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Southern New Hampshire University AI Policy

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Southern New Hampshire University AI Policy

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SNHU’s AI page highlights a thoughtful, institution-wide approach to integrating artificial intelligence in ways that enhance learning, productivity and the overall human experience in education. It showcases how the university pairs innovation with strong commitments to ethics, equity, and responsible use, supported by training, governance frameworks, and a “test, learn and invest” implementation model.

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Your future. Your terms. See why thousands choose SNHU.

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It was cool to see all the international perspectives on AI use. 

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It was cool to see all the international perspectives on AI use. 

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A study, dated 15 Nov 2025, on comparative analysis of AI policies at 313 universities in five countries!

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A study, dated 15 Nov 2025, on comparative analysis of AI policies at 313 universities in five countries!

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policies

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Comparative analysis of artificial intelligence policies in universities across five countries

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Members of the Yale Admissions Committee review the written components of an application to better understand an individual’s unique perspective, background, and insights related to their experiences and aspirations. Successful applications showcase a prospective student’s mind and character, demonstrating much more than merely fluent or cogent writing.

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AI Policy Statement | Yale College Undergraduate Admissions

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Members of the Yale Admissions Committee review the written components of an application to better understand an individual’s unique perspective, background, and insights related to their experiences and aspirations. Successful applications showcase a prospective student’s mind and character, demonstrating much more than merely fluent or cogent writing.

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You should always double check information with reliable sources.

Using AI for a final answer could be plagiarism.

AI should be used for brainstorming, not answering questions (as previously stated)

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Is it bad to use ChatGPT as a way to give you some ideas to start a sentence/paragraph?

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primarily concern academic integrity, responsible technology use, and ethical training

St. Petersburg College (SPC)

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AI is a huge problem in the learning field nowadays. With informations being able to be given in a second rather than a 1hr class, educators can’t rely on students to do their work themselves. Universities have different policies about AI and it can be a very huge difference. Some universities like Penn allow the use of AI, only if you cite it. Others allow a somewhat use of AI if the professor allows and only allows it. Even some universities ban AI and no use of it at all in any assignment and kicked out. While universities allow a little of AI use, overall you shouldn’t use AI and it won’t get you anywhere in college.

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AI policy link

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Grammarly has two sets of tools. If you plan to use Grammarly and your professor has banned the use of AI tools, please make sure you have turned the generative AI function off. For more information, please see Grammarly’s Introducing generative AI assistance.

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Guia - Inteligencia Artificial Generativa (IAG) en la docencia.pdf

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Very robust resource; while it's K-12, it's applicable to HigherEd as well.

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Universidad Peruana de Ciencias Aplicadas

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Universidad Peruana de Ciencias Aplicadas

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Las herramientas de IAG, como ChatGPT, pueden ser utilizadas para potenciar el aprendizaje activo de los estudiantes. Estas herramientas pueden ser usadas por docentes y estudiantes como asistentes poderosos en procesos de reflexión, análisis, investigación y creación en cualquier área del conocimiento. Al utilizar estas herramientas, se fomenta el desarrollo de la creatividad, el pensamiento crítico y la curiosidad por aprender. Se recomienda incorporarlas como un medio para un fin, de modo que su uso sea un recurso durante el proceso de aprendizaje, en el cual se realicen investigaciones, debates, conversaciones reflexivas, planteamiento de problemas y resolución de problemas contextualizados. El objetivo es que las herramientas de IAG sean utilizadas como una ayuda complementaria para enriquecer el aprendizaje y potenciar el protagonismo de los estudiantes en su propio proceso educativo. La UPC cuenta con un espacio dedicado a compartir experiencias sobre el uso de herramientas de Inteligencia Artificial en la Educación https://innovacioneducativa.upc.edu.pe/inteligencia-artificial-en-la-educacion/.

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EYA-PYL-07 LINEAMIENTOS PARA EL USO DE HERRAMIENTAS DE INTELIGENCIA ARTIFICIAL GENERATIVA EN EL PROCESO DE ENSEÑANZA – APRENDIZAJE Y ASSESSMENT | SICA

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A student should include the following statement in assignments to indicate use of a Generative AI Tool: “The author(s) would like to acknowledge the use of [Generative AI Tool Name], a language model developed by [Generative AI Tool Provider], in the preparation of this assignment. The [Generative AI Tool Name] was used in the following way(s) in this assignment [e.g., brainstorming, grammatical correction, citation, which portion of the assignment].”

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Suggested Course Syllabus Statements

When using AI tools, it is important to be aware that the user data supplied might be utilized for training AI models or other purposes. Consequently, there is no guarantee that the information you provide will remain confidential. Instructors and students should exercise caution and avoid sharing any sensitive or private information when using these tools. Examples of such information include personally identifiable information (PII), protected health information (PHI), financial data, intellectual property (IP), and any other data that might be legally protected.
(i) Open Use Guidelines: Embrace and encourage AI use in assignments, with the requirement that students disclose any AI assistance. AI Policy: Permitted in this Course with Attribution
In this course, students are encouraged to use Generative AI Tools like ChatGPT to support their work. To maintain academic integrity, students must disclose any AI-generated material they use and properly attribute it, including in-text citations, quotations, and references.

A student should include the following statement in assignments to indicate use of a Generative AI Tool: "The author(s) would like to acknowledge the use of [Generative AI Tool Name], a language model developed by [Generative AI Tool Provider], in the preparation of this assignment. The [Generative AI Tool Name] was used in the following way(s) in this assignment [e.g., brainstorming, grammatical correction, citation, which portion of the assignment]."
(ii) Moderate Use Guidelines: Encourage AI use in specific assignments, but not all. Students must disclose any AI assistance. AI Policy: Permitted when Assigned in this Course with Attribution
In this course, students are permitted to use Generative AI Tools such as ChatGPT for specific assignments, as designated by the instructor. To maintain academic integrity, students must disclose any use of AI-generated material. As always, students must properly use attributions, including in-text citations, quotations, and references.

A student should include the following statement in assignments to indicate use of a Generative AI Tool: "The author(s) would like to acknowledge the use of [Generative AI Tool Name], a language model developed by [Generative AI Tool Provider], in the preparation of this assignment. The [Generative AI Tool Name] was used in the following way(s) in this assignment [e.g., brainstorming, grammatical correction, citation, which portion of the assignment]."
(iii) Strict Use Guidelines: Discourage AI use in this particular course. AI Policy: Not Permitted in this Course
In this course, it is expected that all submitted work is produced by the students themselves, whether individually or collaboratively. Students must not seek the assistance of Generative AI Tools like ChatGPT. Use of a Generative AI Tool to complete an assignment constitutes academic dishonesty.

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Suggested Course Syllabus Statements When using AI tools, it is important to be aware that the user data supplied might be utilized for training AI models or other purposes. Consequently, there is no guarantee that the information you provide will remain confidential. Instructors and students shoul

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Generative AI in Ghent University Education: Impact and Approach | Educationaltips

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If you choose to use an artificial intelligence (AI) technology for an assignment, your ethical responsibilities as a student are to:

Check your course syllabus and assignments for the AI policy, including how it can or cannot be used.

Avoid submitting sensitive information as it may be used to train the tool. Never provide personal or private information or restricted data (see Penn State’s University Information Assurance and IT Security Policy AD95). Commercial data protection is provided in Microsoft Copilot when signing in with your Penn State login.

Comply with relevant institutional policies, federal and state laws including Privacy Policy AD53, Accessibility Guidelines AD69, and the Family Educational Rights and Privacy Act (FERPA). Also see Guidelines for AI Use at Penn State.

Verify and carefully review generated content as AI may hallucinate and produce biased, inaccurate, incorrect, or incomplete information.

Be transparent and acknowledge how you used artificial intelligence in your work and properly cite it. Failure to cite artificial intelligence may be considered an academic integrity violation. Citation resources may be found at Penn State Libraries Generative AI: ChatGPT and Beyond.

Recognize that you are ultimately responsible for your submissions.

If in doubt, ask your course instructor or TSS for clarification before using AI for any course work.

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AI Centers of PENN STATE WE ARE … EMBRACING CHANGE BEING COURAGEOUS INNOVATING IDEAS TAKING ON CHALLENGES The Center for Artificial Intelligence Foundations and Scientific Applications Accelerating Scientific Advancement through AILearn MoreThe Center for Applications of Artificial Intelligence and Machine Learning to Industry​Connecting Industry with AI ExpertsLearn MoreThe Center for Socially Responsible Artificial IntelligenceNavigating & […]

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American University in Cairo AI Guidelines

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American University in Cairo AI Guidelines

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Includes guidelines for faculty, guidelines for students and will soon also include syllabus guidelines and guidance on reporting improper use

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Artificial Intelligence | The American University in Cairo

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As AI tools become increasingly integral to professional, creative, and academic endeavors, AUC recognizes the need for clear guidelines.

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Principles and guidelines about generative AI use for multiple use cases and audiences, including but not limited to teaching and learning.

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Principles for the Use of Generative AI Tools (PDF)

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Generative AI is a powerful technology that has the potential to transform the way we teach, learn, and work. However, we must carefully consider issues such as data privacy, bias in algorithms, and the potential impact on education. Striking the right balance between AI and human interaction is crucial. The resources below aim to support […]

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Generative Artificial Intelligence (genAI) tools, including large language models like ChatGPT and text-to-image tools like Midjourney, are driving an ongoing conversation about their academic uses. The Office of the Provost encourages the entire University community to explore the uses and impacts of GenAI technologies, whether through critical discussions or creative applications.

GenAI tools represent an addition to the learning process that can be deployed in a number of innovative ways to advance learning outcomes. Faculty are invited to make thoughtful use of genAI tools in their teaching. Used properly, genAI tools can enhance the design of lessons, assignments, and assessments. AI and digital literacies are fundamental skills that students will need in their respective fields and as public citizens and consumers for the rest of their lives. There are many productive ways in which they might use them as students, consistent with stated course policies and outcomes.

While the intent of this resource is not to prescribe universal policies of when to allow or disallow generative AI, the text below provides some guidelines for the use of genAI in connection with academic work at the University.

"I think generative AI offers us this really exciting opportunity to reframe our classrooms as spaces of radical trust, transparency, and community with students-as spaces where we can explore, innovate, and grapple with big, ethical questions together."

Carter Moulton

"Generative AI offers the opportunity to question the systems that we often see as infallible, and to consider whether it is wise to blindly trust the output of a computer over our own intuitions. Additionally, the utility provided by generative AI gives more time for students and instructors to explore topics in way that may not have been practical before."

TRUST

Students and faculty are encouraged to work together to determine how genAI will be used transparently in their coursework, and should learn from each other on how both students and instructors are using genAI in and out of the classroom.

ETHICS

GenAI tools are being developed at an incredible pace. Though promising, genAI tools may be inaccurate, inefficient, or biased. GenAI outputs may come from questionable sources: a network of unknown and known genAI tools, sources that limit or remove human credit, sources that limit or remove pay to humans, etc. It is critical to interrogate both genAI inputs and outputs so as to comply with ethical and legal standards.

Mines is committed to supporting an accessible digital environment for all members of our community. Faculty should proactively investigate how their incorporation of genAI tools might unintentionally build barriers to student learning. When bringing genAI tools into the classroom, faculty should provide equitable access and ensure that they are offering avenues for diverse learners. Financial considerations should be made as not all genAI models are free, and the most updated versions of genAI tools usually come at a cost. Additionally, instructors should build a shared literacy around selected genAI tools by assessing and/or teaching students to use a tool before significant use is expected.

COMMUNICATION

Faculty are expected to state explicitly and affirmatively their expectations regarding student use of genAI tools. Instructors should specify in writing the permitted and prohibited uses of genAI tools in their courses, and should seek to clarify any expectations if they differ from one assessment to another. For additional support, see this faculty guide for clarifying resource use on individual assignments.

HUMAN

All Mines community members play a role in adding to genAI use. Humans will always have unique lived experience and agency to make their own decisions on how to use, implement, and apply genAI tools in the classroom. It is recommended to center student iteration and growth, and to help students develop critical and metacognitive thinking skills to identify where genAI complements, supplants, or fails to replace human contributions. Faculty and students are expected to be creative in their ethical use of genAI tools in ways that center human experience and evaluation.

Guidance is provided below for faculty to consider adding a statement to their syllabus to aid in the communication about genAI tools in their courses. These guidelines have been adapted from George Washington University by a group of Mines faculty, students, and staff. There are three suggested paths for faculty to consider.

Instructors might 1) generally permit the use of genAI tools; 2) generally forbid their use; or 3) permit their use for certain purposes on certain assignments, but not others. If an instructor wishes to permit certain uses of genAI tools, such uses must be set forth explicitly in the course syllabus and/or assignment instructions. Below is some model language for the three permission options.

Generative Artificial Intelligence (genAI) tools such as ChatGPT are important resources in many fields and industries. Because these tools will be used in professional and personal contexts, we believe it is valuable for you to engage critically with these tools and explore their use in generating content submitted for evaluation in this course, including [ papers; take-home examinations; specified other assignments] . You remain responsible for all content you submit for evaluation.

You may use genAI tools to help generate ideas and brainstorm. However, you should note that the material generated by these tools may be inaccurate, incomplete, biased, or otherwise problematic. We encourage you to consider how genAI complements, supplants, or fails to replace your contributions and abilities .

If you include content (e.g., ideas, text, code, images) that was generated, in whole or in part, by Generative Artificial Intelligence tools (including, but not limited to, ChatGPT and other large language models) in work submitted for evaluation in this course, you must document and credit your source . Failure to properly cite sources, including AI tools for generating content, would be considered Academic Misconduct in violation of . Mines Academic Integrity/Misconduct Policy
Additional language regarding the potential pitfalls of genAI, strategies for citation, and methods for centering the human dimension of learning can be found in provided by the

By submitting work for evaluation in this course, you represent it as your own intellectual product. Submitting content for evaluation (e.g., ideas, text, code, images) that was generated, in whole or in part, by Generative Artificial Intelligence tools (including, but not limited to, ChatGPT and other large language models) would be considered Academic Misconduct in violation of . Mines Academic Integrity/Misconduct Policy

By submitting work for evaluation in this course, you represent it as your own intellectual product. Submitting content for evaluation (e.g., ideas, text, code, images) that was generated, in whole or in part, by Generative Artificial Intelligence tools (including, but not limited to, ChatGPT and other large language models) would be considered Academic Misconduct in violation of unless granted permission to do so. We will explain to you the specific uses of genAI tools that are permitted or prohibited in this course, including on what specific assignments use of genAI tools is permitted. Mines Academic Integrity/Misconduct Policy
Faculty might consider using the Faculty Guide For Clarifying Uses tool when specifying permitted uses of external resources, including genAI.

In the absence of explicit directions in writing to the contrary from instructors (instructors: see above for several examples of alternative course policies), the following provisions provide default guidance for the use of genAI under the . Academic Integrity/Misconduct Policy

Students must represent any work (e.g., ideas, text, code, images) submitted for evaluation as the student's own intellectual product, created in accordance with established course and university policies. Unless instructors provide explicit permission or instruction to the contrary, students may not submit content for evaluation that was generated, in whole or in part, by genAI tools. Using genAI tools when unauthorized would be considered a potential violation of Mines' policy on academic integrity.

Illustrative, but not exhaustive, examples of prohibited conduct include:

A student types a prompt into a genAI tool and copies all or part of the generated content (e.g., text, code, images, solutions) into their answer on an assessment, essay, test, or other assignment submitted for evaluation (whether in-class or out-of-class).

A student provides a genAI tool with all or part of the prompt for an out-of-class assignment (e.g., essay or coding assignment) and paraphrases or otherwise adapts all or part of the generated content into their response without proper attribution of the genAI tool.

2. Students are permitted and encouraged to explore the use of genAI tools to generate (and critically examine) content that is not submitted to an instructor for evaluation. Unless instructors provide explicit direction to the contrary, students may use genAI tools for learning, studying, proofreading, and brainstorming.

Illustrative, but not exhaustive, examples of permitted conduct include:

A student uses genAI tools to study for exams, quizzes, and other assessments by providing prompts to the genAI and reviewing the output.

A student uses genAI tools to explore the capabilities and potential uses of genAI in an area of their interest.

A student copies the text of a writing assignment into a genAI to ask for proofreading, while making sure to adhere to established university policies.

Mines community members have a responsibility for establishing, maintaining, and fostering an understanding and appreciation for academic integrity. For all their promise, genAI tools misused could interfere with learning outcomes and impair the development of students' skills in and outside of the classroom, as well as harm the substance of degrees awarded at Mines.

An instructor who suspects an academic integrity violation based on genAI tools should follow Mines , following Academic Integrity/Misconduct Policy This website is designed to provide both students and faculty policy and procedure access to best understand rights and responsibilities of all involved parties when academic misconduct is suspected. The Office of Community Standards staff can also be points of contact for questions regarding policy implementation as it relates to genAI tools. guidance from the Office of Community Standards.

At times, faculty may be interested in using AI-detection resources provided by Mines (e.g. Turnitin) to assess academic integrity in student work. These tools may not be available for faculty to rely on institutionally, and should not be the only tool used to make decisions on academic integrity. The Office of Community Standards staff can provide additional considerations to support faculty in making decisions which honor the rights and responsibilities of faculty and students in the academic integrity/misconduct process.

Each academic department is encouraged to prepare for the increased use of genAI tools within their departments in some of the following ways: to discuss academic integrity expectations within each department, to (re)design assignments to focus on process over output, to create clear expectations of genAI use on specific course tasks, and to develop ways to build trust between students and faculty. All of these examples are proactive ways to reduce academic misconduct in coursework. The Office of Community Standards and the have worked together to provide a resource to help instructors proactively disincentivize academic misconduct .

The continued development of ChatGPT and genAI offers us an opportunity to revisit course assignments and assessments to ensure that they spark curiosity, foster intrinsic motivation, and promote high-level critical and ethical thinking as described in the TEACH ing with genAI section above. We encourage instructors to design assignments that center the learning as much as (if not more than) through evaluative critique, self-reflection, and opportunities for revision. Here are a few ideas for exercises and prompts that explicitly encourage students to critically engage with genAI:

Ask students to describe how they utilized genAI for any given assignment and reflect on their process-what they learned, the challenges and frustrations they faced, what they did to overcome those challenges, their takeaways from the experience, etc.

Ask students to practice formulating effective prompts.

Ask students to critically evaluate AI-generated outputs with respect to accuracy, persuasiveness, bias, equity, quality, and so forth.

Ask students to investigate, fact-check, criticize, or edit AI-generated content.

Ask students to practice metacognition and reflect on where genAI complements, supplants, or fails to replace their own contributions. This can be done by asking students to compare their own work with AI-generated work and to identify their own strengths, tendencies, and perspectives.

In addition to designing assignments that ask students to critically engage with genAI, there are a number of design strategies instructors can use to disincentivize cheating, center the human in assignments, build trust with students, and foster intrinsic motivation:

Set clear expectations (in writing) and communicate the relevance of any given assignment or activity.

Offer opportunities for low-stakes, collaborative practice. Give students a chance to identify mistakes, make corrections and revisions, discuss strategies with their peers, and re-present their thinking in low-stakes contexts.

Incorporate unpenalized opportunities for students to revise and resubmit their work. Consider grading policies that reward student perseverance and growth.

Encourage multimodal communication and creativity (voice memos, podcasts, videos, brochures, infographics, websites, classroom presentations, etc).

Ask students to connect course content, classroom conversations, and their own lived experience. Not only will this create tasks that are more difficult for genAI, but it can help foster intrinsic motivation through personal connections with course content.

Ask students to set personal learning goals and reflect on their progress throughout the semester. Cultivating a "growth mindset" will help students to see the value of their learning process and take ownership over their work.

The Trefny Center has provided useful guidance for instructors to implement effective teaching practices with genAI . The Office of the Provost encourages instructors to consult this resource.

We encourage the campus community to adopt an open-minded and future-oriented approach to working with genAI tools. Our call-to-action reminds us that we are surrounded by dynamic and disruptive change that we will navigate together as a community. To do so, we should work against complacency-believing that the status quo will be successful in the future-and orient toward relevance. Furthermore, to stay competitive as an institution, we need to acknowledge the changing needs that shape education.

The Office of the Provost encourages instructors to confer with the if they have questions about best practices for course design, pedagogy, assessment, and with the for questions about academic integrity. The Office of Community Standards maintains a for genAI tools that you can use in your course. list of citation practices

These guidelines, which will necessarily evolve as genAI continues to evolve, emerged out of conversations that brought together faculty, staff, and student voices in the summer of 2023 on the Colorado School of Mines campus. Principles that guided this drafting process included being future-oriented, reducing harm, focusing on student learning, avoiding punitive culture, and providing a holistic approach that supports the core values and mission of the university.

These guidelines and resources will necessarily evolve as gen-AI continues to evolve. If you do have suggestions, questions, or insights to add, please share them using this form.

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Guidelines for Using Generative Artificial Intelligence at Mines - Academic Affairs

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Generative Artificial Intelligence (genAI) tools, including large language models like ChatGPT and text-to-image tools like Midjourney, are driving an ongoing conversation about their academic uses. The Office of the Provost encourages the entire University community to explore the uses and impacts of GenAI technologies, whether through critical discussions or creative applications.

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Teaching with Generative AI

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NYU Abu Dhabi teaching and learning guidelines for generative AI.

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Video: Generative AI in the Classroom

Generative artificial intelligence, which includes natural language processing systems, may have disrupted some of the traditional classroom practices but it also offers new and exciting opportunities. The prevalence of generative AI demands a rethinking of traditional assessment methods as well as a call to come up with creative ways to use it for an enhanced learning experience. Watch this video to learn more about how you can adapt your teaching style to take Generative AI into account.

