by to help adapt and realize the potential benefits of generative AI. How can you help coordinate our efforts and incorporate our research into policy decisions?

How can you provide extra support for schools to adapt to this disruptive technology? That includes both financial resources as well as guidance, curricula, and professional development opportunities.
How can you encourage or require the artificial intelligence sector to support educational goals and to consider the impact of new technology on schools, students, and teachers?
Technology companies should include educators and researchers in planning conversations. Sam Altman, co-founder of OpenAI, has suggested it would have been helpful to communicate more with educators before releasing generations of ChatGPT. The educators we surveyed all agree.

You have unleashed this tool that can do remarkable things. I think it's exciting and interesting, but we are looking for guidance: What are the skills we need to teach our kids to use this stuff in ways that help rather than hinder?

-Steve Ouellette, Director of Technology, Westwood Public Schools

For Our Fellow Researchers

It is well and good for us to provide advice for educators, parents, and students, but we need to remember that they are in the midst of a daunting challenge and admonitions without support may not land well. Advice and insight are not enough. Educators will need help sifting through the vast quantity of recommendations we have collectively generated. They are going to need curricular and financial resources. We researchers need to find ways to work together so that our insights can be amplified and simplified, and reach the people who need it most.

Balancing Optimism with Urgency

Computer science teacher Chad McGowan told us about how he advised the yearbook club as they designed the cover, with prototyping from AI. The students came up with four prompts for DALL-E, a generative AI visual art tool, which generated images based on their prompts. The students then discussed the AI's creations. "Each person was able to weigh in with 'This is what I was thinking,' or 'I like the background on this one"... just looking at different elements and thinking about how it did or didn't align," McGowan says.

The students used critical thinking skills to evaluate the AI's output, and then designed the cover without using Dall-E. We like how the assignment balanced the use of generative AI with the "traditional" design skills, and how the AI augmented, but did not replace, human creativity.

There will be millions of experiments like this one in the coming years, as teachers and students everywhere explore the possibilities of generative AI, looking for an effective balance that still challenges students to think productively. History tells us our teachers are dedicated and flexible professionals who will adapt effectively to evolving conditions when they have what they need.

We remain hopeful that generative AI will be a net gain for educators, students, and society. We know that the task of adapting to its widespread availability will be difficult. We hope educators will find the support, guidance, and time needed to adapt successfully.

"AI Machines Have Beaten Moore's Law Over the Last Decade, Say Computer Scientists." Discover, February 21, 2022. https://www.discovermagazine.com/technology/ai-machines-have-beaten-moores-law-over-the-last-decade-say-computer

Bryant, Jake, Christine Heitz, Saurabh Sanghvi, and Dilip Wagle."How Artificial Intelligence Will Impact K-12 Teachers." McKinsey & Company, January 2020. https://www.mckinsey.com/~/media/McKinsey/Industries/Social%20Sector/Our%20Insights/How%20artificial%20intelligence%20will%20impact%20K%2012%20teachers/How-artificial-intelligence-will-impact-K-12-teachers.pdf.

Dell'Acqua, Fabrizio, Edward McFowland, Ethan Mollick, Hila Lifshitz-Assaf, Katherine C Kellogg, Saran Rajendran, Lisa J Krayer, François Candelon, and Karim R Lakhani. "Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality."Harvard Business School Technology & Operations Mgt. Unit Working Paper No. 24-013. Preprint, submitted September 18, 2023. https://doi.org/10.2139/ssrn.4573321.

Hamilton, Ilana."Artificial Intelligence in Education: Teachers' Opinions on AI in the Classroom." Forbes Advisor. Last modified December 5, 2023. https://www.forbes.com/advisor/education/artificial-intelligence-in-school.

Heaven, Will Douglas. "GPT-4 Is Bigger and Better than ChatGPT-but OpenAI Won't Say Why." MIT Technology Review, March 14, 2023. https://www.technologyreview.com/2023/03/14/1069823/gpt-4-is-bigger-and-better-chatgpt-openai.

Kraft, Matthew A., and Melissa Arnold Lyon."The Rise and Fall of the Teaching Profession: Prestige, Interest, Preparation, and Satisfaction over the Last Half Century."EdWorkingPapers, November 28, 2022. https://www.edworkingpapers.com/ai22-679.