How to talk with your students about the rules and regulations of using AI:

It is crucial to note that you should never enter personal student information into ChatGPT, nor your own. The platform is trained on what you enter into it and the content you enter in can be reproduced in a response to someone else's prompt.**

We ask all faculty to include a ChatGPT statement on their syllabus and discuss this with your students. Please also ensure you have a notice on your syllabus that you reserve the right to use TurnItIn for written assignments.

To get started in reflection on how ChatGPT impacts your curriculum and pedagogy ask yourself these four questions:

1. What are the cognitive tasks students need to perform without AI assistance? When should students rely on AI assistance from ChatGPT or similar platforms (Google's Bard, Microsoft Bing, etc.)?

2. Where can an AI facilitate a better outcome?

3. Are there efficiencies in grading that can be gained? Are new rubrics and assignment descriptions needed?

4. Will you add a generative ai code of conduct to your syllabus?

Whatever strategy you choose to pursue, all strategies require explicit communications with your students about your expectations and your hopes for what they will learn from the assignments. Please include a description of your preference of when, how, and where ChatGPT can be used in your course on your syllabus and discuss it with your students.

What we'll be doing over the next few years will involve specific adaptations to the strengths and weaknesses of specific tools, but the underlying change in teaching and learning is moving faculty towards being more explicit about both goals and strategies to pursue those goals in their classrooms.

Clay Shirky, NYU Vice Provost for AI and Technology in Education

Sample Syllabus Language

It is not permitted to use ChatGPT for any submitted and assessed assignments in this course. The use of AI software or apps to write or paraphrase text for your paper is not permitted. The use and submission of AI software or apps to write or paraphrase text for your paper constitutes fraud, as you did not author these words or ideas. You may not represent the output of an AI / machine-learning tool as your own work. This would be dishonest, and is a violation of the academic integrity codes of New York University. For instance, if an algorithm constructs an image, a block of code, or a paragraph in response to a prompt, it's dishonest to turn it in as your own creation without disclosing that fact.

Sample Syllabus Language

You are allowed to use ChatGPT to brainstorm and draft ideas in this class. The use of generative AI tools (e.g., ChatGPT, Dall-e, Codex, etc.) is permitted in this course for the following activities: Brainstorming and refining your ideas; Fine-tuning your research questions; Finding information on your topic; Drafting an outline to organize your thoughts; and Checking grammar and style.

The use of generative AI tools is not permitted in this course for the following activities: Impersonating you in classroom contexts, such as by using the tool to compose discussion board prompts assigned to you, reading summaries, or content that you put into a Zoom chat. Using ChatGPT to complete group work that your group has assigned to you is not allowed, unless it is mutually agreed upon that you may utilize the tool. It is not permitted to use ChatGPT to write a full draft of a writing assignment. Writing entire paragraphs or papers to complete class assignments with ChatGPT is not appropriate nor permitted. TurnItIn can be used in this class.

Sample Syllabus Language

Use of ChatGPT and related tools is allowed in this class, but only in ways noted in the assignments. When you use any of these tools, you must include a note describing how you used them with the assignment. Please consult the assignment descriptions to learn more about appropriate use of ChatGPT and other generative AI tools.

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Generative AI in Education | Hilary Ballon Center for Teaching and Learning | NYU Abu Dhabi

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Braeden Ramirez

AI is acceptable to use as a helpful tool as long as it is not used to actually do the work for you. Moral integrity must be used and credit must be given to not cross the line of plagiarism.

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AI is become useful is so many ways. It can help us with home work, how to do thing, or even moms secret enchiladas recipe, but we also have to know how to use it because there is some people that use it for cheating which can get you into a lot of problems. If you get caught cheating in a test after college they can also take away your college degree. There is also a lot of websites from AI such as ChatGPT, DeepAI, AIChatting, some websites are outdated too, such as ChatGPT. But in my opinion AI is good for us since it can help.

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Always push and keep trying and never give up

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Syllabi Polices for Generative AI - Repository

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Syllabi Policies for AI Generative Tools

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Skip navigation links

September 26, 2023

Dear Students,

As you settle into the fall semester, we want to share important reminders and guidance about the use of generative Artificial Intelligence (AI) in your coursework and research. The information below was put together by experts across campus to help you as you assess the use of this technology in your academic studies and research.

Generative AI Reminders and Guidance

Ask before you use AI and check your syllabus and assignment guidelines. Many instructors are providing specific guidance about appropriate use of generative AI in their courses. If you do not know whether your use of generative AI is appropriate to the outcomes in your course or research activities, tell your instructor or supervisor what you are considering and ask for guidance. Do this well in advance of your deadlines, not after you have submitted your work.

Remember that academic and research integrity are a core part of your experience as a student. Disclosing your research methods, using proper citations when incorporating other's work, and contributing original intellectual work as an author are central tenets for ethical scholarly work. As an author, you take full responsibility for your writing and the evidence supporting it, including attribution of sources such as generative AI. It should be used as a resource to facilitate your learning, not a replacement for your education.

Just like personal items, keep your data safe - and beware of scams. Do not provide personal information about yourself or others that is not public, and always follow MSU's data guidance. If you use AI, be sure to use reputable, recognized sources, and never share your credentials with a third party, no matter how legitimate they may seem.
Know the potential risks and limitations of AI outputs. Results might include misinformation, inaccuracies, bias, or inappropriate or unintentionally harmful content.

Throughout the year, we will be engaging in thoughtful conversations with the MSU community and experts about the ethical use of these new technologies in ways that align with the university's mission and values. We will continue to share additional guidance and reminders as those conversations progress. We look forward to learning and using these new technologies in scholarly, equitable, and ethical ways.

Sincerely,

Thomas D. Jeitschko, Ph.D. (he/him)
Interim Provost and Executive Vice President for Academic Affairs

Mark A. Largent, Ph.D. (he/him)
Vice Provost and Dean of Undergraduate Education

Pero G. Dagbovie, Ph.D. (he/him)
Vice Provost for Graduate and Postdoctoral Studies
and Dean of the Graduate School

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Generative Artificial Intelligence (AI) Reminders and Guidance for Students | Michigan State University

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Skip navigation links September 26, 2023 Dear Students, As you settle into the fall semester, we want to share important reminders and guidance about the use of generative Artificial Intelligence (AI) in your coursework and research. The information below was put together by experts across campus to help you as you assess the use of this technology in your academic studies and research.

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Guidance by Role:

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As advancements in Artificial Intelligence (AI) continue to accelerate, Western University recognizes the need for guidance on the ethical and responsible use of generative AI technologies, specifically Large Language Models (LLMs) like GPT-4.

This information aims to provide initial guidance our community, considering the expanding presence of these technologies in various sectors including education, research, and administration. The guidance provided here does not replace or supersede any existing Western policies.

Western trusts the community to innovate and experiment with generative AI responsibly and ethically.

Instructors have autonomy in how AI is integrated into their courses.

Students should act with honesty, ask about uncertainty, and follow course rules.

Researchers must follow the existing policies of funders, publication venues and Western.

Employees must respect Western's existing policies and think carefully about privacy.

What is AI?

Artificial Intelligence (AI) is a huge field encompassing a broad range of theories, technologies, and practices. When someone says 'AI' in 2023 it is more likely than not that they mean generative deep neural networks like GPT-4. We will restrict the scope of our advice to these generative AI tools.

We are accustomed to thinking of software as a tool. Microsoft Excel is like a (very fancy) digital wrench: it does exactly, and only, what you instruct it to do, step-by-step. To get proficient with this type of tool you need to read the manual and invest time in training. Almost all the software that we've worked with up to this point in our lives is of a similar nature.

The most productive mental framework for interacting with current-generation LLM-based chatbots like Bing, ChatGPT, and Claude is to imagine that you are speaking with a very knowledgeable, enthusiastic, and naïve intern. If you have an expectation that software will do only exactly what you tell it, that expectation will be subverted. Likewise, if you have an expectation that software will not offer its own opinions, or occasionally challenge your instructions, you may be surprised!

How should I use AI?

Experiment and share! The reality is that nobody knows how to optimally use generative AI. Each use case for AI is likely specialized to the role, and person, using AI. The more time you spend using this technology, and experimenting with it, the greater the payoff to your productivity. It won't do magic for you "out of the box", but you can accelerate your learning by reading about the successes of others with similar use cases.

What is Western's AI policy?

The creation of policy to govern a single technology is problematic when the technology is new and rapidly evolving, with poorly understood use cases. All of Western's existing policies apply to the use of AI. If you are a student and choose to use AI in a way that violates the rules of a course syllabus, you are violating policy on academic honesty. Rather than rush to create a single-technology policy, Western will continue to rely on our existing policies to govern our community.

Instead, please reflect on the guidance about AI use for instructors, students, researchers, and employees below.

Principles of Using AI at Western:

Experiment responsibly, safely, and ethically. Before considering specific roles and example use-cases, we suggest ethical principles that can help guide our community's engagement with generative AI:

1. Transparency: The algorithms, data, and design decisions underlying AI systems, and the applications of AI systems, should be openly accessible to the extent possible.

2. Accountability: Individuals and teams using generative AI bear the responsibility for the consequences of the AI's actions and decisions.

3. Integrity: The use of generative AI in academic work must be clearly disclosed to preserve the principle of academic honesty.

4. Privacy: Personal data should be adequately protected, and AI should not be used to infringe upon individuals' privacy rights.

5. Inclusion: Accessibility and fairness in AI tools should be actively considered, ensuring they don't perpetuate existing biases.

Guidance by Role:

You have complete autonomy in how, and if, AI is integrated into your course. There are cases where a technology like ChatGPT might become a useful support for the course (for example, it can be a remarkably good tutor), but there are other cases where it is inappropriate.

While the attraction of 'AI detection software' is obviously enormous, the reality is that it is impossible to detect AI-generated content with certainty; this is reflected in the appalling accuracy rates of these 'detectors'.
Students:
You have an obligation to act with honesty and integrity and abide by the rules of the syllabus for each course. You also have an obligation to yourself to learn more about a technology that may have a significant impact on your life. Where you are uncertain, ask your instructor for guidance.
Researchers:
Employing AI in primary research is governed by all the same policies and regulations that govern non-AI-assisted research. Most publishers will not accept AI as a co-author, but many require disclosure of how AI was used in the preparation of the manuscript (and, of course, in the conduct of the research). In the preparation and evaluation of grants, there are some funding agencies (e.g., NIH and CIHR) that have issued direct guidance on permitted use of generative AI while others are relying on existing policies, most importantly the recognition that a Principal Investigator is fully and solely accountable for what they submit.

You must respect all existing policies with special attention to those around privacy and data security. You should not, e.g., submit personal information to an insecure public chatbot like ChatGPT. But where it is appropriate, you should feel empowered to experiment with how these tools can improve your work life. If you aren't sure if a use case is permitted, ask your supervisor or contact caio@uwo.ca.

Our AI journey ahead

Feedback, comments, and questions are welcomed by email to caio@uwo.ca. What you read here is a first step in a long journey. This advice will evolve as the technology, and our understanding of it, grows. Town halls, surveys, and other opportunities to engage in dialogue on this topic are in planning and will be communicated broadly, including on this site.

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Western University, in vibrant London, Ontario, delivers an academic and student experience second to none.

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Generative AI offers the potential for new capabilities in research, education, and productivity. Expectedly, use of tools and services, including OpenAI's ChatGPT, Microsoft's Bing Chat, and Google's Bard, is growing within higher education and across Northwestern University. Understanding what to look for when adopting these tools is key to ensuring the intended use is met while protecting University data.

Background

Generative AI is a general term for artificial intelligence that creates new content based on patterns from the data sets used to train it. These generative models learn patterns, structures, and features from the input data and can create content with similar characteristics. Generative AI tools commonly known today include ChatGPT, Bard, Bing Chat, Starry AI MidJourney, etc. Generative AI is also now embedded in many commercial products that can be integrated with other tools, such as OtterAI, Microsoft Co-Pilot, Zoom IQ, and Fireflies.

Privacy

In most cases, the data you share as part of your queries in generative AI tools will be accessible by others using the same tools. This is because generative AI learns by collecting, analyzing, and storing user-provided information. Therefore, University faculty, staff, students, and affiliates should not enter institutional data into any generative AI tools that have not been validated by the University for appropriate use and have explicit permission of the data provider.

Data Approved for Use with Generative AI

To determine whether your data requires special attention, consult Northwestern's Data Classification Policy. If your data is Level 1 (non-confidential and public data), uploading it to generative AI tools is permissible. To process data above Level 1, any generative AI tool must have been approved through Northwestern IT's procurement and security review processes.

The following table outlines Northwestern's current services posture based on data classification:

Interaction Type Public Data (Level 1)Sensitive/Regulated Data (Level 2, Level 3, Level 4)

Conversational/Interactive Mode

Use of publicly available tools (e.g., ChatGPT, Bing Chat, Bard, MidJourney, etc.)

No solution is currently available at Northwestern, though several products are undergoing review.

Application Programming Interfaces

Use of Northwestern Azure with OpenAI with appropriate security and access controls

Use of Northwestern Azure + OpenAI with security and access controls that meet or exceed regulatory or data protection requirements

At this time, the University has not approved additional applications or services for use at Northwestern with Level 2 and above data; however, several (including Bing Chat Enterprise and Microsoft Co-Pilot) are being evaluated for safety, security, and supportability. This page will be updated as products are approved for use.

Academic Use at Northwestern

Use of generative AI for teaching and learning purposes is governed by the Provost's Committee on Generative AI, in tandem with Northwestern IT. Guidance on generative AI tools and the impact on teaching and learning can be found on the Office of the Provost website.

Research Use at Northwestern

Questions about AI usage pertaining to research applications can be directed to the Northwestern IT Research Computing and Data Services team.

All Other Uses of AI at Northwestern

Questions about AI usage pertaining to information security, privacy considerations, use of AI tools with Level 2 or above data, or evaluation of new third-party AI products can be directed to the Northwestern IT Information Security Office.

Further Considerations for Adoption

Generative AI models are designed to output the most common results possible based on their training data and will invariably tend to suppress less common or marginalized information. Further, these tools can "hallucinate" or make up random data that is not true as they do not have the ability to determine what is true or false.

Revalidation of output is required: Given that generative AI models continue to adjust over time, the output of their responses could change too. It is also possible for generative AI models to "hallucinate," or otherwise use false information in their responses. When deployed where output requires accuracy, these tools should be periodically re-validated to ensure appropriate responses.

Original work and intellectual property: Ensure necessary approvals for use of original works and intellectual property are obtained prior to input into generative AI tools. Examples here could be original works developed by students, research results, or obtained copyrighted material.

Informed consent: Users have the right to know when they are using generative AI tools. This knowledge can encourage informed decisions about whether to engage, share, or rely on the generated content.

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Northwestern Guidance on the Use of Generative AI: Information Technology - Northwestern University

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Generative AI offers the potential for new capabilities in research, education, and productivity. Expectedly, use of tools and services, including OpenAI's ChatGPT, Microsoft's Bing Chat, and Google's Bard, is growing within higher education and across Northwestern University. Understanding what to look for when adopting these tools is key to ensuring the intended use is met while protecting University data.

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Using AI for Teaching, Learning, and Scholarship

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The emergence and proliferation of generative Artificial Intelligence (AI) systems, features, and add-ons provide great opportunities but also raise concerns over security and the unintentional sharing of confidential data. Providing any data to generative AI tools or services as part of a query are equivalent to posting the data on a public-facing website; generative AI systems "learn" by collecting and storing user-provided data. This data may then be used as output provided to others.

As an instructor, you can use AI platforms to assist with some aspects of teaching and course design that do not involve sensitive data, including:

Using ChatGPT to assist in the creation of course materials, assignments, rubrics, etc., while paying particular attention to truthfulness, accuracy, and relevance.

Supporting students through the use of ChatGPT and other AI platforms in educationally appropriate ways.

Submit HIPAA-protected data or research data to any AI system, including patient names and identifiers, health records, or information related to payment for healthcare.

Submit confidential data, sensitive data, or personally identifiable information relating to research projects.

Use Zoom bots (e.g., Otter.ai) to record, transcribe, or summarize Zoom meetings. Your Zoom meeting may inadvertently introduce sensitive or confidential information, and connection to non-University-supported systems may constitute public disclosure.

Exceptions

The University of Miami will review generative AI systems to see if they can be approved and licensed for use with sensitive data. Those that are approved will be added to . Please read the information about each application carefully to understand its purpose, uses, limitations, and restrictions. it.miami.edu/ai-tools

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Using AI for Teaching, Learning, and Scholarship

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The emergence and proliferation of generative Artificial Intelligence (AI) systems, features, and add-ons provide great opportunities but also raise concerns over security and the unintentional sharing of confidential data. Providing any data to generative AI tools or services as part of a query are equivalent to posting the data on a public-facing website; generative AI systems "learn" by collecting and storing user-provided data.

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Syllabus Statements

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Artificial Intelligence Policy: Are all of our classes now AI classes?

A. I expect you to use AI (e.g., ChatGPT, Dall-e-2) in this class. In fact, some assignments will require it. Learning to use AI is an emerging skill, and I will provide basic tutorials about how to leverage it for our work. However, be aware of the limits of these software systems.

B. AI is vulnerable to discrimination because it can inadvertently (or intentionally) perpetuate existing biases present in the data it is trained on. For example, if an AI system is trained on data that contains a bias against a certain group of people, the system may make decisions that are unfair or discriminatory towards that group.

C. There are several reasons why AI systems can perpetuate discrimination:
(i) Bias in the training data: If the training data contains biases, the AI system may learn and replicate those biases in its decision-making.
(ii) Lack of diversity in the training data: If the training data does not include a diverse range of examples, the AI system may not perform well on diverse inputs, which may lead to discrimination.
(iii) Lack of transparency: Some AI systems can be difficult to understand and interpret, making it challenging to detect and correct for biases
(iv) Lack of accountability: Without proper oversight and accountability, it can be difficult to identify and address discrimination in AI systems.
(v) It is important to keep in mind that these biases can be unconscious, unintended and hard to detect, but they can have serious consequences if they are not addressed.

D. AI can be a valuable tool for augmenting human decision-making and critical thinking, but it is not a replacement.

E. AI is a tool, just like a pencil or a computer. However, unlike most tools you need to acknowledge using it. Pay close attention to whatever information you use in your own work that is produced from AI, and explain how/what you used at the end of assignments. My recommendation is to screen shot and save everything (i.e., what prompts you used, what answers were produced, where, why, and how). This is new territory, but basic attribution rules still apply. Cite everything, otherwise you are likely violating academic integrity policies.

F. If you provide minimum effort prompts, you will get low quality results. You will need to refine your prompts to get better outcomes. This will take time and practice.

G. Don't trust anything the systems says. Assume it is wrong, unless you already know the answer and can verify with trusted sources. It works best for topics you deeply understand.

H. Use your best judgement to determine if/where/when to use these tools. They don't always make products easier and/or better.

I. Large language models and chatbots are "look back" machines. They don't advance knowledge (yet). ChatGPT-3 uses data from 2021 and earlier (a lot has changed since 2021).
Note...some of this was written with Ai; OpenAI. (2021). GPT-3 API. Retrieved from https://beta.openai.com/docs/api-reference/introduction

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We encourage all instructors to include a policy in their course syllabus regarding the use (and misuse) of generative AI (e.g., chatGPT) in their course.

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Potential impact of AI on our academic mission

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Dear Members of the Brown Community,

Amid ongoing national conversations across higher education regarding the use of AI tools, I am writing to provide information about University resources related to AI, guidelines for use, and upcoming community discussions.

There is no shortage of public analysis regarding the ways in which the use of generative artificial intelligence (AI) tools (such as ChatGPT) - which are open-access tools that can generate realistic text, computer code, and other content in response to prompts from the user - provides both challenges and opportunities in higher education. Accordingly, exploring the use of AI in ways that align with Brown's values has been a topic of discussion among our senior academic leaders for several months. As we identify the ways in which AI can enhance academic activities for faculty and student success and administrative activities for staff, we must also ensure these tools are understood and used appropriately and ethically.

Below is a list of resources where you can find guidance on the following:
AI Usage Guidance
There is a diversity of practice among faculty regarding AI, and instructors may choose whether or not to allow the use of AI tools and to what extent. While the University is not prescribing specific AI policies, faculty should offer clear, unambiguous information about what is, and is not, allowed in their courses. The Sheridan Center for Teaching and Learning at Brown provides sample syllabus language as well as other tips for classroom integration. Again, links to these and other resources in this message are provided at the bottom of this email.
Academic Integrity
One of the most common concerns about AI is the potential for plagiarism, or students using the system to do work that they then present as something they created without AI assistance. Any unapproved use of AI to complete assignments would be covered by Brown's Academic Code and Academic Code, Graduate Student Edition.
Citation and Attribution
If you choose to use generative AI tools for course assignments, academic work, or other forms of published writing, you should give special attention to how you acknowledge and cite the output of those tools in your work. The Library has compiled general guidelines for citing AI-generated content, as well as more specific guidance from the three major style guides.
Protecting Information
AI tools are often web services that can store, learn from, and possibly re-share the data you share with them. The Office of Information Technology (OIT) provides guidance on how to make sure you are not exposing important or sensitive data to these services.
AI as a Research Tool
The rapid emergence of AI technology offers great potential for researchers as they design, conduct, support, and present their research. However, there are also a number of challenges and risks to understand when using these tools in the research domain. OIT's Center for Computation and Visualization has published a reference guide to understanding and using generative AI as a research tool. The guide also includes a helpful glossary of generative AI terms.
Protecting Intellectual Property
While AI has great potential for university research, Brown investigators contemplating using generative AI tools in their own research should be cognizant of the various intellectual property issues related to its use. The Office of the Vice President for Research (OVPR) has published Initial Guidance for Researchers, which offers important information for researchers about the intersection of generative AI and intellectual property, as well as considerations around public disclosure and patentability.
Next Steps
AI tools will rapidly evolve in the coming months and years, and Brown is well positioned to innovate in this space. Experts from across our campus - from engineering, public health, medicine, the social sciences, the humanities, policy leaders and technologists - have already been incorporating AI into their work. I encourage all of you to experiment with AI to find ways it could be helpful to your academic experience.

Beginning in the fall, the Data Science Institute will partner with the Office of the Provost for "Conversations on AI and our data-driven society," a series of monthly campus-wide discussions about the impact of AI on how we live, work, and educate the next generation The Library has also planned weekly discussions and working groups to address various issues surrounding AI from different perspectives.

I look forward to continued discussions with academic leadership as we monitor the evolution of these tools and discover innovative applications to improve learning outcomes and inform research directions. Please feel free to send additional comments, questions, or concerns to [email protected] .

Sincerely,

Frank Doyle

Provost

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Potential impact of AI on our academic mission

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Dear Members of the Brown Community, Amid ongoing national conversations across higher education regarding the use of AI tools, I am writing to provide information about University resources related to AI, guidelines for use, and upcoming community discussions.

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University of North Carolina at Chapel Hill

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University of North Carolina at Chapel Hill

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Usage Philosophy

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Provost & Chief Academic Officer - UNC Chapel Hill

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Return to Employee Resource page.
To the Instructor:

The Committee has developed the following set of guidelines for instructional applications of generative AI. These guidelines aim to establish a framework for the ethical and responsible employment of AI tools in your teaching.

Please review these recommendations and integrate them into your instructional practices, tailoring them as necessary to suit your specific course requirements. It is also recommended that you inform your students about the intended use of AI in your course on the first day of class.

Given the rapid pace of advancements and instructional applications in generative AI, we anticipate these guidelines to continue to evolve. Thus, if you have any questions or feedback, please do not hesitate to reach out to Dana Riger at driger@unc.edu or your academic unit representative.

Teaching Use Guidelines for Generative Artificial Intelligence

ChatGPT and other generative Artificial Intelligence ("AI") tools that can produce text, images, and other media are now widely available. Integrating AI technology into teaching can enhance the learning experience for our students. AI can be a powerful tool to assist instructors in delivering effective instruction, promoting engagement, and facilitating personalized learning. At the same time, AI can create significant disruptions to learning that yield challenges and opportunities for teaching practices. It is crucial to address classroom and pedagogical challenges responsibly, adjust teaching approaches, and maintain a balance between AI use and human interaction to ensure a holistic educational experience. AI tools are important for instructors to understand and use effectively, appropriately, and ethically. Instructors are responsible for understanding the uses and limitations of AI for both education and the workplace.

Generative AI is limited in the following ways:

1. How output is arrived is not clear as the internal processes used to produce a particular output within the generative AI cannot be determined.

2. The output is based on existing data (often scraped from online sources) and may reflect biases that should be acknowledged; it may also be inaccurate or entirely fabricated, even if it appears reliable or factual.

3. AI evokes a range of intellectual property concerns; sourcing and ownership of information is unclear, and the status of AI output raises numerous questions-e.g., is output equivalent to a published resource? What citational responsibilities are in place for various AI interactions?

The following sections offer philosophies for AI and teaching and specific guidelines for approaching these AI tools and features. Unless your college, school, or department provides other guidelines, you should use these.

Usage Philosophy

Usage of generative AI in your teaching should be based on the following principles:

1. AI should help you teach, not teach for you. AI can serve as a complementary tool to support instructors, but is not a replacement for human interaction. Instructors should continue to play an active role in the teaching process and maintain direct engagement with students. Therefore, make sure you understand how AI tools help achieve learning goals and improve student understanding.

2. Balance quality and timeliness for grading purposes. Take into consideration whether AI is suitable for grading an assignment. If it is deemed appropriate, make sure to verify that the output generated by the AI accurately reflects the actual accomplishments or feedback you would otherwise provide to students. Further, think about the potential discomfort or opposition from students towards AI-driven grading and consider explaining your decision to employ it.

3. You are 100% responsible for your teaching materials. You are responsible for any mistakes made by AI if you choose to incorporate its output into your lectures or other course content. If you are unsure about the accuracy of a statement, it is your responsibility to research and verify it before using it. This includes properly attributing ideas, ensuring the accuracy of facts, and using correct sources.

4. The use of AI should be open and documented. It is essential to be transparent and document the use of AI in your work. Inform your students about the use of AI in generating course assignments, exam questions, and other relevant materials. Provide explanations or demonstrations of how AI is employed, helping students understand the technology's role and limitations.

5. Adjust teaching practices to address AI use concerns. Complement any limits placed on AI use with activities that promote intellectual aims challenged by uncritical AI practices. For example, emphasize in-class generation of knowledge and foreground process-based learning. Consider incorporating oral- or performance-based activities and identify opportunities for reflection and meta-cognition.

6. Select AI tools that align with course objectives. Utilize AI to align teaching strategies with course objectives, making sure that it enhances rather than overshadows the learning process. AI tools should be selected to support students' learning needs. Consider ease of use, accessibility, and the potential for personalized learning. Instructors should ask themselves what they are trying to help students learn and which AI tool best facilitates that learning. The integration of AI should be purposeful and beneficial to the learning process, rather than being incorporated merely for its own sake.

7. Ensure that AI use is inclusive. Make sure that the AI tools and instructions provided are accessible to all students, including those with disabilities or diverse learning preferences. Consider the needs of all students when selecting or designing AI-based instructional materials.

8. Facilitate and encourage critical thinking. While AI can provide valuable insights and assistance, instructors must encourage students to think critically, and question information provided by AI tools. Foster a culture of inquiry and intellectual curiosity and assist students in developing an AI literacy.

9. Emphasize human skills. Highlight the importance of human skills such as empathy, creativity, and critical reasoning alongside the use of AI. Challenge uncritical attempts to ascribe human characteristics to AI. Teach students to leverage AI as a tool while emphasizing the unique qualities that humans bring to the learning process.

10. Specify AI policies for your course. Consider tailoring university or departmental guidelines to your specific course. Any guidelines or limits you specify for student submissions supersede the university guidelines. As different instructors and schools may have different guidelines, remind students regularly of your AI policies and how they can use these tools appropriately in your course.

11. Avoid entering confidential or personal data into AI tools. Putting confidential or personal data (e.g., your students' One Card details,) into these tools exposes you and others to the loss of important information. Therefore, do not do so.

12. Stay informed. Keep up to date with advancements in AI technology and instructional best practices. Stay informed about emerging AI tools, research studies, and ethical guidelines related to AI in education. Engage in professional development opportunities to enhance your AI integration skills.

Guidelines for Specifics Assessments & Instructional Resources:

1. Lecture Enhancement: You can utilize AI-powered tools to create visually engaging presentations, such as interactive graphs, visualizations, or simulations. You can also use AI-based language models to generate real-time examples, case studies, or scenarios to enhance lecture delivery and illustrate concepts effectively.

2. Assessments and Exams: While AI tools can quickly generate a large number of questions, it's important to review and modify these questions as needed and to ensure the level of learning being assessed is appropriate. AI can also be used to create simulations or interactive assignments that require students to apply what they have learned to solve real-world problems. These can be generated by instructors for formative or summative assessments or by students for self-assessment.

3. Plagiarism Detection: Exercise caution while utilizing AI plagiarism detection tools, as their accuracy is not guaranteed and there may be instances where they fail to detect plagiarism. Additionally, be aware that individuals could potentially exploit AI technologies to circumvent detection software. Vigilance and mindful use of these tools is recommended.

4. Student Support: You can integrate AI chatbots or virtual assistants to provide timely and automated responses to common student queries, freeing up instructor time. You can also use AI systems to suggest supplementary resources or personalized study plans based on individual student performance.

5. Critical Thinking: Provide guidance to help students identify biases and misinformation associated with AI. Develop activities that prompt students to participate in iterative, inquiry-based thinking while using AI tools.

6. Student Correspondence. Instructors should consider incorporating language into their emails or email signatures to disclose the use of AI-generated text when communicating with students. For example, "The content of this email might include AI-generated responses; however, they have been examined and confirmed by the sender."

Sourcing Use of AI:

1. Ethical sourcing: When using AI tools, ensure that the technology and data sources used are ethically obtained and comply with privacy and security regulations. Avoid using AI tools that may compromise student privacy or rely on biased or discriminatory data.

2. Reliability: Before implementing an AI tool, thoroughly evaluate its reliability and accuracy. Consider factors such as the technology's track record, user reviews, and endorsements from reputable sources. Pilot test AI tools before incorporating them into instructional practices.

3. Documentation: Include a general statement in the syllabus acknowledging the use of AI in the creation of certain course materials. This statement serves to inform students about the integration of AI technologies and their impact on the learning experience. Additionally, it is recommended that instructors address the importance of following student use documentation guidelines.

Return to Employee Resource page.

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Teaching Generative AI Guidance - Provost & Chief Academic Officer - UNC Chapel Hill

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Return to Employee Resource page. The Committee has developed the following set of guidelines for instructional applications of generative AI. These guidelines aim to establish a framework for the ethical and responsible employment of AI tools in your teaching. Please review these recommendations and integrate them into your instructional practices, tailoring them as necessary to suit your specific course requirements.

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i. All users of AI applications within the University must adhere to ethical principles, respecting human rights, privacy, and data protection.

ii. The use of AI should align with the University's mission, values, and academic goals, supporting teaching, learning, research, and administrative functions.

iii. Approaches involving the use of AI systems should not discriminate against individuals or groups based on characteristics protected by law, such as race, gender, ethnicity, religion, disability, or socioeconomic status.

iv. Staff and students will not be permitted to present work completed by AI applications as their own without acknowledgement.

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Policy on the Use of Artificial Intelligence (AI) in Student Assessment

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Swansea University Policy on the Use of Artificial Intelligence (AI) in Student Assessment

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Building AI policies into your syllabus

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No matter your position on the use of so-called AI in the classroom, I recommend that you consider first your course goals and whether and how any given technology might undermine or support your students in the process of meeting those goals. In full disclosure, I take a "critical AI literacy" (Bali 2023) approach to the media-generative tools now commonly called "generative AI": that is, we need to understand these tools in order to know when and when not to use them, and we need to consider them in the larger context out of which they emerge, including the harms associated with them. My approach below is informed by my " Blueprint for an AI Bill of Rights for Education". Just as a reminder, Sean Kamperman and I put together some curated resources on the topic of critical AI literacy for our AI & Digital Literacy Summit in June, including a number of tools (NB: assume no particular judgment on our part on the inclusion or exclusion of any given tool).

For your reference and to give you a sense of a range of possible individual classroom policy responses to generative AI (the cluster of tools including ChatGPT and other LLMs like Bing, Bard, and Claude), Lance Eaton has curated a collection of " Classroom Policies for AI Generative Tools." (Note that many of these were crafted in early spring 2023 before the launch of other models and before the incorporation of generative AI options into Google Docs and Grammarly [known as GrammarlyGo, and currently only available with the paid model]).

Below is a short draft syllabus policy that you are welcome to adapt and/or adopt; it is not meant to be prescriptive. There may well be uses for AI tools in your course, and if you can imagine ways that students might productively use them, I recommend being very clear about where and why students might be allowed to use them and how you want students to cite their usage. (Note: there is not yet an agreed-upon standard for citing large language models; the APA has suggested a model, but it has been criticized for treating LLMs as equivalent to authors-problematic for a number of reasons including that they synthesize the work of human authors without citation and that their outputs are not repeatable.)

I have also included the draft of my longer policy for the class in which I will be teaching about generative AI; this includes language about citing use (and distinguishes between researching and analyzing generative AI and using it to complete writing assignments). If any of it is useful for you, feel free to use it. I am also happy to discuss anyone else's draft policies if you would find that useful.

Sample generative AI policy

Academic integrity is essential for a fair evaluation of your work and that of your classmates, as KU's policies on academic misconduct make clear. All work submitted in this course must be your own.

Assignments for this course are designed to help you develop your critical thinking, close reading, research, and writing skills. Submitting other people's work as your own, repurposing work you have done for other courses without my prior approval and without substantial revision, or using so-called AI tools for your work in this course undermines those goals. In this course, you may not use ChatGPT or other generative AI software for your assignments, including plug-ins or native tools that automatically generate text within word-processing apps. Exceptions include the grammar check and spellcheck options on your word-processing software or Grammarly grammar check (but not GrammarlyGO's generative text option); these approved tools make suggestions for revising work that you have already generated and may be used with caution and with the understanding that you are, as always, ultimately responsible for the work you generate. If you have questions about what constitutes academic misconduct (plagiarism or unauthorized use of tools or assistance), please ask me before submitting your work.

Draft, full academic integrity policy for my HNRS 190 on generative AI

This course is about so-called generative AI and aims to build literacy in concepts related to those tools, including understanding the ethical and social implications of this technology and what might be appropriate uses for it. It is likely that you will choose to research and analyze generative AI outputs as part of your final project; if you do so, as part of that assignment, you will disclose which tools you have used and explain how you have used them. You should keep a complete, accurate record of your prompts and the model's responses; the name and, where available, version numbers of the model (e.g., Chat GPT 4, Midjourney 5.2, NightCafe SDXL 0.9, Bing Chat Creative Mode); and the dates of your interactions (which will, among other things, help us reconstruct versions for those models that don't provide them, like Bing; but will also provide a more nuanced sense of models that are fine-tuned between version releases). Keeping detailed records is good practice for any course in which the use of generative AI is approved for any portion of an assignment.

Analyzing generative AI tools and their outputs as part of an assignment is not the same as using them to complete your assignments. At this stage in your educational career, I aim to help you develop your critical thinking and research skills, no matter what your chosen disciplines/field(s) of study might be. I am also interested in helping you develop your ability to communicate your thoughts and opinions. For these learning goals in this course, using (rather than analyzing) generative AI in your assignments will not help you gain the competencies you need, and indeed are likely to undermine them. With this in mind, for writing assignments for this course, you are allowed to use built-in spellcheck, grammar check, or Grammarly (without the generative AI feature) after you have already brainstormed and drafted your work. You should not, however, use any separate, built-in, or plug-in tool or feature that generates text (for example, Wordtune, Google Docs "Help me write," GrammarlyGO generative text, ChatGPT, Claude, Bing, Bard) during the idea-generation or drafting stage of the writing process. Ultimately, remember that you are responsible for the writing and work you share, no matter what course you are in or platform you use to communicate, so even if you are using approved tools, check your work before submitting it.

Unauthorized use of AI tools in generating work for this course and attempting to pass it off as your own work constitutes plagiarism; but plagiarism is a broader issue and is not dependent upon AI. Some specific examples of actions that constitute plagiarism include pasting together uncredited information or ideas from the internet or published sources; copying the wording of another source without quotation marks--even a few words--to indicate that the language is not your own (even if the source itself is cited); submitting an assignment written by someone else; submitting a paper written for another class without my approval and without substantial revision; and copying another student's work (even with the student's permission). In order to avoid unintentional plagiarism and to represent your work honestly, you will need to be meticulous about giving credit to any and all sources, whether directly quoted or paraphrased.

Plagiarism hurts you as much as it does anyone. If you plagiarize work, you will not be receiving the practice and needed feedback to improve your own critical thinking, research, and communication skills. Plagiarism will generally result in a 0 for the assignment in which the plagiarism was found; I will notify you and make clear my concerns, and you are always welcome to discuss them with me. All confirmed incidents of plagiarism will be penalized, reported, and kept on file in the English Department, the College of Liberal Arts and Sciences, and the University Provost's Office, which means that the consequences of a second offense are higher. If you have questions, please ask me before submitting your assignment. Asking questions in advance of submission about plagiarism or academic misconduct will not be held against you in any way.

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No matter your position on the use of so-called AI in the classroom, I recommend that you consider first your course goals and whether and how any given technology might undermine or support your students in the process of meeting those goals.

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Discussions of AI and ChatGPT in the Legal Academy

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Scribbr has compiled a list on university policies on AI writing tools. To broadly summarize their findings, the majority of schools leave the question of AI tools to individual instructors and more than a quarter appear to provide no clear guidance.

For law schools, very few appear to have an official policy. The University of California at Berkeley has been mentioned in the news as one of the first law schools with a policy in place and it reads as follows:

Berkeley AGI Policy: Berkeley Law Policy on the Use of Generative AI Software

Generative AI is software, for example, ChatGPT, that can perform advanced processing of text at skill levels that at least appear similar to a human's. Generative AI software is quickly being adopted in legal practice, and many internet services and ordinary programs will soon include generative AI software. At the same time, Generative AI presents risks to our shared pedagogical mission. For this reason, we adopt the following default rule, which enables some uses of Generative AI but also bans uses of Generative AI that would be plagiaristic if Generative AI's output had been composed by a human author.

The class of generative AI software:

- May be used to perform research in ways similar to search engines such as Google, for correction of grammar, and for other functions attendant to completing an assignment. The software may not be used to compose any part of the submitted assignment.

- May not be used for any purpose in any exam situation.

- Never may be employed for a use that would constitute plagiarism if the generative AI source were a human or organizational author. For discussion of plagiarism, see https://gsi.berkeley.edu/gsi-guide-contents/academic-misconduct-intro/plagiarism/

Instructors have discretion to deviate from the default rule, provided that they do so in writing and with appropriate notice."

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LibGuides: AI and Law Teaching: Current Law School Courses, Guidance and Policy

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LibGuides: AI and Law Teaching: Current Law School Courses, Guidance and Policy

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Assignment and Assessment Redesign

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Office of the Vice Provost for Academic Excellence

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Generative Artificial Intelligence (AI) holds promise in improving higher education, offering both opportunities and challenges for universities. AI-powered tools can provide new learning modes, personalize learning experiences, and enable students to receive tailored support and feedback. Additionally, AI can streamline administrative tasks, enhancing efficiency and potentially reducing operational costs.

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From Academic Affairs: Guidelines on Using Generative AI Tools

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Generative Artificial Intelligence (Generative AI) is a computer-based technology that creates a variety of data, such as pictures, videos, music, or words, which look or sound like they were created by a person. It has been predicted that in the future, there will be a greater number of AI systems designed to facilitate creative cooperation between humans and AI. At the University of Alabama, faculty have expressed interests in exploring and applying generative AI tools (such as ChatGPT, DALL.E, Midjourney, Stable Diffusion, and Codex) to collaborative teaching and learning. This document aims at providing UA faculty with understanding the limitations of these tools, offering guidelines to faculty on using these tools in the classroom as they continue to evolve.

Guidelines on Using Generative AI Tools

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Generative Artificial Intelligence (Generative AI) is a computer-based technology that creates a variety of data, such as pictures, videos, music, or words, which look or sound like they were created by a person. It has been predicted that in the future, there will be a greater number of AI systems designed to facilitate creative cooperation between humans and AI.

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Does UB have a policy about artificial intelligence use?

UB has no universal policy about student use of artificial intelligence. Instructors have the academic freedom to determine what tools students can and cannot use in pursuit of meeting course learning objectives. This includes artificial intelligence tools such as ChatGPT.

What should I tell my students about using artificial intelligence?

Because there is no universal UB policy about artificial intelligence tools, instructors need to give students clear guidance about what is and is not allowed in their course overall and/or on each assessment. In the same way students are told when exams are "open book" or not, they need to be told when AI tools are allowed or not. This should be done orally in class when discussing each and every assignment or exam as well as in your syllabus.

Should I allow students to use artificial intelligence in my class?

Deciding whether to allow students to use artificial intelligence on course assessments can best be determined by evaluating how that use affects fulfillment of the learning objectives. If an AI tool can be used to complete low-level work that students can already do, for example, it can be used a head start to propel students toward higher level thinking. But if use of the AI tool replaces the student thinking and process that you intend to assess, it should be disallowed.

How can I help students understand the rules I set around artificial intelligence?

Instructors should make the rationale for their rules around AI as overt as possible. Students don't always understand how the assessments instructors design for them lead to fulfillment of learning objectives. As the expert, it is up to the instructor to help students identify in what ways using artificial intelligence can help or hinder those learning objectives. The more students understand why a tool is disallowed, the more likely they are to respect that rule.

How can I tell if a student used an AI tool?

Some of the common ways instructors can identify use of AI tools include:

Misalignment of student response with what was taught in class.

Odd errors in content. (When AI makes these mistakes, they are called "hallucinations.")

Formulaic prose at a general level, lacking specific details.

What should I do if I suspect a student has used AI in violation of my guidelines?

The procedure for pursuing this suspicion is the same as in any academic dishonesty case, and instructors should follow the consultative resolution process dictated by the academic integrity policy. There are some additional considerations that may help here, however, including:

Entering your assignment prompt in an AI tool yourself. This will show you the type and tone of output a student would get. Consider doing this three or four times to see the extent of possible outputs.

Running the student assessment through an AI-detection tool. Although there are mixed reviews about these tools (they are only providing a "likelihood" that something was generated by AI), they are a good place to start. Turnitin has an AI-detection tool included in UB's Brightspace package. This is different from, and in addition to, the Turnitin plagiarism-detection tool. You can set this as a default so that student work will run through it. Additionally, there are many other free AI-detection tools you can access to collect multiple reports on the likelihood of AI-generation.

Comparing the student's work to an in-class or previous writing sample.

Overtly discussing both the content of the assessment and the process the student took to create it at the consultative resolution meeting. If the student prepared the work, they should be able to talk to you about how they did it and what it includes. They may even be able to show earlier drafts, time-stamped documents, etc. It is good to be prepared with discussion starters for this conversation.