McQuate, Sarah."Q&A: UW Researcher Discusses Just How Much Energy ChatGPT Uses." University of Washington News.Last modified August 2, 2023. https://www.washington.edu/news/2023/07/27/how-much-energy-does-chatgpt-use.

Nicoletti, Leonardo, and Dina Bass. "Humans Are Biased. Generative AI Is Even Worse." Bloomberg, June 9, 2023. https://www.bloomberg.com/graphics/2023-generative-ai-bias.

Oravec, Jo Ann. "Artificial Intelligence Implications for Academic Cheating: Expanding the Dimensions of Responsible Human-AI Collaboration with ChatGPT and Bard." Journal of Interactive Learning Research 34, no. 2 (2023): 213-37. https://www.academia.edu/105260068/Artificial_Intelligence_Implications_for_Academic_Cheating_Expanding_the_Dimensions_of_Responsible_Human_AI_Collaboration_with_ChatGPT_and_Bard.

Reich, Justin. Failure to Disrupt: Why Technology Alone Can't Transform Education. Cambridge, MA: Harvard University Press, 2020.

Singer, Natasha. "Will Chatbots Teach Your Children?" New York Times, January 11, 2024. https://www.nytimes.com/2024/01/11/technology/ai-chatbots-khan-education-tutoring.html

UNESCO. "Guidance for Generative AI in Education and Research." UNESCO Digital Library. Accessed December6, 2023.
https://www.unesco.org/en/articles/guidance-generative-ai-education-and-research

Valentine, Nick."The History of the Calculator."Calculator Site. Last Modified August 26, 2022. https://www.thecalculatorsite.com/articles/units/history-of-the-calculator.php

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Generative AI and K-12 Education: An MIT Perspective

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In November of 2022, a Silicon Valley company launched an invention that could complete students' homework for them. Available only to subscribers at first, by the spring of 2023 OpenAI's ChatGPT-3.5 was available to millions of students. As of January 2023, anyone with . . .

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Free practical guides for engaging students in AI creation The Hands-On AI Projects for the Classroom guides from ISTE and GM provide elementary, secondary, elective and computer science teachers with innovative curricular resources about AI across various grade levels and subject areas. Each guide

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Student Use Cases for AI Part 2: AI as Personal Tutor

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Student Use Cases for AI Part 2: AI as Personal Tutor

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When students spend frequent one-on-one instruction time with a tutor, their learning improves. Tutors help clarify complex topics, elicit reflection, and motivate students to take ownership of their learning.

As educators, we know that many of our students could benefit from this specialized type of help. But few students have had access to personal tutors-until now.

Generative AI, if fed deliberately designed and structured prompts, has the potential to give every student a personalized tutoring experience on any topic. While there are still a number of issues with using AI for tutoring (like the substantial risk of AI-generated fabrications), the technology shows great promise as a scalable, accessible way of helping increase student comprehension.

We have asked students to use AI as a tutor when they're confused about a concept or would otherwise be looking up something on the internet. Based on this experience, we'll explain how to write and give students a prompt that morphs AI into a tutor, walk through the risks your students need to watch out for when engaging AI in this way, and offer a sample prompt you can use in your class. A note of caution: Large language models (LLMs) "know" more about some topics and less about others. It's important to test the prompt given the specific topic you teach.

Crafting the right prompt to make AI an effective tutor

We know that good tutors ask questions and scaffold students' thinking rather than just giving them answers or explanations. They create interactive dialogue, prompting students to respond actively; they check for understanding and guide students to construct their own knowledge; and they customize explanations based on what they know about students and their experiences.

Collectively, these elements make tutoring a powerful instructional strategy. They're also the basis for a strong prompt that gives AI the potential to "act" as a good tutor. Using the right prompt, any student with access to the internet can construct their own knowledge using Microsoft's Bing Chat in Creative Mode or OpenAI's ChatGPT4.

A carefully crafted tutoring prompt will ensure that the AI:

Knows its role (an expert, encouraging, upbeat tutor) and mission (to help students understand concepts and ideas by explaining those ideas and asking students questions).

Meets students where they are by asking them about their learning level, the topic they would like to learn about, and what they already know about the topic.

Provides explanations, examples, and analogies to clarify concepts, since students learn best through tailored explanations and diverse examples.

Guides students in an open-ended way and asks leading questions so that students have to explain their thinking.

Adjusts to student performance. If a student is struggling, it can offer additional support.

Prompts students to construct knowledge. For example, it can end its responses with questions so that students keep generating ideas.

Asks students to explain the concept in their own words because being able to explain something yourself is a mark of understanding.

What to do-and what risks to consider-before you give students the tutor prompt

While using AI as a tutor has the potential to enhance student learning, there are pedagogical risks involved. For instance:

The AI may not have up-to-date information about the specific topic.

There is a risk of errors and fabrications with any model, so there is a possibility of students learning the wrong thing.

The AI's output is hard to assess for students new to a topic.

With this in mind, after you write a tutor prompt, test it multiple times yourself and evaluate the outputs before giving it to your students. As AI can sometimes provide inaccurate information and may not be equally proficient across all topics, repeated testing on different models can offer insights into its effectiveness for specific subjects.

As you experiment with the prompt, be sure to introduce errors and note the AI's reactions. It's especially informative to see what happens when you mimic common student mistakes or address familiar stumbling blocks. As a teaching and subject-matter expert, you are attuned to the specific context of your students and the nuances involved in the topics you teach. Evaluate the AI from this perspective: What might your students already know about the topic? How good is the AI at explaining this topic? Does it make mistakes, and how easy are those to spot? The testing process for any prompt can go a long way in ensuring that students are more likely to learn by interacting with it.

"Generative AI, if fed deliberately designed and structured prompts, has the potential to give every student a personalized tutoring experience on any topic."

Another critical step before sharing the prompt is to ensure your students recognize the AI's limitations as a tutor-a class discussion can underscore this. Since this technology is so new, we simply don't know to what extent it can help students or how students will react to the interaction. Students without a grounding in the topic may learn the wrong thing or simply choose to believe the AI's explanation without checking with verified sources. It may, therefore, be worth engaging with the AI tutor together in a class setting to teach students about these limits. One approach is to use the prompt yourself in class, with students watching your interaction with the AI. You can work through the process together and have students share their initial thoughts.

If you do want to assign the prompt as part of a homework exercise, give students a specific topic you would like them to learn more about and ask them to use AI as a tutor to learn more about that topic on their own. Then explore the topic together in class through an active discussion that gauges student understanding, clears up sticking points or misconceptions, and builds on what students learned with the AI tutor. You can also ask students to share their interactions with the AI tutor and discuss their experiences, including what they learned and how they evaluated the AI's output. You can point out that cross-checking with valid sources is a valuable exercise for any AI interaction.
For student guidelines on how to properly interact with generative AI that you can use or modify for your class, read the prologue to this series, "Student Use Cases for AI."
A sample tutoring prompt you can use in your class

Here is a prompt ( or click here) that works for OpenAI's ChatGPT4 that we give our students to cast AI in the role of tutor. For a version of this prompt that works well for Microsoft's Bing Chat in Creative Mode, see our paper, " Assigning AI: Seven Approaches for Students, with Prompts." You can use the prompt as is or adapt as needed.
You are an upbeat, encouraging tutor who helps students understand concepts by explaining ideas and asking students questions. Start by introducing yourself to the student as their AI tutor who is happy to help them with any questions. Only ask one question at a time. Never move on until the student responds. First, ask them what they would like to learn about. Wait for the response. Do not respond for the student. Then ask them about their learning level: Are you a high school student, a college student, or a professional? Wait for their response. Then ask them what they know already about the topic they have chosen. Wait for a response. Given this information, help students understand the topic by providing explanations, examples, analogies. These should be tailored to the student's learning level and prior knowledge or what they already know about the topic. Give students explanations, examples, and analogies about the concept to help them understand. You should guide students in an open-ended way. Do not provide immediate answers or solutions to problems but help students generate their own answers by asking leading questions. Ask students to explain their thinking. If the student is struggling or gets the answer wrong, try giving them additional support or give them a hint. If the student improves, then praise them and show excitement. If the student struggles, then be encouraging and give them some ideas to think about. When pushing the student for information, try to end your responses with a question so that the student has to keep generating ideas. Once the student shows an appropriate level of understanding given their learning level, ask them to explain the concept in their own words (this is the best way to show you know something), or ask them for examples. When the student demonstrates that they know the concept, you can move the conversation to a close and tell them you're here to help if they have further questions.
(Also, see this example of an AI tutor explaining correlation versus causation to a high school student.)