How do I decide if I should report the case to the Office of Academic Integrity?

Whenever an instructor believes that it is "more likely than not" that academic dishonesty occurred, they are obligated to report it to OAI. This standard of evidence is called "preponderance." Instructors do not need the certainty of "beyond a reasonable doubt." In the case of unauthorized use of AI, preponderance can come in many forms. If you are uncertain about this, you can contact the Office of Academic Integrity for guidance.

What charge and sanction are appropriate for this kind of policy violation?

While unauthorized use of AI on assessments can fall into a number of the violations described in the policy, it is commonly reported to OAI as "falsifying academic materials." This violation includes, "submitting a report, paper, materials, computer data or examination (or any considerable part thereof) prepared by any person or technology (e.g., artificial intelligence) other than the student responsible for the assignment." Since plagiarism implies violating ownership of ideas or language and AI can't have ownership, these cases are typically not processed as plagiarism under UB's policy.

How can I design assessments that are AI-proof?

There is no foolproof way to do this, but there are some steps you can take to prevent unauthorized AI use on assessments:

Move assessments to in-class and proctor well.

Apply Lockdown Browser and Respondus Monitor for remote assessments. This ensures that the student cannot access an AI tool on their test-taking device and allows you to observe if they attempt to use another device.

Include an oral component to your assessments so students can explain what they've done and what they know.

Consider redesigning your assessments. The more you can make them specific to course content, class materials, and student experience, the less helpful AI tools are.

Contact the Office of Curriculum, Assessment and Teaching Transformation (CATT) for more guidance and support on assessment redesign.

Guidance is provided for students on this topic here.

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UB has no universal policy about student use of artificial intelligence. Instructors have the academic freedom to determine what tools students can and cannot use in pursuit of meeting course learning objectives. This includes artificial intelligence tools such as ChatGPT.

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The rise of artificial intelligence (AI) raises a number of important issues for universities like St Mary's.

Whilst recognising that opportunities to enhance learning and teaching may emerge from the use of these new technologies, there are also concerns in the sector about the potential negative impacts of AI on the student learning, and the integrity of the University degree awards.

The Policy ensures that there is greater clarity in terms of the University's 'position' vis à vis artificial intelligence, and how and when AI tools may be used.
Read our AI policy
Guidance for staff and students

Please also read our staff and student guidance on AI:

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Read our AI policy for student - St Mary's University.

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Academic Integrity

Crediting Generative AI in an Assignment

Detecting AI-Generated Content

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For more information on promoting academic integrity in your classroom, view our strategies.

Although the Artificial Intelligence (AI) landscape is evolving rapidly, one clear impact is that this technology is already being integrated into the academic lives of both students and faculty. As a result, all instructors should consider how they will provide clear guidelines concerning the use of generative AI in academic work for every class they teach.

Here are a few things to keep in mind when addressing AI and academic integrity in your course.

Clearly Communicate your Expectations

Students benefit from clearly communicated expectations for their coursework, and will likewise benefit from transparency regarding limits to and expectations for use of generative AI in their course assignments and exams.

To reduce the chance of violations of academic integrity, explicitly communicate your course's generative AI policies by:

Including in your syllabus clear expectations regarding the use of generative AI tools and any differentiation in the usage policy for specific assignments.

Clearly identify in what situations generative AI use is prohibited or permitted.

When generative AI is permitted, faculty should be clear about student expectations in terms of documentation and attribution, as well as what work is expected to be produced by the student themselves, and how the student is expected to validate or verify output from generative AI. See Crediting generative AI in an assignment, below.

Discussing the expectations spelled out in the syllabus during a class session.

Reviewing expectations and communicating how they apply to specific assignments.

Engaging in ongoing conversations about the importance of academic integrity, including the fact that basic academic integrity principles remain important and still apply regardless of the existence of generative AI tools.

Example Course Policy Language for Generative AI Use

Crediting Generative AI in an Assignment

When using material generated from an LLM in course materials or in assignments that students submit, transparency is key, and these instances should be properly referenced. When possible, you may want to select citation guidelines and share them with your students using the style guide appropriate to your discipline. Currently, the APA style guide, MLA style guide, and Chicago Manual of Style include recommendations and examples for citing LLM-generated materials that you can share with students.

For example, consider:

Including an in-text citation with reference to the LLM model, i.e. include text in quotes and reference the author, i.e. (OpenAI, 2023).

Asking students to include an AI-generated source in the methods section of their research paper.

Having students provide the text or prompt they used for the LLM to generate a response and include what LLM model, date, and version they used.

Similarly, if instructors choose to use LLMs to help in the preparation of course materials, such work should be acknowledged and attributed.

If you have specific questions and would like to schedule an individual or group consultation, please contact us.

Detecting AI-Generated Content

Generative AI does provide an increased risk that students may use it to submit work that is not their own. It is tempting to seek technology-based solutions to identify inappropriate use of generative AI tools such as ChatGPT.

Unfortunately, it is unlikely that detection technologies will provide a workable solution to this problem. It can be very difficult to accurately detect AI-generated content. Although some instructors have reported that the tone and feel of AI-generated content is different from the student work they normally receive, in some cases it is almost undetectable from the work produced by students. Detection tools claim to identify work as AI generated, but cannot provide evidence for that claim, and tests have revealed significant margins of error. This raises a substantial risk that students may be wrongly accused of improper use of generative AI.

We currently do not recommend using current automatic detection algorithms for academic integrity violations using generative AI, given their unreliability and current inability to provide definitive evidence of violations. We believe that establishing trusting relationships with students and designing authentic assessments will likely be far more effective than policing students.

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AI & Academic Integrity | Center for Teaching Innovation

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Although the Artificial Intelligence (AI) landscape is evolving rapidly, one clear impact is that this technology is already being integrated into the academic lives of both students and faculty. As a result, all instructors should consider how they will provide clear guidelines concerning the use o…

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Course Policies & Syllabi Statements

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Teaching in the presence of ubiquitous GenAI tools should be considered a dynamic endeavor and necessitates flexibility. Approaches and expectations will vary by instructor, course, assessment, and activity. To ensure transparency, it is critical that expectations are clearly articulated in the syllabus and continually reinforced when assignments are given. Instructors should align their course policies with their college's academic misconduct policies.

Instructors are disciplinary experts responsible for what is taught, appropriate pedagogies, and assessment methodologies. GenAI may influence all three, and as such, instructors need flexibility to allow or disallow the use of GenAI tools. If the latter policy is adopted, instructors should be aware that U-M does not currently support the use of surveillance and plagiarism detection tools as they cannot be reliably counted upon. Further, clear policies have to be formulated regarding punitive measures and how they may be enforced. If GenAI tools are allowed, the instructor should be clear which tools are allowed and in what capacity.

It is also important to point out to students that although AI-generated content may appear reliable and factual, that is not always the case, and that inaccuracies, untruths, and false citations may be missed if one is overly reliant on the tools.

Syllabus Language

GenAI is changing rapidly, and new tools will become available. Course policies therefore need to be provisional and subject to change. Several principles can guide instructors to develop course policies appropriate for their contexts:

1. Protect cognitive dimension of learning: GenAI should enhance, not hinder, learning.

2. Responsibility for content: Students are responsible for all content (ideas, facts, citations), however the work is generated. (Note: Chat GPT can generate untrue, biased, inaccurate, and hallucinatory content.)

3. Require ethical transparency: Students should be transparent about how they use GenAI and adhere to standards of academic integrity.

4. Explain academic misconduct: Instructors should distinguish between allowed and disallowed uses of GenAI, including how, when, and why GenAI may (not) be used.

Course policies might fall into one of three categories:

Specific uses of GenAI are encouraged (generating ideas, editing, translating, outlining)

Specific uses of GenAI are allowed if students clearly distinguish between their original work and GenAI output (highlighting output, tracking changes in GenAI output)

Any use of GenAI constitutes academic misconduct.

Instructors could consider including in the syllabus, the following recommendations from The Sentient Syllabus Project, led by a team of multidisciplinary scholars at leading universities throughout the world:
Referencing and validating. You are taking full responsibility for AI-generated materials as if you had produced them yourself: ideas should be attributed and facts should be true. Openness. We encourage you to use AI tools to explore the field, play with knowledge, and help you study. But you need to be open about this, and document your use. Documentation. A portion of your term grade will evaluate your documentation of AI use throughout the course. By keeping track of your AI use and sharing your experiences, we all gain understanding, identify potential issues in this rapidly changing field, and discover better ways to use the resources for our objectives. Academic Integrity Statement. Academic integrity is our foundation as a community of scholars and learners. It defines the values we personally uphold, and it expresses a shared understanding why we do so. This includes: a commitment to truth; a commitment to personal integrity; and a commitment to certain standards and shared values on which membership in this community is based. Assignment Instruction statement. By submitting an assignment for evaluation:

you assert that it accurately reflects the facts and to do so you need to have verified the facts, especially if they originate from generative AI resources;

you assert that all your sources that go beyond common knowledge are suitably attributed. Common knowledge is what a knowledgeable reader can assess without requiring confirmation from a separate source;

you assert that you have respected all specific requirements of your assigned work, in particular requirements for transparency and documentation of process, or have explained yourself where this was not possible.

If any of these assertions are not true, whether by intent or negligence, you have violated your commitment to truth, and possibly other aspects of academic integrity. This constitutes academic misconduct.
Example Syllabi Statements

Use-permitted

Example #1

Domain: Agnostic
GenAI-use: Permitted with disclosure
Consequences for inappropriate use: Reported to school-based academic misconduct processes
Source: U-M Faculty member Nigel Melville

Any and all use of machines that emulate human capabilities (ChatGPT, Stable Diffusion, DALLE, etc.) to perform assignments or other works in the course should be disclosed (this includes all graded deliverables as well as other course works and activities). In addition, an explanatory appendix is required for each and every unique usage to describe in clear steps how such a machine was used, including which machine, iteration, editing, etc. WARNING: the current state-of-the-art of machine capabilities have two salient features: 1) the quality is such that more work may be required in a machine-assisted mode; 2) it is feasible to discern the presence of "machine fingerprints." Our goal as a community of learners is to explore and understand how these tools may be used to augment human performance. However, violation of the explicit disclosure requirement may subject students to standard Ross processes (for reporting, determining misconduct (if any), and assigning sanctions (as appropriate) as would be employed for any other type of potential Academic Misconduct.

Example #2

Domain: Agnostic
GenAI-use: Permitted with disclosure
Consequences for inappropriate use: Not specified
Source: U-M Faculty member

Learning how to use AI functions such as ChatGPT is important for all of us. Used properly, ChatGPT can enhance our work; used improperly, it can border on plagiarism. If you have used ChatGPT on anything you submit for [CLASS NAME], please include an explanation as to (1) what was your original prompt to the chatbot; (2) what are some examples of incorrect data that the chatbot provided to you; and, (3) how did you rework and revise so that your final document was both factually accurate and reflected your writing voice and style.

Example #3

In principle you may submit AI-generated code, or code that is based on or derived from AI-generated code, as long as this use is properly documented in the comments: you need to include the prompt and the significant parts of the response. AI tools may help you avoid syntax errors, but there is no guarantee that the generated code is correct. It is your responsibility to identify errors in program logic through comprehensive, documented testing. Moreover, generated code, even if syntactically correct, may have significant scope for improvement, in particular regarding separation of concerns and avoiding repetitions. The submission itself should meet our standards of attribution and validation.

Example #4

In principle you may submit material that contains AI-generated content, or is based on or derived from it, as long as this use is properly documented. This includes for example drafting an outline, preparing individual sections, combining elements and removing redundant parts, and compiling and annotating references. Your documentation should make the process transparent - the submission itself should meet our standards of attribution and validation.

Example #5

ChatGPT and similar technologies are rapidly becoming part of our professional lives. As such, I expect that you will incorporate these technologies into your work in this class as appropriate, and will treat the work you produce as demonstration of your abilities to engage with these new tools. We do ask that you cite the technologies used as part of your submission so that we're all engaging in a dialogue around the role and efficacy of these tools.

Use permitted depending on activity type:

Example #1

The use of generative AI tools (e.g. ChatGPT, Dall-e, etc.) is permitted in this course for the following activities:

Brainstorming and refining your ideas;

Fine tuning your research questions;

Finding information on your topic;

Drafting an outline to organize your thoughts; and

Checking grammar and style.

The use of generative AI tools is not permitted in this course for the following activities:

Impersonating you in classroom contexts, such as by using the tool to compose discussion board prompts assigned to you or content that you put into a Zoom chat.

Completing group work that your group has assigned to you, unless it is mutually agreed upon that you may utilize the tool.

Writing a draft of a writing assignment.

Writing entire sentences, paragraphs or papers to complete class assignments.

Use-forbidden:

Example #1

Domain: Agnostic
GenAI-use: Forbidden
Consequences for inappropriate use: Reported to school-based academic misconduct processes

The use of AI tools is explicitly forbidden. Any indication of use will be reported to [School/College] name for investigation as academic misconduct, and subject to consequences like failing the assignment or failing the course depending on the scope and severity of the actions taken.

Example #2

Domain: Agnostic
GenAI-use: Forbidden
Consequences for inappropriate use: Failed assignment

ChatGPT and other similar technologies are advancing rapidly and there are many instances where they will be key tools in your schoolwork and career. For the purposes of this class, we are asking all students to pledge that they will not use these technologies. We believe this is key for this learning environment because we want you to learn how to critically engage with the material we'll be discussing, including learning how to search for and identify relevant sources, synthesize these materials, and make recommendations without the aid of technology. Artificial Intelligence cannot do this learning for you. Students who are found to have used ChatGPT or the like to complete their assignments will receive a grade of zero for that assignment.

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Teaching in the presence of ubiquitous GenAI tools should be considered a dynamic endeavor and necessitates flexibility. Approaches and expectations will vary by instructor, course, assessment, and activity. To ensure transparency, it is critical that expectations are clearly articulated in the syllabus and continually reinforced when assignments are given.

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Acceptable uses

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Overview

Since 2020, there has been an exponential increase in the investment and development of generative artificial intelligence (AI) services. Generative AI is a type of artificial intelligence system that generates new text, images, or other media in response to prompts. Notable generative AI systems include ChatGPT, Microsoft Copilot, and Google Bard.

Precautions

Generative AI has potential applications across a wide range of industries, including art, writing, and software development. However, there are also concerns about the potential misuse of these tools and any data shared with the services. When you provide information to these tools, such as queries, student essays, grant proposals, source code, or datasets, it is the same as posting the information on a public website.

Indiana University encourages its affiliates to experiment with using these generative AI services, as long as no institutional data is submitted to them. To date, no generative AI tools have been approved for data beyond Public classification, and these have not been through the Software and Services Selection Process (SSSP).

Prior to the sharing of any institutional data, these services will need to go through review to ensure the necessary contracts and safeguards are in place to protect the data submitted and to ensure the algorithms in use are ethical, transparent, and beneficial to the IU community.

Institutional data that should not be submitted to generative AI tools, even when anonymized, include:

Data classified as University-Internal or higher (for examples, visit the Data Classification Matrix)

Any data that may be considered student, faculty, or staff intellectual property, unless the individual submitting that intellectual property created it

Specific examples that are not appropriate for the public versions of generative AI tools include:

Sharing names and information about a real student, employee, research participant, or patient

Asking an AI service to summarize and grade a student paper or assignment

Sharing employee-related data such as performance or benefit information for communication drafting or analysis

Asking an AI service to generate code for IU systems protecting institutional data or sharing IU source code for editing

Sharing grant proposals still under review

Acceptable uses

With these precautions in mind, there are numerous ways to use generative AI tools without submitting university data or intellectual property. Using general queries to generate content to pull information from the AI resources is a good way to engage with the products.

Students should use generative AI in ways that align with university academic integrity policies and communicate with their instructors before using generative AI in their coursework. Schools and departments may elect to further restrict generative AI.

From a data management perspective, examples of acceptable uses of generative AI include:

Syllabus and lesson planning: Instructors can use generative AI to help outline course syllabi and lesson plans, getting suggestions for learning objectives, teaching strategies, and assessment methods. Course materials that the instructor has authored (such as course notes) may be submitted by the instructor.

Correspondence when no student or employee information is provided: Students, faculty, or staff may use fake information (such as an invented name for the recipient of an email message) to generate drafts of correspondence using AI tools, as long as they are using general queries and do not include institutional data.

Professional development and training presentations: Faculty and staff can use AI to draft materials for potential professional development opportunities, including workshops, conferences, and online courses related to their field.

Event planning: AI can assist in drafting event plans, including suggesting themes, activities, timelines, and checklists.

Reviewing publicly accessible content: AI can help you draft a review, analyze publicly accessible content (for example, proposals, papers and articles) to aid in drafting summaries, or pull together ideas.

Even if you use generative AI tools for activities that do not share personal or institutional data, you should still check the tool's output for accuracy. Since these tools have been known to produce inaccurate content (sometimes called "hallucinations"), verify any factual information generated by an AI tool, and make sure to reference the tool as you would any other source.

Learn more and get help

If you have any privacy-related concerns about generative AI tools, or questions about the type of data that can be shared with it, email privacy@iu.edu. For details about specific AI products, see:

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Acceptable uses of generative AI services at IU

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encourages instructors to exercise their own judgment about student use of these technologies within their courses

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So, absent any articulated standard for the use of AI in a specific discipline and recognizing that usage expectations will vary across the university, it will be essential for instructors to develop clear policies on the use of AI tools in their classroom.

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Brief Overview

AI, specifically Generative AI (GenAI), is altering the way that humans access information, learn, and work. The opportunities and challenges offered by these alterations are compelling educational institutions to reconsider their curricula and expand into new fields of inquiry. But, while these potential updates and expansions are explored, instructors and students will need a more immediate plan for navigating ever-improving AI tools in the context of their courses. As Danielle Aming (2023) suggests, students will be looking to their instructors to provide clarity and direction:

On the outside, it may seem as if the faculty member may have a lesser role with the introduction of AI but this is quite the opposite. With the introduction to AI, faculty members become a more centralized part of the learning experience. Faculty members will have the opportunity to provide a more in-depth explanation to students. By providing students with how the outcomes of AI impact their learning, faculty members are able to have a more holistic discussion regarding how students are progressing and how they can continue to move forward with this information.

But how can instructors best inform themselves and their students about GenAI? What policies need to be in place to support students in developing the knowledge, skills, and behaviors they need to successfully navigate both their learning in the course and their learning around GenAI? How can instructors best communicate those policies in a way that preserves trust, encourages inquiry, and allows for flexibility as AI tools develop and become more deeply embedded in daily life?

Information Gathering

The decision to incorporate or discourage the use of GenAI in a course will depend upon several factors: how AI is currently used in the related discipline, whether its use is required by accreditors, and/or whether instructors determine it can support or enhance teaching and/or learning activities. Relative to the ways GenAI might improve teaching and learning experiences, the WMUx Teaching and Learning team suggests the following:

1. Subscribe to and read the archived and current issues of Dr. Ethan Mollick's Substack newsletter, "One Useful Thing." An associate professor in the Wharton School at the University of Pennsylvania, Dr. Mollick provides a comprehensive and accessible commentary on the specific impacts of GenAI on higher education. He is also among the most cited academics on the subject.

2. Explore WMUx's AI @ WMU Resources. Each resource aims to offer insight into current research as well as information on how educational and other institutions across the globe are addressing AI. Resources are updated as new information becomes available, offering up-to-date, relevant, and actionable information to support the development of AI literacy across campus.

3. Connect with the WMUx Instructional Design and Development team. Instructional Designers are available to explore questions, concerns, and ideas about how GenAI or other AI tools might impact or be incorporated in the classroom. The team can serve as a thought partner in the development of AI policies or a collaborator on the design of student-centered activities that support the development of AI literacy and achievement of course goals. The team can also help instructors identify ways to leverage GenAI in the design, development, and management of courses.

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Usage Considerations

As AI tools improve and are more readily available, experimentation with GenAI is increasing. And, with that increased use, disagreements about whether and how these tools should be used, by whom and in what context, continue to grow. So, absent any articulated standard for the use of AI in a specific discipline and recognizing that usage expectations will vary across the university, it will be essential for instructors to develop clear policies on the use of AI tools in their classroom. To that end, it will be beneficial to consider the following questions.

1. How might bias and security factor into the use of GenAI in the classroom? First, because GenAI programs are trained on vast amounts of information drawn from the Internet, they reflect the biases and cultural assumptions present in Western society. But, because some tools are also trained by user interactions, it is also important to understand that any personal or confidential information entered into GenAI programs may become part of its body of knowledge. While the information is disaggregated, users should still use caution and think critically about information entered or extrapolated during interactions with GenAI.

2. How might access to and understanding of AI impact students? If the use of any AI tool is to be permitted, it will be important to ensure that all students have access to the tool and understand the challenges and opportunities inherent to its use. If use of a given tool is not permitted, instructors should consider potential disadvantages a student may encounter relative to the development of necessary knowledge, skills, and behaviors within a given discipline, but also as current students and future participants in their chosen fields.

3. How do current academic integrity policies and expectations relate to the use of AI? Concerns about academic and professional integrity are most often highlighted by university instructors in discussion of the rapid developments in GenAI. There is, however, disagreement about whether and/or how the use of AI may or may not constitute cheating. Likewise, as plagiarism detectors continue to prove ineffective in consistently or correctly identifying text produced using GenAI, many are asking how universities should approach emerging generative technologies. To that end, the need to clearly articulate a course-by-course policy around AI is essential. Approaches to articulating that policy can be found in WMUx's article, AI in the Syllabus.