Here is the text of the prompt:

Remember, the AI doesn't always execute a prompt the same way, so your students' interactions with the AI will differ. However, LLMs are pretty good at following directions. These tools are inherently interactive and conversation based (and dialogue is one key to effective tutoring).

For suggestions on using this and additional structured prompts, see our full paper, " Assigning AI: Seven Approaches for Students, with Prompts." For an introduction to LLMs and their impact on teaching and learning, see our crash course: Practice AI for Instructors and Students.

The power to democratize learning for all
From the editors: Share with us how you are using generative AI in your classes. What is your experience so far? What are your biggest concerns? What use cases have you found beneficial? We look forward to learning from you.
AI tutors represent a promise to democratize education, with the potential to bring expert guidance to learners everywhere. Yet this emerging capability comes with a unique set of challenges. How do we use the adaptability and range of AI's capabilities while also guarding against its very real limitations?

With expert guidance and vetting from instructors, AI has the potential to increase student learning in ways that were impossible before. In the next article in the series, we will extend the use of AI as a way of helping student teams solve issues and learn how to work together effectively.

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In this 4-part series on student uses cases for AI, learn how to enable generative AI to act as a tutor to offer students tailored learning experiences on a wide range of topics. We share a sample prompt and guidance for rolling this out in your class.

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Can we reverse the process where students avoid learning by using AI tools, and instead have them train a model on a specific domain or topic? Teaching is the best way to learn and maybe we could have students teach an LLM?

As I researched this idea, I found a similar initiative that worked with custom software built at Vanderbilt, called 'Betty's brain' . Not sure if anyone has tried this with LLM's in class yet, but this is exactly what I am getting at. Another trend that informed this idea is the proliferation of firms providing capabilities to provide human feedback to improve LLM's. One of the biggest firms in the space is Scale, which provides services to OpenAI to help improve the output from ChatGPT and such. So when you give a 'thumbs down' to a chatGPT response, some poor college student contracted by Outlier AI, which gets business from Scale AI, might land up with your conversation a couple days later. How do I know? my kids are training the models on outlierAI :). Perhaps we could leverage the protocols these firms use to train the models in class?

The final score for the class or module could then be based on the performance of their AI model, assessed through automatic benchmarking by a more advanced language model. The instructor would only need to train this smarter model or could use the most advanced general purpose model and create the bench marking questions and answers.

We constrain the capability of the LLM that students have been given, host it in a controlled environment so they cannot just dump an entire PDF textbook into it or feed it unlimited text for context. For starters we can stick to text based models and not include vision enabled models. They are encouraged to use external AI tools to ask questions, get answers, and synthesize the knowledge and then train their individual LLM. Another interesting spin could be if they are expected to create a knowledge graph from the readings they are given or other external sources and then use that along with the LLM.

The instructor would most likely use a flipped classroom model and perhaps could continue as earlier with some in class group problem solving/discussions. As the students learn the foundations of a topic, they could get feedback from other students testing their model or perhaps another advanced LLM, so as to replicate scaffolding. We could have some kind of instructor/TA in the loop as well. Students reflect on their training process and document it.

Each LLM instance for a student could be a docker container that could be used to create multiple instances in a cloud provider. We would use an open source LLM, so there are no licensing costs and no data leakage issues. A cheaper way would be to setup the docker container in each students laptop as long as there is a way to ensure that they dont tamper with the install.

By training their AIs, students gain insights into learning and teaching with AI as a collaborator and gives them skills that they can use to learn any topic and also prepares them for the workplace.

What do you think?

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How to train your dragon .... oops.. AI?

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A Survival Guide to AI and Teaching pt. 8: Academic Integrity and AI: Is Detection the Answer?

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A Survival Guide to AI and Teaching pt. 8: Academic Integrity and AI: Is Detection the Answer?

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Stephanie Laggini Fiore, Associate Vice Provost

Even if you've done your due diligence in clarifying acceptable use of AI in your course, you may still suspect that students are using these tools in unauthorized ways. While unauthorized AI use is not considered plagiarism, it is still cheating and a violation of the university's standards on academic honesty, as it both uses "sources beyond those authorized by the instructor in writing papers, preparing reports, solving problems, or carrying out other assignments" and engages "in any behavior specifically prohibited by a faculty member in the course syllabus, assignment, or class discussion." The sticky question is, therefore, "How can I be sure that students have indeed inappropriately used these tools to complete their work?" We may be tempted to lean on detection methods as a solution, but is that the answer to this conundrum?
Can Humans Detect AI Work Unaided?
In playing with AI tools, you may have noticed some quirks in the output they provide (based on your prompts): they can be repetitive, go off on tangents unrelated to the topic at hand, or simply produce generic or illogical text. Generative AI can also "hallucinate" citations or quote text that simply doesn't exist. These " AI tells" can sometimes tip us off to unauthorized AI use by our students. But how good are we at accurately identifying these tells? Our colleagues at the University of Pennsylvania conducted an investigation into human ability to detect AI text. They found that participants in their study were significantly better than random chance at detecting AI output but that there was large variability in ability among the participants. The good news is that their findings suggest that detection is a skill that can be developed with training over time (Dugan et al., 2023). At this point, however, few of us have had the targeted training referenced by the authors nor have we been able to dedicate the time necessary to improve. Barring glaring hallucinations or illogical content, most of us are simply not yet familiar enough with the features of AI text to be confident that our hunches are accurate. Try the test the researchers used; you may find, like me, that identifying AI text can be pretty darn challenging. And, of course, these tools will continue to evolve and improve, so our ability to detect non-human content may dwindle as generative AI advances.
Can AI Detectors Do the Job?
Don't we all wish that AI Detectors (such as Turnitin, GPTZero, Copyleaks, or Sapling) were the answer to all of our generative AI concerns? Sadly, the simple and definitive answer to whether AI detectors can reliably detect AI-generated writing is "not at this time." The reality is that these detector tools are flawed, delivering both false positives and negatives. In addition, unlike plagiarism detection tools, there is no way to verify that the detector's conclusions are correct as the results do not link to source material in the same way. The CAT and the Student Success Center are conducting an investigation into error rates in a variety of AI detectors; early indications are concerning. In the meantime, others have pointed to the unreliability of the tools in both formal and informal investigations (here's another), and in explanations of why these tools fail. Companies creating AI detectors themselves include disclaimers such as Turnitin's statement that it "does not make a determination of misconduct...rather, we provide data for educators to make an informed decision." They then go on to advise us to apply our "professional judgment" to these situations. That professional judgment, though, can itself be flawed.

Some faculty have been advised to run student work through multiple detectors, but the potential for (both positive and negative) bias may come into play as we make decisions about which detector to believe when they return different results (which, from our experience, they most likely will). My wonderful student couldn't possibly have used AI so I believe the detector that says it's human-written. OR I don't doubt for a minute that this student cheated, so I believe the detector that says it is AI-written. Importantly, these detector tools can't tell us if students have used AI in the ways we have outlined in our syllabi as permissible. Let's say I am allowing students to use AI for idea generation or for writing an outline, but not for writing full drafts of papers. The detector cannot tell me whether students have used AI in permissible ways. Finally, there are already hacks out there with advice on how to beat the detectors; for example, videos that demonstrate how to run AI-generated content through a rephraser in order to fool AI detectors. All this adds up to inconsistent and unreliable results whereby catching those who have engaged in academically dishonest behavior is hit or miss and does not provide incontrovertible proof of misconduct. Most importantly, we have to consider the very real and potentially damaging effects of wrongfully accusing students of cheating when they have not.*
What's a Harried Faculty Member To Do?
If detectors aren't reliable and our own skills at detecting AI writing are not mature, what's the answer? While we will all be adjusting to this new reality for a while, we can keep some fundamental principles in mind to nudge our students towards transparency and academic honesty, the first of which is to give up on a surveillance mentality as it simply won't be effective (and you don't want to police students anyway, right?). Instead, think developmentally and pedagogically by taking these steps:

1. Shift from a reactive to a proactive stance. Test your assessments in a generative AI tool to see how vulnerable they are to AI use. Then make some intentional decisions about whether to change assessments or create new ones. In the long run, of course, it is all about our assessments. We may have used these same types of assessments for decades, but they simply may not work in the way we want them to in the age of AI. Review blog posts #4, #5 and #6 to think about changes you may make to your assessments, or if you missed our Using P.I. to Manage A.I. series, see our suggestions there. Remember you can also make an appointment with a CAT developer to help you think this through.