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Policy Communication

As policies and the approach instructors take in employing or addressing the use of AI in their classrooms will vary across campus, it will be essential for all instructors to clarify their expectations around the use of AI tools in their courses. But it may also be beneficial for instructors to spend some time engaging students in the examination of the policy, encouraging students to consider their own perspectives on the use of AI in a given course, as well as in their fields of study and, in general, as learners, future professionals, and humans. Given the development of AI tools is ongoing, it will be important to also revisit these policies over time. Following are some ways instructors might ensure their AI policies are clearly communicated to students.

1. Specify the AI policy in the syllabus. Many argue that university policies around academic integrity sufficiently address the use of AI but given that expectations for the use of AI will vary by department and course, it will be important, at the least, to clarify whether or not the use of AI is permitted in the course, and, if permitted how. Given the ever-changing capacity of AI tools, building room for flexibility in your policy may be necessary. Explaining in detail why you have established your policies, particularly if dictated by an accrediting body or professional organization relative to their field of study, may also be beneficial. In essence, explaining to students what constitutes an appropriate or acceptable use of AI in a given course vs. an inappropriate or unacceptable use of AI is essential.

2. Hold intentional conversation(s) about AI with students. Whether in person or online, reflecting on and encouraging students to reflect on expectations, perceptions, and/or experiences around the usage of GenAI in various contexts can support the development of AI literacy as well as critical thinking skills. Asking what is known about how GenAI or other tools can also be helpful to address possible misconceptions and/or identify potential areas for exploration as a class. Likewise, engaging students in conversations about how they see the use of AI in relation to academic and professional integrity can ensure a common understanding of what is or is not acceptable in the course according to a set policy.

3. Help students navigate the variability of AI usage across campus. Be intentional about sharing with students that "acceptable use" of AI in one class may be considered "unacceptable" and potentially violate the University's code of conduct expectations in another. It is, therefore, very important for students to be aware that they need to have a conversation with each instructor before using AI for coursework.

Given that most students take multiple courses in a semester, they will be responsible for learning multiple AI policies. This is why it is crucial for every instructor to make their policies known; it cannot be assumed that students will know what is acceptable or unacceptable regarding the use of AI in a given course if they are not explicitly told.
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References

Danielle Aming, "AI and the Student Experience: How Faculty Can Help," 2023.

WMUx
Western Michigan University
Kalamazoo MI 49008-5230 USA
(269) 387-4200

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Establishing AI Policies for Your Classroom

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AI, specifically Generative AI (GenAI), is altering the way that humans access information, learn, and work. The opportunities and challenges offered by these alterations are compelling educational institutions to reconsider their curricula and expand into new fields of inquiry.

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Course policy examples

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Acceptable use-case examples

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Your syllabus is an essential document for conveying expectations to your students. You likely already have policies around late work, attendance, and other aspects of your course, so it is highly recommended that you think about how you might incorporate a policy related to A.I. use into your existing course structure. There's no one-size-fits-all approach here since courses differ vastly from one another and there is wide variety in how instructors are incorporating A.I. into their courses.

Before we present some specific examples, here are a few things for you to consider:

Having an A.I. policy is important. Some instructors allow any use of A.I. in completion of coursework while others prohibit it entirely. It is therefore very important for you to clearly convey when and how you allow use of A.I. in your courses, if at all. Otherwise, students may assume that the rules from their other courses also apply in yours, which can lead to inadvertent violations of academic integrity.

You may have different policies based on assignment type. You likely already have different sets of expectations across the assessments in your course. For example, textbook use is often encouraged on homework but prohibited on exams. You may end up with similarly distinct policies regarding A.I. use. For example, it might be okay to use it for the brainstorming phase of a project but not production of the final work. Maybe it could be useful for helping improve essays but prohibited on quizzes and exams. If this is the case, be very clear about when and how you allow A.I. use.

Explain the 'why' behind your policy. Generative A.I. is quickly becoming a commonplace feature in our world. It is already being used in workplaces to speed up tasks, brainstorm ideas, organize concepts, and so many other things. Be clear about the value you place on your assignments in terms of learning value and explain why you place limits on A.I. use in terms of how it would negatively impact learning.

Give guidance for citation of A.I. If you allow students to use A.I., you may want to ask them for formal citations of any text that was generated by A.I. as well as asking them to explicitly state how they used A.I. for help. Guidance is becoming available for citation of A.I. using formal styles like APA and MLA.

Ask for help. If you're not sure how to get started or want to discuss your policy with someone, instructional designers at the CTT are available for consultation on this or any other teaching challenge you may be facing.

A.I. Syllabus Policy Examples

As noted above, there are a wide variety of different approaches you can take to developing a syllabus policy around A.I. Consider the different types of assignments you have in your course, how they would be impacted by student use of A.I., and what reasonable limits you can put in place. Example policies tend to fall into 3 main categories, as outlined below. This page includes some examples of each type, but you can find many more on this page of crowdsourced A.I. policies.

Unrestricted use.

These policies allow students to use A.I. in any way they'd like to complete coursework. These policies tend to include warning language about the likely pitfalls of turning in A.I. generated work without making any revisions.

The use of A.I. tools like Chat GPT is neither encouraged nor prohibited on assignments for this course. If you choose to use Chat GPT for assignments, please be sure to revise the content for clarity, conciseness, and audience awareness. Chat GPT is simply a tool and should not be used as a way to produce first and only drafts. Every assignment submission will be graded using the rubric provided in the syllabus. Be aware that Chat GPT may not develop high-quality work that earns a passing grade. It is your responsibility to review and revise all work before submitting to the instructor.

Within this class, you are welcome to use foundation models (ChatGPT, GPT, DALL-E, Stable Diffusion, Midjourney, GitHub Copilot, and anything after) in a totally unrestricted fashion, for any purpose, at no penalty. However, you should note that all large language models still have a tendency to make up incorrect facts and fake citations, code generation models have a tendency to produce inaccurate outputs, and image generation models can occasionally come up with highly offensive products. You will be responsible for any inaccurate, biased, offensive, or otherwise unethical content you submit regardless of whether it originally comes from you or an A.I. model. If you use an A.I. tool, its contribution must be acknowledged in the work you hand in, and you will be penalized for using A.I. without proper citation. Having said all these disclaimers, the use of foundation models is encouraged, as it may make it possible for you to submit assignments with higher quality, in less time. The university's policy on plagiarism still applies to any uncited or improperly cited use of work by other human beings, or submission of work by other human beings as your own.

Use allowed under specific circumstances.

These policies allow students to use A.I. in some situations but not others. For example, it may be allowed in particular phases of a project but not others. In other courses, it might be allowed on some types of assignments but not all of them. If you choose to apply this type of policy, be sure that you cover all types of assignments in your course so your students know exactly what they can use when.

The use of A.I. tools such as ChatGPT is allowed in this course under some circumstances. For homework and projects, you must clearly indicate any use of A.I. tools and provide appropriate citations or references for any A.I.-generated content or results produced. This should include full documentation of exactly how the tool was used. A.I. should not replace your individual effort or original work but rather, should be used as supplemental resources to support your own analysis, critical thinking, and problem-solving. For quizzes and exams, use of any external resource (A.I., Google, your textbook, other students, etc.) is strictly prohibited. Any misuse or violation of the policy, including unauthorized or excessive use of A.I., will be considered a breach of academic integrity and subject to disciplinary actions as per the institution's policies and procedures on academic misconduct.

Intellectual honesty is vital to an academic community and for my fair evaluation of your work. All work submitted in this course must be your own, completed in accordance with the University's academic regulations. Use of A.I. tools, including ChatGPT, is permitted in this course, but only to help brainstorm assignments or to revise existing work you have written. Using A.I. to complete assignments on your behalf is considered plagiarism. Also, be aware that the accuracy or quality of A.I. generated content may not meet the standards of this course, even if you only incorporate such content partially and after substantial paraphrasing, modification and/or editing.

The beta release of Dall-E-Mini in July 2022 and ChatGPT in November 2022 are among many tools using artificial intelligence. There is a good possibility that using tools like these are going to become an important skill for careers in the not distant future. For my class, a responsible use of A.I.-based tools in completing coursework or assessments must be done in accordance with the following:

1. You must clearly identify the use of A.I.-based tools in your work. Any work that utilizes A.I.-based tools must be clearly marked as such, including the specific tool(s) used. For example, if you use ChatGPT-3, you must cite "ChatGPT-3. (YYYY, Month DD of query). "Text of your query." Generated using OpenA.I.. https://chat.openai.com/"

2. You must be transparent in how you used the A.I.-based tool, including what work is your original contribution. An A.I. detector such as GPTZero (https://gptzero.me/) may be used to detect A.I.-driven work.

3. You must ensure your use of A.I.-based tools does not violate any copyright or intellectual property laws.

4. You must not use A.I.-based tools to cheat on assessments.

5. You must not use A.I.-based tools to plagiarize without citation.

Some assignments in this course will explicitly state that you may use Artificial Intelligence (A.I.) such as ChatGPT when completing the work. Use of A.I. to produce or help develop content when an assignment does not explicitly allow for it is a form of plagiarism and will be treated as academic misconduct. If you are unsure about whether something may be plagiarism or another form of academic dishonesty, please reach out to me to discuss it as soon as possible.

Acceptable uses:

Using AI to help brainstorm ideas and organize thoughts.

Using AI image generators to create visuals for storyboards.

Using AI to help with grammar and spelling checks.

Using AI to explain confusing concepts in simple language.

Using AI to translate text from one language to another.

Using AI to help diagnose error messages.

Unacceptable uses:

Using AI to generate content for assignments.

Using AI image generators to create visuals for journalism or fact-based reporting, photo journalism or anything you're creating that's represented as real.

Using AI to plagiarize content from other sources.

Using AI to answer exam or quiz questions.

Using AI to automate the completion of assignments.

Other ethical considerations to keep in mind when using AI for college assignments:

Be transparent with your instructor. If you are using AI to help with your assignments, let your instructor know. This will help to avoid any misunderstandings or accusations of plagiarism.

Avoid plagiarism. Using AI-generated content without proper citation could result in academic dishonesty charges.

Use AI responsibly. Do not use AI to cheat or to automate the completion of assignments. Instead, use it to help you learn, understand and improve your academic skills.

Use prohibited entirely

The set of policies put in place an outright ban on any use of generative A.I.. For policies like these, we recommend being very clear with students why you have the policy that you do. Given how easy it is to use A.I. and how difficult it is for instructors to detect, these policies will be difficult to enforce making it particularly important to work on getting student buy in for the learning process.

Since writing, analytical, and critical thinking skills are part of the learning outcomes of this course, all writing assignments should be prepared by the student. Developing strong competencies in this area will prepare you for a competitive workplace. While A.I. is readily available and can be useful in some circumstances, it is essential that you learn basic skills to help you identify what good writing looks like before you'll be able to accurately and ethically employ their use. To better help you meet the learning goals of this course, A.I.-generated submissions are not permitted at all in this course and any use of A.I. tools at any point in your writing process will be treated as plagiarism.

This course aims to provide students with a comprehensive understanding of the subject matter by engaging in critical thinking, problem-solving, and creative exercises. To promote the development of essential skills and encourage active participation, the use of artificial intelligence (A.I.) tools or technologies such as paraphrasing software or ChatGPT is prohibited in this course. Use of any such tools will be considered a violation of the academic integrity policy and disciplinary action will be taken.

Use of an A.I. Generator such as ChatGPT, iA Writer, MidJourney, DALL-E, etc. is explicitly prohibited. The information derived from these tools is based on previously published materials. Therefore, using these tools without proper citation constitutes plagiarism. Additionally, be aware that the information derived from these tools is often inaccurate or incomplete. It's imperative that all work submitted should be your own. Any assignment that is found to have been plagiarized or to have used unauthorized A.I. tools may receive a zero and / or be reported for academic misconduct.

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Key considerations and examples for creating an A.I. policy for your course(s).

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Syllabus Statements

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Examples of possible academic integrity policies that address student use of generative AI tools - Eberly Center - Carnegie Mellon University

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Examples of possible academic integrity policies that address student use of generative AI tools

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Multiple policies related to GenAI

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Boise State University has a number of policies that safeguard institutional data, which university faculty, staff, students, and affiliates must follow. Those using generative AI in their work should consider what data they are using and whether or not such data usage is prohibited by university policy or otherwise generally cautioned against.

Entering data into a generative AI tool or service is like posting that data on a public website. AI tools collect and store data from users as part of their learning process. Any data you enter into an AI tool becomes part of its training data, which it may then share with other users outside the university.

Boise State University does not have enterprise contracts or agreements with any generative AI tool or service provider. No AI tool meets the university's security, privacy, and compliance standards for handling anything besides public data. Therefore, you may not enter internal, sensitive, or restricted data into any generative AI tool or service. Only publicly available information classified as level three data (data that has no legal or other requirement for confidentiality, integrity, or availability under the Freedom of Information Act) may be used in generative AI tools and services. Questions about university data standards and security should be directed to the Chief Information Security Officer at CISO@boisestate.edu or (208) 426-5701.

As with everything you do at the university, you must follow Idaho State Board of Education and University policies when using generative AI tools and services.

General Policies Relevant to AI Use

8000
Information Technology Resource Use
Prohibitions on using university IT resources to intentionally damage, disrupt, or expose IT resources or data to unauthorized access or harm.

8060
Information Privacy and Data Security
Explanation of the need to classify data as part of the university's obligation to protect sensitive information

Data classifications

Requirement for all information to be kept in a manner consistent with appropriate controls, and standards commensurate with its data classification and the protections outlined in UWSA 1031.B, Information Security: Data Protection Standard

Prohibited Use and Relevant Policies

You may not enter any sensitive, restricted or otherwise protected data into any generative AI tool or service. This information includes, but is not limited to:

1. FERPA-protected information, such as:
- Bronco card ID photos
- University Directory data
- University non-directory data such as student ID numbers
- Work produced by students to satisfy course requirements
- Student names and grades
- Student disability-related information

2. Health information protected by HIPPA

3. Information related to employees and their performance

4. Intellectual property not publicly available

5. Material under confidential review, including research papers and funding proposals

6. Information subject to export control

The university is obligated to protect sensitive information to comply with applicable state and federal privacy and security laws and regulations and with university and Idaho State Board of Education policies. Access to protected institutional data must be authorized and managed to protect individual privacy, maintain promised confidentiality, and ensure appropriate access and use.

University Policy 1090 (Intellectual Property)

University Policy 1150 (HIPAA Hybrid Entity Designation)

University Policy 2080 (Equal Access for Students with Disabilities)

University Policy 5120 (Export Control and Controlled Data)

University Policy 7530 (Employee Files)

University Policy 8000 (Information Technology Resource Use)

University Policy 8060 (Information Privacy and Data Security)

You may not upload any data that could be used to help create or carry out malware, spam and phishing campaigns or other cyber scams.

System IT resources may not be used to disseminate unauthorized email messages.
University Policy 8000 (Information Technology Resource Use)

You may not direct AI tools or services to generate or enable content that facilitates sexual harassment, stalking or sexual exploitation or that enables harassment, threats, defamation, hostile environments, stalking or illegal discrimination.

University policy 1060 prohibits discrimination or harassment on the basis of protected class. University Policy 1065 prohibits sexual harassment, stalking, dating violence, and domestic violence. University Policy 1075 prohibits discrimination on the basis of disability.

University Policy 1060 (Non-discrimination and Anti-harassment)

University Policy 1065 (Sexual Harassment, Sexual Misconduct, Dating Violence, Domestic Violence, and Stalking)

University Policy 1075 (Non-discrimination on the Basis of Disability)

You may not use AI tools or services to generate content that helps others break federal, state or local laws; institutional policies, rules or guidelines; or licensing agreements or contracts.

System IT resources may not be used to violate laws, policies or contracts.
University Policy 8000 (Information Technology Resource Use)

You may not use AI tools or services to infringe copyright or other intellectual property rights.

System IT resources may not be used to violate copyright or other intellectual property laws. Entering copyrighted material into a generative AI tool or service may effectively result in the creation of a digital copy, which is a copyright violation. Feeding copyrighted material into a generative AI tool or service could "train" the AI to output works that violate the intellectual property rights of the original creator. In addition, entering research results into a generative AI tool or service could constitute premature disclosure, compromising invention patentability.

University Policy 1130 (Use of Copyrighted Works)

University Policy 1090 (Intellectual Property)

University Policy 8000 (Information Technology Resource Use)

In addition to violating university policies, many of the above uses also violate generative AI providers' policies and terms.

Incident Reporting Policies

Any member of the university community who learns of a potential breach of data protection or confidentiality-including through the use of generative AI-must report the incident.

Trustworthy AI

For uses of generative AI that are not prohibited, university faculty, staff, students, and affiliates can help protect themselves and others by choosing tools and services that exhibit the National Institute of Standards and Technology's (NIST's) characteristics of trustworthy AI.

Additional AI Resources

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Generative Artificial Intelligence (AI) - Guidance on Use and Applicable Policies

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Boise State University has a number of policies that safeguard institutional data, which university faculty,...

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student letter

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Dear students,

You may have received an email this week from the University's Chief Information Officer highlighting the need for caution when using generative AI (such as ChatGPT, etc.) with personal and/or sensitive information and the need to protect confidential data.

Another area of concern is the possibility that inappropriate use of these tools will interfere with your ability to successfully engage with and master course material, given the potential that they create for academic dishonesty. Below is the University's policy regarding the use of generative AI in your courses.

This new technology offers numerous ways to support your education, such as making study guides or flash cards or providing help with understanding difficult concepts.

However, misuse of generative AI impedes the University's mission to develop the gifts and talents that you bring to the ND community, and using it as a substitute for genuine engagement with your coursework runs counter to the heart of education itself. Think carefully about the difference between supplementing your education and replacing it.

1. With this in mind, remember that representing work that you did not produce as your own, including work generated or materially modified by AI, constitutes academic dishonesty. Use of generative AI in a way that violates an instructor's articulated policy, or using it to complete coursework in a way not expressly permitted by the faculty member, will be considered a violation of the Honor Code.

2. Finally, Undergraduate Education at Notre Dame and the Office of Academic Standards would also like to support you as you navigate the impacts of generative AI on your education and career path. If you have concerns about academic dishonesty, or about generative AI impacting learning in your classes, email honor@nd.edu and let us know.

Welcome back to campus, and best wishes for the fall semester.

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Marketing Communications: Web | University of Notre Dame

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Generative AI Policy for Students (August 2023)

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The University of Notre Dame's Academic Code of Honor expresses the shared commitment of undergraduates and faculty to respect and honor the intellectual and creative contributions of each individual.

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University Policy with Principles/Goals

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Policy Ideas for the Use of AI in Higher Education

Artificial Intelligence (AI) is one of the key transformative technologies of the coming century. Its impact on research institutions and universities is already significant today, and is likely to continue growing intensively over the coming years.

For students and scholars, AI can be a powerful tool that enhances their learning experience or research endeavors - but, if used improperly, it can also degrade the quality of a student's education and undermine the rigor and impact of scholarly research. Much depends on the specific pedagogical or intellectual context within which AI tools are being used, and on the ways in which those tools either support or weaken the underlying goals of a given academic endeavor. Since these contexts vary widely, it is the policy of Vanderbilt University to leave it up to individual faculty to decide whether and how AI tools are used in their courses or research work.

Below we include the most recent Guidance from Faculty Affairs, which stresses the importance of each faculty member deciding whether or how to incorporate generative AI into course expectations.

In an effort to assist faculty in making these their own policies, the AIGC has catalogued example policies regarding the academic uses of AI as falling along a broad spectrum that ranges from permissive to restrictive. Here we present examples of such policies that have been adopted by Vanderbilt faculty hailing from a variety of disciplines. We have divided these examples into four basic gradations: permissive; slightly restrictive; moderately restrictive; and completely restricted.

If you would like to submit your own policy to be included, please click here.

University Policy
Vanderbilt University Academic Affairs Guidance for Artificial Intelligence (AI) Tools
POLICY STATEMENT:

Artificial Intelligence is a valuable, dynamic, and vital tool. As with all other tools, you must abide by all applicable laws and policies when you use AI, and you should strive to use AI ethically. You should follow guidelines (including prohibitions) provided by your dean, instructor, supervisor, or other individual(s) overseeing your work. You should disclose the use of AI in an appropriate way. You should exercise sound judgment in your use of AI. When AI is used, it should be used in ways that are consistent with university policies on confidentiality and privacy. This applies to all aspects of research, service work, and creative expression. You are the author of content that you produce with AI and responsible for its accuracy, impact, and compliance with relevant laws and policies.

PRINCIPLES/GOALS:

Faculty should decide whether and how generative AI is used in courses.

Faculty should clearly communicate expectations to students.

Faculty should clearly communicate what constitutes academic dishonesty.

Students are responsible for understanding the rules of engagement for using AI in each of their courses and seeking out information if they do not understand or are unsure how to comply.

Faculty and students are responsible for using AI appropriately and ethically.

Faculty and students should disclose the use of AI in their work if such disclosure is expected.

Faculty and students are the authors of content generated by AI and are responsible for that content as they are with content that they author.

AI should be used in ways that respect confidentiality and privacy.

AI should be used legally, ethically, and reasonably.

Permissive Policies
Scott Crossley, Peabody
Generative AI. You can use generative AI models (ChatGPT, GPT, DALL-E, Stable Diffusion, Midjourney, GitHub Copilot, and anything after) as needed, for any purpose, at no penalty as long as you recognize the model's contribution. Failure to acknowledge the use of AI models will be penalized as plagiarism. However, please note that AI models tend to include incorrect information, fake citations, and inaccurate outputs. You are responsible for any inaccurate, biased, offensive, or otherwise unethical content submitted regardless of whether it originally comes from you or an AI model.