2. Put a statement about AI in your syllabus clarifying acceptable use of AI! I can't repeat this enough. Our colleagues at The Office of Student Conduct and Community Standards have expressed to us that it is essential to have clear guidelines clarifying what is and isn't acceptable use of AI in our courses.

3. Engage your students in a discussion about generative AI and academic integrity, including why you have set the standards you have in your course. Remind them periodically about the ethics of generative AI use. (Look for an upcoming blog post for guidance on how to speak with your students about AI.)

4. Design courses that reduce the factors that induce students to cheat. James Lang, in his excellent book Cheating Lessons: Learning From Academic Dishonesty, reminds us that the literature on cheating points to an emphasis on performance, high stakes riding on the outcome, an extrinsic motivation for success, and a low expectation of success as factors that promote academic dishonesty. The good news is that we know also from the literature on learning that evidence-based teaching practices such as formative assessments, scaffolded assignments, ample opportunity for practice and feedback, development of a positive learning environment, and helping students to find relevance and value in what they are learning will both deter cheating by reducing these factors, and improve learning. Need help in reducing the temptation to cheat? Make an appointment with a CAT developer.

5. Plan thoughtfully for how you will manage situations where you suspect unauthorized use of generative AI, starting with a conversation with the student. (We'll include advice on how to speak to students in the aforementioned future blog post.)

There is no doubt that generative AI is a disruptor in the educational space. Our response to that disruption matters for learning and for our relationship with students. Let's work together thoughtfully towards a productive and forward-looking response. The answer is not detection-it is development

*Note: If I haven't convinced you to avoid these flawed detectors in accusing students of cheating, I agree with Sarah Eaton that it is essential to transparently state in your syllabus that you will be using detectors. Do not resort to deceptive practices in an effort to "catch" students. In addition, never use detectors as the sole source of evidence as, of course, the results may not be reliable.
Stephanie Laggini Fiore serves as Associate Vice Provost at Temple University's Center for the Advancement of Teaching.

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A Survival Guide To AI and Teaching pt. 8: Academic Integrity and AI: Is Detection the Answer?

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Stephanie Laggini Fiore, Associate Vice Provost Even if you've done your due diligence in clarifying acceptable use of AI in your course, you may still suspect that students are using these tools in unauthorized ways.

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AI Detectors Don’t Work. Here’s What to Do Instead.

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AI Detectors Don’t Work. Here’s What to Do Instead.

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MIT Sloan Teaching & Learning Technologies

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AI Detectors Don’t Work. Here’s What to Do Instead. - MIT Sloan Teaching & Learning Technologies

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As new tools raise concerns around AI academic integrity, instructors can deter misuse through transparent policies, open discussions with students, assignments that engage intrinsic motivation, and inclusive teaching practices.

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AI at Work is Here. Now Comes the Hard Part

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AI at Work is Here. Now Comes the Hard Part

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Classroom Chatbot Improves Student Performance, Study Says

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Classroom Chatbot Improves Student Performance, Study Says

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ATLANTA-Georgia State's pioneering, artificial intelligence-enhanced chatbot "Pounce" is well established as an institutional tool for helping incoming students navigate the thorny world of finances, registration and just getting started in college. Now, Georgia State is showing student performance jumps when classes employ the chatbot to keep them connected.

Receiving direct text messages about their class assignments, academic supports and course content increased the likelihood students would earn a B or higher and, for first-generation students, increased their likelihood of passing the class. First-generation students receiving the messages earned final grades about 11 points higher than their peers.

"Eleven points is more than a full letter grade, and a full letter grade can be the difference between students holding onto their HOPE Scholarship and Pell Grant awards or not," said Timothy M. Renick, the founding executive director of the National Institute for Student Success at Georgia State. "These are exceptional results from a fairly light-touch initiative."

An AI-enhanced text messaging tool in use at Georgia State since spring 2016, the institution-level Pounce chatbot is just one of myriad efforts by the university to support students as they progress through college. Georgia State's student success efforts have made it a national leader in graduating students from diverse backgrounds and reduced the average time it takes to earn a degree by almost a full semester.

In 2021, the chatbot was integrated for the first time into course content. A group of 500 students in one of Georgia State's largest courses - political science 1101 - was selected to participate in a randomized controlled trial led by Lindsay Page, the Annenberg Associate Professor of Education Policy at Brown University. Half of the students were selected to receive messages related to the class from the chatbot, and half did not receive the messages.

Page and colleagues Katharine Meyer of Brown and Tyler Walsh of Georgia State presented the findings last week, in a talk titled "Let's Chat: Chatbot Nudging for Improved Course Performance," during a conference of the Association for Education Finance and Policy in Denver.

The study was conducted in coordination with Michael Evans, senior lecturer in political science at Georgia State and the faculty coordinator for the course, taken by about 3,000 students each semester to satisfy degree requirements in American government. Students participating in the fall 2021 trial were taking the course fully online.

Evans said the course typically has a relatively high rate of what are known as DFWs (students receiving a grade of D, F or withdrawing) - around 15 percent prior to the COVID-19 pandemic and as high as 25 percent since the onset.

"It's a challenge because a lot of times students are just learning how to be college students," Evans said. "We get every type of student, in terms of their preparation."

Students already get email reminders about assignments, quizzes and exams, but Evans said students receiving text messages from the "PolsPounce" chatbot were getting an extra, direct reminder as well as the opportunity to text questions back, through PolsPounce, to their professor. Some reminders were targeted to students who didn't have much time left to complete a reading assignment by the deadline. Students were also reminded when an exam was coming up and given an opportunity to take a short practice quiz to gauge their preparedness.

"Clearly this was having a bigger impact than just the emails, and part of that was because they were receiving the message," Evans said. "I did not expect we'd get this overwhelming evidence that the chatbot is this effective."

According to Page's study results, students who received the PolsPounce messages earned grades of B or above at a rate 16 percent higher than those not on the chatbot.

The average final grade in the class for first-generation students in the control group was 64, while similar students receiving the messages earned a final grade an average of 11 points higher.

Among students who were not in their first year of college, those who received the chatbot messages were more likely to pass the class and less likely to drop their other Georgia State classes. They also did better in their other classes, earning fall term grade point averages 0.28 points higher.

Students participating in the study who displayed lower academic performance in high school were more likely to attend supplemental instruction, more likely to earn a B or higher in the class and complete more credits for the semester if they received the chatbot messages.

"While we've seen that students from all kinds of different demographic groups benefit, what we've seen consistently is that students who struggle the most overall benefit the most from the chatbot intervention," Renick said. "Students who are low income, the first in their families to go to college or from underrepresented minorities often lack a support system at home or familiarity with how to get help in college. They are the ones to benefit the most by having this tool in their hand 24/7."

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Classroom Chatbot Improves Student Performance, Study Says

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Research involving a Georgia State University course using a chatbot to keep students connected showed improved grades and retention rates.

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Using Detailed Rubrics in Combating AI Content Generators

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Using Detailed Rubrics in Combating AI Content Generators

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Using Detailed Rubrics in Combating AI Content Generators

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In the age of advanced technology, educators face the challenge of deterring students from relying on AI content generators for their academic papers. By employing detailed rubrics, educators can empower students, foster critical thinking, and preserve the significance of written assignments in the

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A Student Guide to Navigating College in the Aritifical Intelligence Era

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A Student Guide to Navigating College in the Aritifical Intelligence Era

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Generative Artificial Intelligence University of Michigan Resources

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Generative Artificial Intelligence University of Michigan Resources

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Safe Computing Guidance

U-M AI Services

U-M AI Services (, , and U-M GPT Toolkit) meet the privacy and security standards of the university to be used with institutional data of Moderate sensitivity, including FERPA data. For details, see the ITS AI Services page in the Sensitive Data Guide.

See below for guidance provided to U-M community members about the use of third-party Artificial Intelligence (AI).

Third-Party AI Tools

The University of Michigan is actively reviewing the role third-party AI tools, like ChatGPT, play at the university, and part of that review involves examining formal contracts and agreements with AI vendors. Currently, U-M does not have a contract or agreement with any AI provider, which means that standardized U-M security and privacy provisions are not present for this technology. There are, however, still ample opportunities to experiment and innovate using third-party AI tools at the university.