Somewhat limited Policies
David Schlundt, Psychology
The worlds of science and academics are rapidly changing with the introduction of complex-language AI software. This software, like ChatGPT, can be very useful when researching topics. You are encouraged to explore AI tools to find and organize knowledge. I strongly recommend that you avoid pasting AI output into your papers. You will have to backup what you write with citations to studies, literature reviews, and other scholarly works. Helping you become a better writer is one of the most important course goals. You may use AI but write the papers using your own words and only use citations that you have accessed and reviewed for accuracy. All papers will be reviewed for plagiarism using Turnitin software. The software now includes the ability to detect text that was generated using AI. Plagiarism is a violation of the Honor code. Currently, I will treat copying text from AI as a reason for deducting points to further encourage you to write in your own words.
Michael Bess, History
For the purposes of this course, the use of AI tools such as GPT4, Bing, Claude, or Bard falls under two categories:
1. text-generation tool (prohibited);
2. research, brainstorming, and editorial aid (permitted).
Turning to AI text-generation tools can all too easily degrade the quality of a
student's learning experience in thinking through the material for themselves and creating their own work. On the other hand, certain targeted usages of AI tools can powerfully augment the learning experience for students, when conducted in a way that enhances their critical thinking, creativity, and research capabilities. Please read the detailed description of this policy (and the rationale for it) below. Failure to adhere to this policy, or to acknowledge your use of such a tool, constitutes a violation of Vanderbilt's honor code. Feel free to consult me if you have any questions about this.

Unattributed (adapted from Monica Linden, Brown University).
The use of AI has exploded in just this last year. Although many of the assignments in this class will not lend themselves to use AI (I think) because a lot of it is personal reflection, it's possible that it could be used in some instances. So for our purposes, I ask that you report your use, using the following guidelines. There will be no deduction in your grade for the reported use of AI as this is an experimental time. At the bottom of your assignments please write the statement that most closely describes your use. If you have used it extensively, I will call you in for a conversation on a case-by-case basis.

I did not use Generative AI for this assignment.

Generative AI helped with grammar and readability, but the original writing was my own.

Generative AI helped with idea generation, but I wrote the assignment myself.

A small amount of my work (just a couple of sentences) was written by Generative AI.

Generative AI significantly wrote my work, but I edited it.

An example of unacceptable use of AI would be to have it write a song on a particular topic without making significant changes. If you do use it to give you some ideas, you will need to submit a screenshot of the prompt and the song the AI wrote along with your assignment.
If you do use AI in your work, you must properly cite it. Look at these guidelines for how to cite properly in APA style: https://apastyle.apa.org/blog/how-to-cite-chatgpt

I reserve the right to modify the guidelines as needed.
Ole Molvig, History/CSET
In this course, students are allowed limited and intentional use of AI text-generation tools such as ChatGPT, Bing, or Bard in crafting essays or other assignments for this course. (The only exception is for specific assignments in which we explicitly instruct students to use such a tool.)

Care should be taken in the use of AI for education. Turning to AI text-generation tools can all too easily degrade the quality of your own learning experience in thinking through the material for yourself and writing your own essay. Therefore it is not allowed to simply use AI text-generation tools such as ChatGPT, Bing, or Bard merely to replace your own effort. (In other words, you may not simply enter the essay prompt, hit generate, and turn the work in as your own.) AI tools may be employed in the brainstorming, research, and editing of class assignments, but the final, submitted essay must be primarily written by the student author. Text predominantly generated by an AI must be cited as such, as you would a human author. Note, rarely will an AI expert be a preferred citation to a human expert.

In the use an AI text-generation tool, you must include a citation of this tool in your references, indicating in detail the ways in which you used this tool in crafting your assignment. Failure to acknowledge your use of such a tool constitutes a violation of Vanderbilt's honor code. For a full description of this policy (and the rationale for it), please see the statement in the class pack; do feel free to reach out to us if you have any questions about this policy.
Richard Alan Peters II, Electrical and Computer Engineering
Use of generative AI.
You are free to use generative AI algorithms such as Chat-GPT in your work. However, You must:

1. Cite any text that the AI generated (even if you edited it) with a bibliography entry that includes the name and version of the AI model that you used, the date and time it was used, and includes the exact query or prompt that you used to get the results.
2. Cite, as described in rule 1, any code that you had it generate for you.I recommend that you not ask it to write code for you. Doing so will probably be more work than simply writing it yourself. Because:
(a) You must thoroughly test the code to prove that it works.
(b) You must explain what you did to verify that it works.
(c) To demonstrate that you understand it, you must comment every single logical object be it a data structure or line or short block of code that it generates. I.e. exactly what that bit of code does and how it does it.
(d) The code must follow the other rules. For example the assignment may have stated restrictions on methods, procedures, external libraries, or programs.
(e) It must generate the results that are asked for in the assignment instructions.

I hope that by following these rules you will learn how to use generative AI as
an assistant to increase your productivity in writing and coding. If you fail to
follow these rules, that will be an honor code violation and you will be referred
to the Honor Council.

Strongly limited Policies
Unattributed
You may use AI programs e.g. ChatGPT to help generate ideas and brainstorm. However, you should know that the material generated by these programs may be inaccurate, incomplete, or otherwise problematic. You may not submit any work generated by an AI program as your own. If you include material generated by an AI program, it should be cited like any other reference material (with due consideration for the quality of the reference, which may be poor). Any plagiarism or other form of cheating will be dealt with severely under relevant Vanderbilt policies. Of course, there are many cases where I may not be able to tell if you cheated or not. Note however that, for any reason, including doubts about whether the paper was written with external help, any student can be required to come in for an oral examination on their paper. In such a case, a grade for the oral examination will replace that for the paper.
Anna Castillo, Spanish
Policy regarding ChatGPT and other AI tools: The objective of this course is to help you to develop your own voice as a film critic while cultivating your intellectual creativity. The use of generative AI to substantially complete your homework goes against this objective and the honor code. Having said this, learning to use artificial intelligence (AI) language models is an emerging skill, and I can give you advice about using it in a responsible and effective manner. If you have any doubts, ask me. It is important not to outsource your learning in this course. (translated from Spanish)
Ray Friedman, Owen
You are writing about your own personal experiences, which are not in the AI databases. Therefore, chat bots cannot answer the assignments. Chatbots can sometimes help with writing. If you would like to use AI to help your writing, please submit a) your own version (an outline, at least) and b) the AI-enhanced final version. I will assess your final version only but want to also have a clear sense of your initial input. (Careful! AI can also sometimes produce vapid buzz-word-filled sentences that name no sense. It is up to you to decide what words best represent your ideas.)

Restricted Policies
Unattributed
Due to the centrality of the craft of writing in this course, students may use NO AI text-generation tools (such as ChatGPT, Bing, or Bard, or similar) in any way in crafting their essays or other assignments for this course. Turning to AI text-generation tools can all too easily degrade the quality of the student's learning experience in thinking through the material for themselves and creating their own essay. The use of such a tool constitutes a violation of Vanderbilt's honor code. Feel free to reach out to me if you have any questions about this policy.

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Policy Ideas for the Use of AI in Higher Education Artificial Intelligence (AI) is one of the key transformative technologies of the coming century. Its impact on research institutions and universities is already significant today, and is likely to continue growing intensively over the coming years.

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Links to other university's AI policies sites. Hosted by University Library

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LibGuides: Artificial Intelligence in Higher Education: Policies

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Find resources on Artificial Intelligence (AI) in relation to teaching, learning, and higher education

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Artificial Intelligence at the University of Arizona

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Artificial Intelligence at the University of Arizona

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Stay in the know about AI access, equity, integrity and impact, on campus and across higher ed.

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The latest generation of Artificial Intelligence (AI) systems will impact teaching and learning in many ways, presenting both opportunities and challenges for the ways our course instructors and students engage in learning. At the University of Toronto, we remain committed to providing students with transformative learning experiences and to supporting instructors as they adapt their pedagogy in response to this emerging technology.

While many generative AI systems have recently become available, ChatGPT is currently the most prominent, garnering worldwide media attention. This is an AI tool that uses predictive technology to create or revise written products of all kinds, including essays, computer code, lesson plans, poems, reports, and letters. The products that the tool creates are generally of good quality, although they can have inaccuracies. We encourage you to try the system to test its capabilities and limitations.

In this FAQ, ChatGPT refers to the free, online AI chat system that utilizes the OpenAI GPT technology. Please note that this is only one of a variety of generative AI tools currently available.

Sample Syllabus Statements

April 2023: The University has created sample statements for instructors to include in course syllabi and course assignments to help shape the message to students about what AI technology is, or is not, allowed. These statements may be used for both graduate and undergraduate level courses.

SGS Guidelines

July 2023: The School of Graduate Studies (SGS) has announced new Guidance on the Appropriate Use of Generative Artificial Intelligence in Graduate Theses which will be of interest to graduate students, supervisors, supervisory committee members, Graduate Chairs and Graduate Units.

Copyright Considerations

September 2023: There remains significant legal uncertainty with the use of generative AI tools and copyright. This is an evolving area, and our understanding will develop as new policies, regulations, and case law becomes settled. Some of the concerns surrounding generative AI and copyright include:

Input: Concerns as to whether content used to train AI systems infringe copyright or if the copying of the material constitutes fair dealing / fair use.

Output: Are works created by humans using generative AI tools entitled to copyright protection? How do we determine human involvement in the expression of the work?

If you have further questions about copyright, please view the U of T Libraries webpage, Generative AI tools and Copyright Considerations.

If you are an instructor who is interested in in using generative AI to develop course materials, review the FAQ below for considerations.

Frequently Asked Questions

Have feedback or want more information?

If you have any suggestions for teaching and learning resources that would be helpful to you as a course instructor, or if you have any other questions about generative AI at U of T that are not addressed through this FAQ, contact us now:

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ChatGPT and Generative AI in the Classroom

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The latest generation of Artificial Intelligence (AI) systems will impact teaching and learning in many ways, presenting both opportunities and challenges for the ways our course instructors and students engage in learning. At the University of Toronto, we remain committed to providing students with transformative learning experiences and to supporting instructors as they adapt their pedagogy in response to this emerging technology.

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Tufts University (Resources for Faculty & Staff)

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Tufts University (Resources for Faculty & Staff)

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Center for the Enhancement of Learning and Teaching

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When AI tools are able to replicate or mimic some or much of the work we ask our students to do in higher education - say write an essay or a term paper, code, an admissions essay, or the preface to a grant proposal - we need to reflect deeply about our role in educating students and our own professional standards. Important questions need to be addressed globally, but also across higher education as an important agent of change, progress, and democracy.

What does this mean for Tufts' role in graduating an educated citizenry for the world they will enter post-graduation?

What opportunities do these new tools offer us that might enhance our work?

Are there equity concerns or affordances for different students?

What ethical, legal and safety considerations should we be wrestling with vis a vis AI?

How does this change how we think about academic integrity?

What policies for academic integrity do we need to put in place regarding its use that are meaningful and flexible enough to evolve with these tools?

What might be the impact on intellectual property?

What are the privacy issues we need to address to protect our students, faculty and staff?

What other big questions should we be asking ourselves?

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Artificial Intelligence Resources for Tufts Faculty and Staff - Center for the Enhancement of Learning and Teaching

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Notes - This information will continue to evolve and change. Events related to AI can be found on the CELT Events 2023 page and links to helpful resources for instructors can be found at the... Read More

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We are providing initial guidelines on the use and procurement of generative artificial intelligence (AI) tools-such as OpenAI's ChatGPT and Google Bard-that can generate content in response to prompts. We support 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 we will watch developments and incorporate feedback from the community to adjust our approach as needed.
1. Protect confidential data 2. You are responsible for any content that you produce or publish that includes AI-generated material
AI-generated content can be false, misleading, or worse-AI sometimes makes up events and facts. In addition, AI can infringe Intellectual Property rights, and your use of the results can be considered infringement-subjecting you to lawsuits and damages. On the other side of the Intellectual Property coin, AI results are not well-protected by Intellectual Property laws. All of these risks require you to read and understand the terms that come along with the AI you are using.
3. There is almost never "no contract." 4. Adhere to current policies on academic integrity
Review your School's student and faculty handbooks and policies. For example, misuse of ChatGPT already violates our We expect that Schools will be developing and updating their policies as we better understand the implications of using generative AI tools. In the meantime, faculty should be clear with students they're teaching and advising about their policies on permitted uses and required disclosures, if any, of generative AI in classes and on academic work. Students are also encouraged to ask their instructors for clarification about these policies as needed.
5. Connect with TTS before procuring generative AI tools

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Guidelines for Use of Generative AI Tools

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We are providing initial guidelines on the use and procurement of generative artificial intelligence (AI) tools-such as OpenAI's ChatGPT and Google Bard-that can generate content in response to prompts. We support 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.

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...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.

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As AI continues to revolutionize various industries, it is essential for Pace University to embrace this technology responsibly and ethically. AI has tremendous potential to enhance teaching, learning, and administrative processes in higher education.

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Use of ChatGPT, Google Bard and other generative artificial intelligence (AI) tools and services is growing rapidly within higher education, including at UW‍-‍Madison. Although AI offers new and powerful capabilities for research and education, it is clear it also poses a potential risk to institutional data that UW‍-‍Madison is legally and ethically obligated to protect.

University faculty, staff, students and affiliates must not enter institutional data into any generative AI tool or service unless that data is classified as public (low risk). Providing any data to generative AI tools or services as part of a query is equivalent to posting the data on a public-facing website. That is because generative AI "learns" by collecting and storing user-provided data. This data may then be used as output provided to others.

Use of generative AI by UW‍-‍Madison faculty, staff, students and affiliates is subject to UW‍-‍Madison, UW System Administration (UWSA) and Universities of Wisconsin Regent policies. Examples of generative AI use prohibited by these policies include, but are not limited to, the following:

In addition to violating UW policies, some of the above uses may also violate generative AI providers' policies and terms.

Researchers who may need to enter higher risk data into a generative AI tool or service as part of their research programs should consult the policies referenced above. For additional guidance, researchers may contact Chief Technology Officer (CTO) Todd Shechter ( todd.shechter@wisc.edu).

For uses of generative AI that are not prohibited, UW-Madison faculty, staff, students and affiliates can help protect themselves and others by choosing tools and services that exhibit the National Institute of Standards and Technology's (NIST's) characteristics of trustworthy AI.

You can learn more about the benefits and risks of using ChatGPT and other generative AI tools by attending the summer 2023 "Exploring Artificial Intelligence @ UW-Madison" webinar series. Sponsored by the Division of Information Technology (DoIT) and the Data Science Institute, the series aims to provide a platform for experts and visionaries in the field of AI. Speakers will share their insights, research and experiences in the classroom, research lab and wider academic community.

Also instructive is the "Generative AI @ UW-‍Madison: Use & Policies" page.

If you have questions about classifying data, contact the relevant data steward.

Thank you,

Jeffrey Savoy
Chief Information Security Officer
University of Wisconsin-Madison

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Guidance from UW-‍Madison Chief Information Security Officer Jeff Savoy about the use of ChatGPT, Google Bard and other AI tools and services.

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Use of ChatGPT, Google Bard and other generative artificial intelligence (AI) tools and services is growing rapidly within higher education, including at UW‍-‍Madison. Although AI offers new and powerful capabilities for research and education, it is clear it also poses a potential risk to institutional data that UW‍-‍Madison is legally and ethically obligated to protect.

University faculty, staff, students and affiliates must not enter institutional data into any generative AI tool or service unless that data is classified as public (low risk). Providing any data to generative AI tools or services as part of a query is equivalent to posting the data on a public-facing website. That is because generative AI "learns" by collecting and storing user-provided data. This data may then be used as output provided to others.

Use of generative AI by UW‍-‍Madison faculty, staff, students and affiliates is subject to UW‍-‍Madison, UW System Administration (UWSA) and Universities of Wisconsin Regent policies. Examples of generative AI use prohibited by these policies include, but are not limited to, the following:

In addition to violating UW policies, some of the above uses may also violate generative AI providers' policies and terms.

Researchers who may need to enter higher risk data into a generative AI tool or service as part of their research programs should consult the policies referenced above. For additional guidance, researchers may contact Chief Technology Officer (CTO) Todd Shechter ( todd.shechter@wisc.edu).

For uses of generative AI that are not prohibited, UW-Madison faculty, staff, students and affiliates can help protect themselves and others by choosing tools and services that exhibit the National Institute of Standards and Technology's (NIST's) characteristics of trustworthy AI.

You can learn more about the benefits and risks of using ChatGPT and other generative AI tools by attending the summer 2023 "Exploring Artificial Intelligence @ UW-Madison" webinar series. Sponsored by the Division of Information Technology (DoIT) and the Data Science Institute, the series aims to provide a platform for experts and visionaries in the field of AI. Speakers will share their insights, research and experiences in the classroom, research lab and wider academic community.

Also instructive is the "Generative AI @ UW-‍Madison: Use & Policies" page.

If you have questions about classifying data, contact the relevant data steward.

Thank you,

Jeffrey Savoy
Chief Information Security Officer
University of Wisconsin-Madison

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Guidance from UW-‍Madison Chief Information Security Officer Jeff Savoy about the use of ChatGPT, Google Bard and other AI tools and services.

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ChatGPT and other generative AI tools are changing Higher Education, organizations, and society. To help faculty and staff use these tools appropriately and ethically, the UNC AI Committee has developed a set of resources, including guidance, training, and tools.

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Provost & Chief Academic Officer - UNC Chapel Hill

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Generative AI Employee Resources - Provost & Chief Academic Officer - UNC Chapel Hill

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Two PowerPoint slides that faculty can use on the first day of class that summarize the guidance above. This in-depth six-module training series for faculty provides guidance for teaching with generative AI. The modules include the following: Module 1, Course Introduction: An introduction to the course from Provost Chris Clemens Module 2, Generative AI: Provides an overview about generative AI and the AI Ecosystem.

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Citation Guidelines: How and When to Cite

On April 7, 2023, the American Psychological Association (APA) issued guidance on how to cite ChatGPT. Walden has adopted this method of citation.

Use of direct citation of an output from an AI-assisted writing tool should be uncommon. AI Assisted writing tools, like ChatGPT, are not scholarly sources. Think of this similar to how you think of Wikipedia, which is also not a scholarly source and therefore citation from Wikipedia is not encouraged. But, if you have a reason to cite, please follow the APA guidelines below.
APA provides the following guidance for citing an AI-Assisted Writing tool:
Author. (Date). Title (Version) [Description]. Source
Example:
OpenAI. (2023). ChatGPT (May 24 version) [Large language model]. https://chat.openai.com/chat

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Academics: Artificial Intelligence: Guidelines for Using ChatGPT and Other AI Tools in Writing and Research

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Using AI tools responsibly for academic purposes and in the classroom is an emerging competency for both students and faculty.

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Chapman University's AI Hub was created to promote a deep understanding of the importance of AI principles. These principles serve as ethical frameworks, aiding Chapman University's community, which includes researchers, educators, and staff, in the responsible use of AI.

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Promoting Artificial Intelligence information on campus.

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Position on the Use of Generative Artificial Intelligence in Research

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Nanyang Technological University (NTU) recognizes that Generative Artificial Intelligence (GAI) - such as OpenAI's ChatGPT, Microsoft Bing, and Google Bard - provides new tools to support research practices and scholarly activities, including the preparation and drafting of research proposals, manuscripts, and other scholarly work.

NTU acknowledges that GAI has the potential to enhance the quality and efficiency of research, and provide new modes of inquiry. However, it is important to recognize that its use in research can raise ethical, research integrity and other concerns. Researchers must therefore carefully evaluate these issues, and have a thorough understanding of the technology - including its potential risks and limitations (e.g., current AI-generated outputs can contain bias, errors, inaccuracies, or falsehoods) - before considering its use.

Researchers who use AI tools in their scholarly work must use GAI in a responsible and accountable manner , and be transparent on the extent and nature of the involvement of GAI in their work. This would include acknowledging the use of any AI tools in their research proposals, manuscripts, and scholarly works.

Currently, NTU does not impose restrictions on the use of GAI in research, except in the scenarios outlined in points 3 and 4 below.

The Responsible Use of GAI in Research
1. Acknowledging/Declaring the Use of GAI
In the interests of transparency and integrity, the use of GAI of beyond basic spelling and grammar checks should be appropriately acknowledged and cited.

Additionally, any use of GAI to generate images for illustrations or figures should be clearly stated in the caption and/or document, in a manner easily understandable to the reader.
2. Authorship

Authorship requires the acceptance of responsibility for the work described in any manuscript. Researchers should recognise that GAI cannot be held responsible as an author for the accuracy, integrity, and content of such work.

Therefore, any GAI (e.g. ChatGPT) will not be listed as an author of any paper with an affiliation to NTU; or listed as a Principal Investigator (PI), Co-PI, or collaborator in any research proposals.

Authors and/or PIs are fully responsible for the content of their scholarly materials (e.g., research proposals, grant applications, manuscripts for publication) in which GAI was used in the preparation and/or development of such outputs. Researchers must therefore exercise caution and judgement when using GAI, and be ready to verify the accuracy and validity of their work. (See Figure 1 under resources.)
3. Data Privacy & Confidentiality
The use of GAI to process or analyse research data must comply with all relevant data privacy and protection laws, regulations, and institutional policies - e.g., the Personal Data Protection Act (PDPA), NTU's Data Governance Policy.

Any confidential or sensitive information, and/or personal data are not to be uploaded to any GAI software, system, or platform unless:

1. Access to the GAI is controlled and restricted to only authorized study members involved in the research;

2. The data is not retained in or by the GAI; and

3. The activity does not contravene any applicable laws, regulations, or institutional policies in the process.

Researchers must prioritize and safeguard the privacy and confidentiality of research data when using GAI and will be held responsible for any leakage of data.

4. Data Owned by Non-NTU Entities
Data owned by external parties, including but not limited to businesses, organisations, and government ministries and agencies, are also not to be uploaded to any GAI software, system, or platform unless:

1. All requirements for confidential, sensitive data, and/or personal data listed under "Data Privacy & Confidentiality" are satisfied;

2. Valid written permission has been explicitly provided by the data owner/custodian; or

3. The use of such GAI has been agreed upon in the Research Collaboration Agreement (RCA).

5. Rules established by other Parties

If the proposal, manuscript or other document is being submitted to a agency, journal or other party with rules concerning GAI that differ from the NTU position, then the stricter rules should be followed. NTU researchers may contact RIEO for advice.

In conclusion, Generative AI in research must be used in an ethical, responsible, transparent, and accountable manner that benefits society. NTU faculty, staff, and students should adhere to the following when employing GAI in their research:

1. Appropriately acknowledge and cite the GAI used;

2. Do not attribute or list any GAI as an author;

3. Take full responsibility for the use of any GAI-generated content in their work; and

4. Safeguard the privacy and confidentiality of confidential and/or sensitive research data, including Personal Data.

Disclosure
OpenAI's ChatGPT (Mar 23 version) was used to improve the clarity and readability of this statement. No confidential or sensitive information was uploaded during this process.

Resources
UNESCO (2023). ChatGPT and Artificial Intelligence in Higher Education: Quick start guide.
Figure 1: When is it safe to use ChatGPT?
(UNESCO / ChatGPT and Artificial Intelligence in higher education: Quick start guide -

licensed under CC-BY-SA 3.0 IGO)

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NTU Position on the Use of Generative Artificial Intelligence (GAI) in Research

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Nanyang Technological University (NTU) recognizes that Generative Artificial Intelligence (GAI) - such as OpenAI's ChatGPT, Microsoft Bing, and Google Bard - provides new tools to support research practices and scholarly activities, including the preparation and drafting of research proposals, manuscripts, and other scholarly work.

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German HE: Leitfäden und Studien zu Künstlicher Intelligenz (KI) in Lehre & Unterricht

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German HE: Leitfäden und Studien zu Künstlicher Intelligenz (KI) in Lehre & Unterricht

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Overview of publications around the handling of ChatGPT in higher education and in the classroom. What are the possible applications and what should educators keep in mind when dealing with artificial intelligence? (as of June 2023)

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UTS: Five principles for the effective ethical use of generative AI

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UTS: Five principles for the effective ethical use of generative AI

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This guide introduces five principles, plus supporting resources, to assist academics in transitioning their assessments in response to the new opportunities that GenAI opens up, as well as academic integrity challenges.

Responsibility for issuing guidance to students on how GenAI may be used, for each assignment, rests with the lead academic.

https://lx.uts.edu.au/collections/artificial-intelligence-in-learning-and-teaching/resources/five-principles-for-effective-ethical-use-generative-ai/

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This policy governs all AI applications deployed by UTS, excluding research. It subscribes to the state government’s AI Ethics principles, and is accompanied by an AI Operations Procedures document specifying in more detail how it is to be implemented within UTS.

https://www.uts.edu.au/about/uts-governance/policies/uts-policy/artificial-intelligence-operations-policy

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These overarching provisional principles have guided the work of the Task Force on Generative AI in Teaching and Learning.

Students want to learn, and instructors want to support their learning.

Participatory learning – learning which happens in relationships and community – continues to be a valuable and vital way for students to learn.

Assessments that require students to document the process of learning continue to be meaningful for student learning.

Generative AI poses risks, as well as opportunities. Individuals will have different reactions and different expectations for the technology.

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Academic Excellence - Office of the Provost

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Developed by the Task Force on Generative AI in Teaching and Learning these Provisional Guidelines were last updated in August, 2023.

Share your questions or suggestions related to the Provisional Guidelines

Get in touch

Provisional Principles

These overarching provisional principles have guided the work of the Task Force on Generative AI in Teaching and Learning.

Students want to learn, and instructors want to support their learning.

Participatory learning - learning which happens in relationships and community - continues to be a valuable and vital way for students to learn.

Assessments that require students to document the process of learning continue to be meaningful for student learning.

Generative AI poses risks, as well as opportunities. Individuals will have different reactions and different expectations for the technology.

Provisional Guidelines on the Use of Generative AI in Teaching and Learning

1. Instructors are not required to use generative AI tools for teaching.

2. Individual instructors should determine if generative AI will be incorporated into course design, activities, and assessments based on course learning outcomes, individual interest, and conventions and expectations of the discipline.

3. Individual instructors should clearly communicate to students if and to what extent generative AI is acceptable in the course in the course outline, verbally in-class and in assessment descriptions. See the sample syllabus statements listed below.

4. Instructors with courses that incorporate generative AI should:

1. Consider the course learning outcomes and ensure the incorporation of generative AI will support core learning outcomes; and ensure incorporation offers meaningful learning, rather than inclusion for the sake of novelty.

2. Describe or discuss with students the strengths, limitations and ethical considerations of the technology, including factual inaccuracies or 'hallucinations', societal biases present in the training data and the rationale for using generative AI in assignments. See this sample slide deck for talking with your students.

1. McMaster's existing academic integrity policy applies when using generative AI. Its overall definition of academic dishonesty, which is to knowingly act or fail to act in a way that results or could result in unearned academic credit or advantage, allows for allegations related to generative AI. The policy states under item 18(c) that "It shall be an offence knowingly to ... submit academic work for assessment that was purchased or acquired from another source".

1. Unless otherwise stated, students should assume use of generative AI is prohibited.

2. Instructors who incorporate generative AI into courses should explain to students in writing and verbally in-class how generative AI material should be acknowledged or cited. See McMaster's citation guide for examples.

2. Generative AI plagiarism detection software is currently unavailable or not recommended at McMaster. This software will continue to be reviewed and may be used in the future.

1. These detectors will produce false positives and are not approved for use through the University's policy. Students have not consented to the sharing of their intellectual work through these tools. It is also unclear how the material submitted to the third-party detectors is retained or used.

2. Until more is understood about generative AI detection tools, instructors should not submit student work to generative AI detection tools.

3. McMaster has an institutional membership to Turnitin, a plagiarism detection software. Turnitin announced an update aimed at detecting writing produced by generative AI. McMaster, like many other institutions, has not yet turned on this feature as there is a need to understand the functionality of the tool, assess the security and privacy considerations for student work and determine whether it aligns with existing policies.

4. If you do suspect student work may have violated the academic integrity policy, please review the steps to take.

1. If instructors use generative AI in their teaching materials instructors should explain in the course outline the extent to which generative AI has been, or will be, used.

1. Instructors should fact-check any generative AI produced materials.

2. Instructors should not submit student work to generative AI tools for feedback without students' consent and ability to opt-out.

2. Course instructors have three options for directing teaching assistant use of generative AI

1. Permitting teaching assistants to use generative AI for any aspect of teaching assistant work, with the exception of summative evaluation, with no expectation that they use generative AI and no training specific to generative AI required. TAs must inform the instructor of the intended use of generative AI, and receive approval, before implementation.Summative evaluations are those which significantly impact a student's grade or progress in a course. This includes providing a quantitative grade (number or letter grade).

2. Requiring teaching assistants to use generative AI for specified teaching tasks as outlined in the hours of work form and with training provided.
In the instance of required use: As directed by the course instructor explicitly in the hours of work form, teaching assistants will use generative AI for the specific teaching tasks. Course instructors will provide teaching assistants with the necessary training to use generative AI for the specified teaching purpose(s) with this training included in the hours of work. Teaching assistants will evaluate all teaching materials/formative feedback developed with generative AI for accuracy before use with students. Any planned use of generative AI by teaching assistants will be shared with students in the course outline.

3. Prohibiting teaching assistants from using generative AI for teaching tasks

3. Generative AI tools can be used to provide formative feedback on student work; generative AI tools cannot be used to provide summative evaluation of student work.

AI-generated formative feedback is intended to guide learning and improve understanding, by pointing out strengths and areas for improvement in student work.

Summative evaluations are those which significantly impact a student's grade or progress in a course. This includes providing a quantitative grade (number or letter grade).

Instructors, or teaching assistants when directed, should review AI-generated formative feedback to ensure it aligns with the learning objectives and course materials, and add their own insights where necessary. Formative feedback that uses AI should not be given a quantitative grade by the AI tool. A "pass/fail" or "completion" may be applied.

Instructors, or teaching assistants when directed, are responsible for summative evaluations to ensure appropriateness and accuracy.

Data collection should be turned off on generative AI tools when used for providing formative feedback.

When providing AI-generated formative feedback, students should be made aware that it is generated by AI explicitly in the course syllabus.

4. Instructors who include assessments that incorporate generative AI should:

1. Consider including reflective components that invite students to comment on the use of/experience with generative AI in the assessment

2. Explicitly review criteria and/or rubrics in ways that demonstrate how the use of generative AI is being assessed.

1. Instructors incorporating generative AI should be aware of the privacy policies and user agreements of each generative AI tool and alert students to these policies in the course outline.

2. Where possible, courses that incorporate generative AI should rely on free versions of generative AI tools (e.g. Microsoft Bing, ChatGPT 3) for student use.
Alternatives should be provided for Generative AI tools that are restricted to users 18+ (e.g. ChatGPT).

1. Assessment alternatives that may be less susceptible to the use of generative AI include oral exams, presentations followed by a Q and A, invigilated/in-class assessments, practical tests, assessments that incorporate class discussion/activities, and process-based work.

2. Instructors may consider adding an honour pledge to assessments.

3. Students may opt-out of assessments that require the use of generative AI only in exceptional circumstances as approved by the course instructor. If approved to opt-out of an assessment that requires the use of generative AI based on an exceptional circumstance, students will not face academic penalty, but will be required to provide alternative and equivalent evidence of their learning as proposed to, and agreed to by, the course instructor.

1. The MacPherson Institute will continue to provide training and resources for instructors and students on how to use generative AI effectively. See the MacPherson Institute website for current workshops, resources and to schedule a consultation.

2. McMaster will explore an annual donation to carbon offsetting programs to address the environmental impact of training large AI models.

3. The MacPherson Institute will collect feedback from instructors and students this fall on their experiences, questions and concerns about using generative AI in teaching and learning in an effort to update and improve these guidelines.

4. These guidelines will be regularly reviewed and revised.

Sample Syllabus Statements

These sample syllabus statements may be included on a course syllabus to communicate with students the expectations around generative AI in a course. Instructors may adapt or modify these statements according to their individual teaching goals and course learning outcomes.

Students are not permitted to use generative AI in this course. In alignment with McMaster academic integrity policy , it "shall be an offence knowingly to ... submit academic work for assessment that was purchased or acquired from another source". This includes work created by generative AI tools. Also state in the policy is the following, "Contract Cheating is the act of "outsourcing of student work to third parties" (Lancaster & Clarke, 2016, p. 639) with or without payment." Using Generative AI tools is a form of contract cheating. Charges of academic dishonesty will be brought forward to the Office of Academic Integrity.

Example One
Students may use generative AI in this course in accordance with the guidelines outlined for each assessment, and so long as the use of generative AI is referenced and cited following citation instructions given in the syllabus. Use of generative AI outside assessment guidelines or without citation will constitute academic dishonesty. It is the student's responsibility to be clear on the limitations for use for each assessment and to be clear on the expectations for citation and reference and to do so appropriately.
Example Two
Students may use generative AI for [editing/translating/outlining/brainstorming/revising/etc] their work throughout the course so long as the use of generative AI is referenced and cited following citation instructions given in the syllabus. Use of generative AI outside the stated use of [editing/translating/outling/brainstorming/revising/etc] without citation will constitute academic dishonesty. It is the student's responsibility to be clear on the limitations for use and to be clear on the expectations for citation and reference and to do so appropriately.
Example Three
Students may freely use generative AI in this course so long as the use of generative AI is referenced and cited following citation instructions given in the syllabus. Use of generative AI outside assessment guidelines or without citation will constitute academic dishonesty. It is the student's responsibility to be clear on the expectations for citation and reference and to do so appropriately.

Students may use generative AI throughout this course in whatever way enhances their learning; no special documentation or citation is required.

Honour Pledges

Honour pledges are formal, student-led commitments to uphold the principles of academic honesty and integrity. These pledges represent students' personal assurance to maintain and respect academic standards, abstaining from any form of plagiarism, cheating, or other academic misconduct. They often form part of the assessment submission process, where students attach a pre-defined pledge to their work as a statement of authenticity. Several studies have investigated the use of honour codes and academic integrity and found them effective in reducing academic dishonesty.

Instructors might consider developing honour pledges together with their students, or adapting this McMaster honour pledge to their purposes.

"I understand and believe the main purpose of McMaster and of a university to be the pursuit of knowledge and scholarship. This pursuit requires my academic integrity; I do not take credit that I have not earned. I believe that academic dishonesty, in whatever form, is ultimately destructive to the values of McMaster, and unfair to those students who pursue their studies honestly. I pledge that I completed this assessment following the guidelines of McMaster's academic integrity policy."

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Provisional Guidelines on the Use of Generative AI in Teaching and Learning - Academic Excellence - Office of the Provost

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Provisional Guidelines: The Use of Generative Artificial Intelligence (AI) in Teaching and Learning at McMaster University - June, 2023 by McMaster University is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License [...]Read More...

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UNESCO survey: Less than 10% of schools and universities have formal guidance on AI

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UNESCO survey: Less than 10% of schools and universities have formal guidance on AI

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UNESCO survey: Less than 10% of schools and universities have formal guidance on AI

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A new UNESCO global survey of over 450 schools and universities found that fewer than 10% have developed institutional policies and/or formal guidance concerning the use of generative AI applications.

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Should You Add an AI Policy to Your Syllabus?

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Should You Add an AI Policy to Your Syllabus?

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Advice | Should You Add an AI Policy to Your Syllabus?

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What to consider in drafting your own course guidelines on students’ use of tools like ChatGPT.

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The Duke Community Standard is still the university's strongest measure against plagiarism and cheating, but the issue around whether and how AI-use falls into these categories is a complex one. As a baseline, faculty can emphasize that along with use of uncredited ideas and content created by persons, AI-generated content falls under the definition of plagiarism at Duke.

We encourage all faculty to thoughtfully consider their stance on AI use, even if you adopt existing policy language. In this shift in higher education and the rapidly changing AI market, standardized, one-size-fits-all, AI policies probably will not be sustainable in the long term. They may also not account for the varying stances instructors will take regarding AI use in their classrooms. Furthermore, because generative AI is becoming more ubiquitous, you'll need to consider your personal stance on AI in your own work and in your classrooms. Establishing an AI policy for your class allows you to have meaningful discussions with students on this topic. Being specific about how AI is or isn't allowed makes the rules clear for students and faculty if there are academic integrity violations as well.

The following guidelines for developing an AI policy for your syllabus include examples of generative AI policies from instructors, universities, and centers of teaching and learning. For the most part, they have been curated from a crowd-sourced document that we encourage you to explore to find AI policies developed by instructors in your field and which represent your level of comfort with the use of AI. Other sources are cited in the text itself.

Do your homework

Kevin Gannon (Queens College in Charlotte) argues in a recent article titled "Should I Write an AI Policy" that faculty need to read in a balanced approach to the subject before settling on their syllabus policy. He offers a list of varied sources that provide a primer on generative AI, pros and cons of AI use, and a grounding in how generative AI can be incorporated into teaching.

We encourage you to try one of the tools listed below to see what they are like. A good way to start exploring is to enter the prompts that you give students for an assignment into the AI tool, and see what the tool returns. Keep an open and curious mindset when considering whether such tools could be helpful to some or all of your students. There are two easy entry options. They both have cost-free options.

1. Open AI is the company that developed the content generator ChatGPT and image generator DALL-E2. Open AI offers internal documentation with training tips and

2. Try Google's products Bard and Workspace which offer a comparable experience to Open AI.

Share the rationale behind your policy

As you define your individualized AI syllabus statement, your rationale will no doubt be grounded in the intellectual work of the course, your discipline or your understanding of critical thought. Communicating these principles can be an important aspect of how you discuss your policies with your students. What will students lose (or gain) by using generative AI? Why did you choose your particular policy? What do you want students to understand about AI and their intellectual development?

In one extensive policy example from Joel Gladd (University of Western Idaho) he mentions two guiding principles in a course that will allow the use of AI: "1) Cognitive dimension: Working with AI should not reduce your ability to think clearly. We will practice using AI to facilitate-rather than hinder-learning. 2) Ethical dimension: Students using AI should be transparent about their use and make sure it aligns with academic integrity."

Lis Horowitz (Salem State University) shares a practical reason behind a zero tolerance policy for generative AI in their writing course. "Since writing, analytical, and critical thinking skills are part of the learning outcomes of this course, all writing assignments should be prepared by the student. Developing strong competencies in this area will prepare you for a competitive workplace. Therefore, AI-generated submissions are not permitted and will be treated as plagiarism."

By defining the idea of integrity, Megan McNamara (UC Santa Cruz) points out what is at stake when we talk about academic honesty and personal growth. As she states, "Integrity - other people's perception of your word as true - is one of the most valuable assets you can cultivate in life. Being attentive to integrity in academic settings allows others to trust that you have completed work for which you are taking credit." Her course allows students to use AI in limited ways and this rationale sets the foundation for this use.

Support AI literacy

While a never-ending list of rules won't encourage students to read your policy thoroughly, students do need more guidance than we might expect. Generative AI is a brand-new source for information and the rules for citation and the general use of AI are still under formation. It is important to highlight what will happen if you suspect an academic integrity violation and what the consequences will be if the allegation is true. But in addition, students need to understand how to become AI literate. Here are a few considerations for helping them:

When students have information about the limitations, bias, and inaccuracies of generative AI, it underscores why AI is not a shortcut to good results. Ethan Mollick (Harvard's Wharton School) frames it this way, "[d]on't trust anything it says. If it gives you a number or fact, assume it is wrong unless you either know the answer or can check in with another source. You will be responsible for any errors or omissions provided by the tool. It works best for topics you understand." The library at University of Northwestern St. Paul has a guide for students with an overview of AI's shortcomings, plus information on how to verify sources and double check AI responses.

If you explain to students how to cite content generated from AI sources it reduces the cognitive load of students unsure of how to act within the Duke Community Standard. This may encourage them to cite and note AI use more thoroughly. Many academic style guides have already formulated citation practices for generative AI. Monash University has curated an extensive list of the various AI citation formats.

You have the option to personalize your approach to citations. Some educators are instructing students to submit a transcript of the conversation with generative AI as an appendix to their work. Another alternative might be a reflective piece as a companion to an assignment, as Pam Harland (Plymouth State University) has done by providing guiding questions for students: "What was your prompt?" "Did you revise the AI model's original output for your submission?" "Did you ask follow-up questions?" "What did you learn?"

Define acceptable use

You may decide that you see room for the use of generative AI models in your courses. If so, clarify for your students the circumstances in which use of AI is allowable. For example, you may be comfortable with students using tools like Grammarly to help with spelling and grammar. Kim Sydow Cambell (University of Texas) offers an example policy that expands upon the tasks that students can ask AI to perform:

"Because the effective use of Artificial Intelligence (AI) tools is increasingly important to the work of technical communicators, their use is sometimes required or allowed in course assignments. AI tools can support a content creator during all phases of their work:

pre-writing: before content is created, writers can use some tools to research topics, collect genre samples, brainstorm ideas, craft outlines, etc.

drafting: some tools support the generation of content

revising: after content is generated, many tools aid writers in identifying and altering style/tone, spelling, punctuation, grammar, etc."

From Howard University of Law, Howard Bruckner explains to students what tasks are acceptable and underscores the students' responsibilities for ethical use. "Generative AI tools can be invaluable for generating ideas, identifying sources, synthesizing text, and starting to understand what is essential about a topic. But YOU must guide, verify and craft your work product; do not just cut and paste without understanding."

You may discover that if you allow AI use in different assignments, specific guidelines may need to accompany each assignment.

Explore the continuum of policies

You may wish to review these policy statements from the University of Delaware's Center for Teaching and Assessment of Learning, which distill the four basic approaches that instructors can take in their syllabi. We invite you to consider them as starting points in your exploration of what your AI policy will be.
Use prohibited
Students are not allowed to use advanced automated tools (artificial intelligence or machine learning tools such as ChatGPT or Dall-E 2) on assignments in this course. Each student is expected to complete each assignment without substantive assistance from others, including automated tools.
Use only with prior permission
Students are allowed to use advanced automated tools (artificial intelligence or machine learning tools such as ChatGPT or Dall-E 2) on assignments in this course if instructor permission is obtained in advance. Unless given permission to use those tools, each student is expected to complete each assignment without substantive assistance from others, including automated tools.
Use only with acknowledgement
Students are allowed to use advanced automated tools (artificial intelligence or machine learning tools such as ChatGPT or Dall-E 2) on assignments in this course if that use is properly documented and credited. For example, text generated using ChatGPT-3 should include a citation such as: "Chat-GPT-3. (YYYY, Month DD of query). "Text of your query." Generated using OpenAI. https://chat.openai.com/" Material generated using other tools should follow a similar citation convention.
Use is freely permitted with no acknowledgement
Students are allowed to use advanced automated tools (artificial intelligence or machine learning tools such as ChatGPT or Dall-E 2) on assignments in this course; no special documentation or citation is required.

About AI detection software

We don't recommend AI detection software for three main reasons.