The university's guidance on third-party AI usage will adapt and change as U-M engages in broader institutional review and analysis of these tools. U-M encourages its community members to use AI responsibly and review the data inputted into AI systems to ensure it meets the current general guidelines.

U-M Guidelines for Secure AI Use (Third-Party Tools)

1. Third-party AI tools should only be used with institutional data classified as LOW.

2. Third-party AI tools like ChatGPT should not be used with sensitive information such as student information regulated by FERPA, human subject research information, health information, HR records, etc.

3. AI-generated code should not be used for institutional IT systems and services unless it is reviewed by a human and meets the requirements of Secure Coding and Application Security.

4. Open AI's usage policies disallow the use of its products for many other specific activities. Examples of these activities include, but are not limited to:

Illegal activity

Generation of hateful, harassing, or violent content

Generation of malware

Activity that has high risk of economic harm

Fraudulent or deceptive activity

Activity that violates people's privacy

Telling someone that they have or do not have a certain health condition, or providing instructions on how to cure or treat a health condition

High risk government decision-making

For more detail, please see Artificial Intelligence and U-M Institutional Data on the Safe Computing website.

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U-M AI Services (, , and U-M GPT Toolkit) meet the privacy and security standards of the university to be used with institutional data of Moderate sensitivity, including FERPA data. For details, see the ITS AI Services page in the Sensitive Data Guide.

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Read the Report
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Moving from reaction to action, higher education stakeholders are currently exploring the opportunities afforded by AI for teaching, learning, and work while maintaining a sense of caution for the vast array of risks AI-powered technologies pose. To aid in these efforts, we present this inaugural EDUCAUSE AI Landscape Study, in which we summarize the higher education community's current sentiments and experiences related to strategic planning and readiness, policies and procedures, workforce, and the future of AI in higher education.

Strategic Planning and Readiness

Primary Motivators for AI-Related Strategic Planning

AI-related strategic planning is primarily motivated by institutions' need to keep up with the rapid uptake of AI tools.

Read more about strategic planning and readiness >

Strategic Leaders and Partners

Impressions of Institutional Leaders' General Attitude toward AI, by Job Role

Institutional leaders are cautiously optimistic about AI.
Learn more about strategic leaders and partners >
Policies and Procedures

Impact of AI on Institutional Policies

Institutional policies are being revised and created to address AI-related issues.
Read more about policies and procedures >
Workforce and the Future of AI in Higher Education

AI-Related Workforce Changes

AI is just starting to make an impact on the higher ed workforce.
Learn more about workforce and the future of AI >
More AI Resources

Looking for other resources on AI? The EDUCAUSE Library has a breadth of resources on this topic and many others.
Explore other AI resources in the EDUCAUSE Library >

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This report summarizes higher education's experiences related to AI strategic planning and readiness, policies and procedures, workforce, and its futu

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Reducing Points of Friction with AI: Making Institutional Processes More Accessible, Equitable, and Efficient

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Reducing Points of Friction with AI: Making Institutional Processes More Accessible, Equitable, and Efficient

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Inside Higher Ed | Higher Education News, Events and Jobs

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Reducing Points of Friction With AI: Making Institutional Processes More Accessible, Equitable and Efficient | Inside Higher Ed

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Much of the discourse on AI in higher education has focused on classroom applications for teaching and learning and on AI’s potential to automate routine tasks in ways that boost institutional efficiency. Inside Higher Ed’s newly-published deep dive report examines opportunities that lie between these complex pedagogical and more basic AI use cases to streamline processes in ways that have a direct and meaningful impact on students.

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Generative AI Resources from US San Diego

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Generative AI Resources from US San Diego

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AI Across the Curriculum – The Center for Teaching and Learning

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The Center for Teaching and Learning has launched a stream of programs and resources for faculty and instructional staff to support the increasing role that Generative Artificial Intelligence (AI) is playing in shaping teaching, learning, and classroom learning experiences.

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Getting Started with AI: Good Enough Prompting by Ethan Mollick

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Getting Started with AI: Good Enough Prompting by Ethan Mollick

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Getting started with AI: Good enough prompting