1. The products are unreliable. The latest research on AI detection software from MIT highlights the false positive and negative rates. OpenAI (the company behind ChatGPT) recently withdrew its own detection software due to the software's unreliability.

2. Detection software is biased against non-native speakers, as research from Stanford shows.

3. As AI changes, detection software cannot keep up.

If you decide to use detection software, share that information with your students ahead of time. Results from the software should not be the only measure of whether students have cheated. Students can be encouraged to use detection software prior to handing in work to check for originality (although they should be warned of the limitations as well).

Another option is to craft a policy that warns students they will need to speak with you and defend their work if plagiarism is suspected. As Liza Long (College of Western Idaho) explains "[i]f I suspect that you have used ChatGPT, and you have not included the required citation and reflection, then you will need to meet with me either in person or through Zoom to talk about the assignment. This conversation will include knowledge checks for course content."

If you'd like to discuss your AI policy in more depth, please reach out to learninginnovation@duke.edu. You can also explore our central resource on AI in education.
Looking for other university syllabus policies? Please refer to the Office of Undergraduate Education menu of sample language for many other policies. You can also refer to Learning Innovation's template for help crafting the rest of your syllabus.

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Artificial Intelligence Policies: Guidelines and Considerations - Duke Learning Innovation

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The Duke Community Standard is still the university's strongest measure against plagiarism and cheating, but the issue around whether and how AI-use falls into these categories is a complex one. As ...

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U.S. Department of Education Shares Insights and Recommendations for Artificial Intelligence

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U.S. Department of Education Shares Insights and Recommendations for Artificial Intelligence

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U.S. Department of Education Shares Insights and Recommendations for Artificial Intelligence

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Today, the U.S. Department of Education's Office of Educational Technology (OET) released a new report, "Artificial Intelligence (AI) and the Future of Teaching and Learning: Insights and Recommendations" that summarizes the opportunities and risks for AI in teaching, learning, research, and assessment based on public input.

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Despite concerns, only 3% of colleges have developed student policy on AI

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Despite concerns, only 3% of colleges have developed student policy on AI

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The rate of administrators who believe AI generative writing tools would positively affect student learning shot up by 24% among those who used it compared to those who haven't.

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Despite concerns, only 3% of colleges have developed student policy on AI

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Firsthand use of generative AI changed administrators’ beliefs about its potential value and the need for regulation.

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University of Minnesota - Sample ChatGPT syllabus statements

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University of Minnesota - Sample ChatGPT syllabus statements

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This resource from the Office of the Executive Vice President and Provost provides instructors with sample ChatGPT policies. Sample statements cover allowing ChatGPT usage, allowing limited ChatGPT usage, and prohibiting ChatGPT usage completely.

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The following syllabus statements were developed by the Senate Committee on Educational Policy as a resource for faculty and instructors, with options ranging from "no restrictions" to "ChatGPT and other AI tools may not be used under any circumstances." These statements may be adopted or adapted to reflect a given instructor's course learning objectives and instructional / assessment style.

For instructors who wish to embrace ChatGPT

Artificial intelligence (AI) language models, such as ChatGPT, may be used for any assignment with appropriate citation. Examples of citing AI language models are available at: l ibguides.umn.edu/chatgpt [or provide an alternative reference appropriate for your class]. You are responsible for fact checking statements composed by AI language models.

For instructors who wish to allow limited usage of ChatGPT

Artificial intelligence (AI) language models, such as ChatGPT, may be used for [assignment types A, B & C] with appropriate citation, but not for [assignment types D, E & F]. If you are in doubt as to whether you are using AI language models appropriately in this course, I encourage you to discuss your situation with me. Examples of citing AI language models are available at: libguides.umn.edu/chatgpt [or provide an alternative reference appropriate for your class]. You are responsible for fact checking statements composed by AI language models.

For instructors who wish to prohibit the usage of ChatGPT

The Board of Regents Student Conduct Code states the following in Section IV, Subd.1: Scholastic Dishonesty:

"Scholastic dishonesty means plagiarism; cheating on assignments or examinations, including the unauthorized use of online learning support and testing platforms; engaging in unauthorized collaboration on academic work, including the posting of student-generated coursework on online learning support and testing platforms not approved for the specific course in question; taking, acquiring, or using course materials without faculty permission, including the posting of faculty-provided course materials on online learning and testing platforms; ..."

Artificial intelligence (AI) language models, such as ChatGPT, and online assignment help tools, such as Chegg®, are examples of online learning support platforms: they can not be used for course assignments except as explicitly authorized by the instructor. The following actions are prohibited in this course [remove bullets as necessary]:

Submitting all or any part of an assignment statement to an online learning support platform;

Incorporating any part of an AI generated response in an assignment;

Using AI to brainstorm, formulate arguments, or template ideas for assignments;

Using AI to summarize or contextualize source materials;

Submitting your own work for this class to an online learning support platform for iteration or improvement.

If you are in doubt as to whether you are using an online learning support platform appropriately in this course, I encourage you to discuss your situation with me.

Any assignment content composed by any resource other than you, regardless of whether that resource is human or digital, must be attributed to the source through proper citation. (Examples of citing content composed by digital tools are presented in: libguides.umn.edu/chatgpt [or provide an alternative reference appropriate for your class].)

Unattributed use of online learning support platforms and unauthorized sharing of instructional property are forms of scholastic dishonesty and will be treated as such.

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The following syllabus statements were developed by the Senate Committee on Educational Policy as a resource for faculty and instructors, with options ranging from "no restrictions" to "ChatGPT and other AI tools may not be used under any circumstances." These statements may be adopted or adapted to reflect a given instructor's course learning objectives and instructional / assessment style.

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Scribbr- University Policies on AI Writing Tools | Overview & List

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Scribbr- University Policies on AI Writing Tools | Overview & List

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This spreadsheet details the AI policies of 100 American universities. It is current through June 2023 and updated as circumstances change.

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Boston College Center for Teaching Excellence (CTE) - Artificial Intelligence in Teaching and Learning

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Boston College Center for Teaching Excellence (CTE) - Artificial Intelligence in Teaching and Learning

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This resource from the CTE provides instructors with background on ChatGPT and guidance on how to best design coursework in response to emerging AI tools.

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ChatGPT in Teaching and Learning

Limitations of ChatGPT

While ChatGPT can produce text that can pass for human-created work, it does have many significant limitations:

ChatGPT does not use any information after 2021. Current events and trends are not a part of its database.

ChatGPT does not access the internet. It relies completely on the information already present in its database.

ChatGPT is not "unbiased." It is limited by the culturally-specific and language-specific information that was used to train it.

ChatGPT will often make up facts, produce misleading information, and include content unrelated to a user's question. This is because the technology is constantly changing and adjusting to how users interact with it. The technology has no native way to differentiate fact from fiction.

Faculty Concerns

Faculty have raised several questions and concerns since ChatGPT was released in November 2022:

How will we know if students are submitting original work?

Should we ban the use of ChatGPT and other AI tools?

If we assume students will use ChatGPT, how can the content it produced be cited?

Are there ways that ChatGPT can be used to help students learn?

Can we use ChatGPT for our own course design?

Tools for Detecting AI

While these tools may be helpful in some cases, there are also significant downsides to using these tools:

It is unsustainable for faculty to run every one of their students' submissions through these services. It is not only an enormous time-consuming process, it is also likely that the services will be inaccurate.

Using these tools creates an adversarial relationship with students wherein they are taught that being caught cheating is worse than choosing to use the service in the first place.

As of January 2023, it is unclear whether or not there are FERPA violations in submitting student work through these plagiarism detectors.

Below are alternative instructional responses to ChatGPT, including short- and long-term interventions.

Instructional Responses to ChatGPT

Instructional responses to the use of ChatGPT and similar tools vary depending on how much time and energy faculty have to make course-level or assignment-level changes. The short-term solutions provided below are meant to serve as an immediate response that should be revisited when time permits. The long-term approaches, on the other hand, are meant to prompt students to think more critically about the technology and their own intellectual formation.

Course Design: Short-Term Interventions

Update Your Syllabus

Syllabus Statement 1 (Discourage Use of AI)

Artificial Intelligence (AI) Tool Usage: AI tools can generate text, images, and other media very quickly. Since a central goal of this course is to help you become independent and critical thinkers, you are discouraged from using AI tools to create text, video, audio, or images that end up in your work (assignments, activities, responses, etc). Any work submitted using AI tools will be treated as though it was plagiarized.

If any part of this is confusing or uncertain, please reach out to me for a conversation before submitting your work.

Note: This statement assumes the syllabus has an academic integrity policy and/or statement about how plagiarized work will be treated.

Syllabus Statement 2 (Treat AI-generated text as a source)

Artificial Intelligence (AI) Tool Usage: AI tools can generate text, images, and other media very quickly. Since a central goal of this course is to help you become independent and critical thinkers, you are discouraged from using AI tools to create text, video, audio, or images that end up in your work (assignments, activities, responses, etc).

If any AI-generated content is used for your assignments, you must clearly indicate what work is yours and what part is generated by the AI. In such cases, no more than 10% of the student work should be generated by AI. Any AI-generated work not cited and/or used for more than 10% of your assignment will receive ____.

If any part of this is confusing or uncertain, please reach out to me for a conversation before submitting your work.

Note: This statement assumes that students are told which citation styles to use for secondary sources. The instructor would indicate the penalty for not following the policy.

Discuss ChatGPT With Students

Allocate some time in class to discuss ChatGPT. The conversation can be a way to discuss ethics in education, your expectations, as well as student perspectives. Discussion prompts and questions can include:

What are some reasons students would want to use ChatGPT?

In what ways does ChatGPT impact the course goals? In what ways can AI prevent or enable students in meeting these goals?

What are the differences between plagiarism, remixing, influencing, and originality?

How does the use of Wikipedia compare to the use of ChatGPT?

What is Academic Integrity and why is it so valued at Boston College?

Collect a Writing Sample

Towards the beginning of the semester, ask students to write in class a short version of what you might ask them to write later in the semester. This "diagnostic" can be a source of comparison if a student later submits work that seems significantly different.

Additional Short-Term Course Design Interventions

Course Design: Long-Term Approaches

Break Up Major Assignments

Divide major assignments into smaller graded components that build on each other. This "scaffolding" approach requires students to incorporate feedback on earlier assignments to improve their later assignments.

Distribute the Grading

When students are asked to produce one or two exemplar assignments for a semester, they are often pressured to focus on the product rather than the process of learning. By creating more lower-stakes graded assignments, instructors encourage students to demonstrate learning as a continuous activity. Faculty can see the progress of their students and easily discover how thinking on a subject has evolved.

Assess Student Workload

Assignment Design: Short-Term Interventions

Try running your assignment prompts through ChatGPT itself. If you are finding the responses to be on par for what you would expect, make small changes to make it more challenging for the tool to be used inappropriately, and plan for longer-term adjustments to how the technologies should be engaged, if it all.

Require Recent References

ChatGPT is only able to use information prior to 2021 in its responses. By asking students to use current events, recent newspaper sources, or very recent academic articles, faculty can more reliably assume that the analysis will be the student's own.

Since ChatGPT does not use materials behind paywalls or require institutional access in its output, faculty may also want to ask students to refer to specific journals or resources that are accessible to them by virtue of being a Boston College student.

Add Reflection to Assignment

Show Students How to Cite

If allowing students to use ChatGPT to some degree, or assuming students will do so whether it's allowed or not, demonstrate how to attribute content produced by the AI.

Assignment Design: Long-Term Approaches

Teach with ChatGPT

Ask students to analyze the output of the AI for a question that could easily be asked as part of an assignment. In the analysis, see if students can differentiate the output with something that a human would produce. For example:

Does the output have a "style" of writing that makes it distinct?

Does the AI rely on clichés or casual speech in a manner that is inappropriate for the topic?

How would a student rewrite the AI output to be more accurate or more distinct?

It can access any information fed into or created by its technology

It uses log-in data, tracking, and other analytics

The technology does not respond to "Do Not Track"

Plan for Social Annotation of Text

Create Assignments that Require Multiple Modes

Since ChatGPT is text-based, it can only render output that uses traditional sentence structure and syntax. In place of written essays, faculty can ask students to create multimodal submissions: podcasts, posters, mind maps with annotations, short videos, etc.

Future Impact of AI on Teaching and Learning

The strategies suggested above respond to what we know about AI tools today; however, it's clear that such technologies will improve and so our strategies will have to improve with them.

In the long run, instructional responses that engage the technology and its limits - rather than seek to simply ban them - promise to be more effective ways to meet learning goals across disciplines. Such strategies may also help faculty find new ways to respond to other persistent challenges in higher education, such as:

How can AI tools be used ethically and strategically for our curriculum?

How can they be used to teach students about information literacy, data privacy, and intellectual property?

Can AI be used to promote more equitable learning experiences for students, especially those who have faced structural barriers to resources? See Equity and Academic Integrity for a reference on how accusations of cheating are disproportionately aimed at underrepresented learners.

Should we find more ways to assess the process of learning and not just the product?

The CTE will continue to update this resource with suggestions, strategies, and perspectives that can inform faculty decisions on these questions.

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Artificial Intelligence in Teaching and Learning

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There are many artificial intelligence (AI) tools that can generate “human-like” responses to a wide range of questions and statements. Among the most popular generative AI (or GenAI) tools is ChatGPT, a text-based tool that can produce essays, reports, lesson plans, and more. Boston College student…

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Classroom policies for AI generative tools

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Classroom policies for AI generative tools

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This resource was created by Lance Eaton, for the purpose of sharing and helping other instructors see various policies made by other educators, to help in the development of their own policy development.

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Syllabi Policies for AI Generative Tools

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All content generated through the use of ChatGPT is to be understood and identified as “co-authored” by the user and the AI. To mitigate the possible risks of AI-generated content, OpenAI (the creators of ChatGPT) have developed their own Sharing & Publication Policy.

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LibGuides: ChatGPT: ChatGPT and Ethical Considerations

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LibGuides: ChatGPT: ChatGPT and Ethical Considerations

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Arizona State University - Generative AI FAQs

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Arizona State University - Generative AI FAQs

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ASU faculty and administrators are focused on the positive potential of generative AI while also thinking through concerns about ethics, academic integrity, and privacy. The Provost’s Office is currently reviewing ASU’s academic integrity policy through the lens of what kind of content can be produced through generative AI and what kind of learning behaviors and outcomes are expected of students. Students are advised to confer with instructors about their course policies regarding AI use before using AI tools in their coursework.

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Generative AI FAQs | Office of the University Provost | ASU

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Yale University's Teaching and Learning Center - AI Guidance

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Yale University's Teaching and Learning Center - AI Guidance

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Individual instructors decide on whether AI tools may be used in their courses. Instructors should be clear and transparent about which tools are allowed and which ones are not, and the reasons for any restrictions.

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Since OpenAI released ChatGPT in November 2022, the availability of generative AI tools that produce text, image, or code has expanded to include Microsoft's BingAI, Google's Bard, and OpenAI's more powerful GPT-4. The power of these tools to answer complex questions and generate coherent text continues to improve, and they've recently been integrated into widely used software like Google Docs and Microsoft Office. This has led to questions about how students are using these tools and how faculty can respond to best support their students' learning.

In consultation with instructors and technology experts at Yale and beyond, the Poorvu Center offers guidance on exploring how Generative AI works and suggestions about how to adapt your current teaching. We also offer advice for teaching fellows and a list of recommended reading. Additional resources include examples for incorporating AI into teaching from Yale instructors and resources from Poorvu Center events on teaching in the age of AI.

Instructors: Share your thoughts and ideas with us! Send questions or examples of how you are integrating these tools in your lessons or adjusting in light of them. Email us at askpoorvucenter@yale.edu.

Understanding Generative AI

Generative AI tools respond to user-submitted text with relevant content expressed in natural language. Many of these tools remember all text that a user has entered in an ongoing conversation, and they are capable of revising their responses when prompted.

Generative AI tools operate on programs called large language models. (The technology that underlies image and coding tools is also referred to by LLM.) LLMs are programmed by being fed large bodies of text (e.g. the entire internet) and trained to predict the most relevant sequence of words in response to a prompt. As a result, they can draw on a vast database of human knowledge, and their responses reflect the biases and limitations of the material they are trained on. Put differently, generative AI produces text by calculating the probability that it will be relevant to the prompt a user has submitted. As a result, the responses produced by generative AI tools tend to reflect consensus understandings, including any biases and inaccuracies that inform those positions.

Because generative AI predicts words based on probabilities, they are not capable of original thought. They are best at responding to prompts that require less creativity, like summarizing information, solving problems whose answers are already known, or supporting one position among alternative theories or approaches. But AI tools can generate new ideas by combining existing knowledge in new ways, and users can submit follow-ups that prompt the bot to incorporate additional ideas into an existing response or to revise it for greater complexity.

The best way to learn about AI tools is to try them yourself. Signing up for an account at ChatGPT, Google Bard, or BingAI (requires Microsoft Edge browser) will allow you to explore how these programs work and help you better understand how your students might be using them. You can often learn the most about these programs by simply letting curiosity be your guide. But here are a few prompts you might use to get started:

Ask the program to write a response to one of the assignments from your class.

Prompt the tool for help with a task you're working on like writing an email or choosing the next step toward completing a project.

Choose an assignment one of your students has submitted to your class and prompt the AI tool to produce a response that is as close as possible to the student's, entering follow-up prompts as necessary to bring the text closer to the student's.

Ask the tool to teach you about a subject and then quiz you at the end.

A few words of caution
While it can be valuable to try out these tools so that you understand how they work, they do raise issues of privacy and intellectual property.

Material that you submit may then become part of the program's database-using the software also contributes to its development.

The systems have different policies and methods to let you remove your material, so be sure to look for those instructions and to review the Terms of Service.

If you ask students to use these tools-and over time, many of us may encourage some of this collaboration-the risks become even more complicated.

Even software platforms that don't use your content to train the product DO collect your information and often install cookies to track your other activity. While you may choose to make this bargain to try out AI tools yourself, it can be more problematic to require students to sign up for tools that will track and use their private data.

This article from the New York Times offers guidance on how to phrase prompts submitted to AI tools, and this Beebom article lists dozens of sample prompts that show the range of generative AI capabilities.

Guidance for Instructors

(1) Instructors should be direct and transparent about what tools students are permitted to use, and about the reasons for any restrictions. Yale College already requires that instructors publish policies about academic integrity, and this practice is common in many graduate and professional school courses. To ensure that your students' use of AI tools aligns with your learning goals, consider updating your academic integrity policy to clarify how students are permitted to engage with AI tools and how they should signal that it has been used. Find example statements in the next section.

As for explaining why, understanding the learning goals behind assignments helps students commit to them. The only reason to assign written work is to help students learn-either to deepen their understanding of the material or to develop the skill of writing in itself. From our long experience in reviewing the portfolios of Yale College students, we see clear evidence that over the course of four years, they progress significantly as writers.

Research shows that people learn more and retain the information longer when they write about it in their own words. If instead, students task an AI to generate texts, they won't learn as much. This is why we ask them to write their own papers, homework assignments, problem sets, and coding assignments. This impact on learning applies across all disciplines-STEM problem sets that require explanations also depend on students' generating language to learn more deeply. AI tools can also generate coding solutions, not just natural language.

(2) Controlling the use of AI writing through surveillance or detection technology is probably not feasible. Turnitin has acknowledged a reliability problem with its detection tool software and Yale has not enabled this feature in Canvas. We believe there are more fruitful ways to engage writing processes and expectations than to rely on predictions that will probably be outpaced by further AI development.

Research by the Poorvu Center shows that a minuscule number of Yale College students plagiarize in their course papers. While acknowledging that new technologies can change the landscape, we know that most students want to do their own work because they care about learning.

Tools like ChatGPT raise broader questions. We agree with this approach from Inside Higher Ed: "Go big. How do these tools allow us to achieve our intended outcomes differently and better? How can they promote equity and access? Better thinking and argumentation? ... In the past, near-term prohibitions on ... calculators, ... spellcheck, [and] search engines ... have fared poorly. They focus on in-course tactics rather than on the shifting contexts of what students need to know and how they need to learn it." While the Poorvu Center doesn't have all the answers, we can offer some general guidance and work with you to put this advice into practice.

(3) Changes in assignment design and structure can substantially reduce students' likelihood of cheating-and can also enhance their learning. Based on research about when students plagiarize (whether from published sources, commercial services, or each other), we know that students are less likely to cheat when they:

Are pursuing questions they feel connected to

Understand how the assignment will support their longer-term learning goals

Have produced preliminary work before the deadline

Have discussed their preliminary work with others

Make their writing process visible in the completed assignment

Many of the above characteristics can be integrated into authentic assessments, which are assignments that 1. have a sense of realism to them, 2. are cognitively challenging, and 3. require authentic evaluation (see this handout for more). Examples can be found at the bottom of this website. Authentic assessments also align with practices that prioritize student learning, and make it harder to collaborate with AI tools, including:

Asking students to engage primary and secondary sources, which can include:

Assignments where students place their ideas in conversation with the ideas of other scholars-either readings you give them, or sources they find themselves

Asking students to use resources that are not accessible to ChatGPT, including any resources behind a paywall or many of the amazing resources in Yale's collections.

Incorporating the most up-to-date resources and information of your field so that students are answering questions that have not yet been answered or have only begun to be answered.

Engaging with ChatGPT as a tool that exists in the world and having students critically engage with what it can produce as in these examplesfrom Yale instructors.

Using alternative ways for students to represent their knowledge beyond text (e.g., draw images, make slides, facilitate a discussion). Consider adopting collaboration tools like VoiceThread.

Addressing Generative AI on your Syllabus

The Poorvu Center recommends including on your syllabus an academic integrity statement that clarifies your course policies on academic honesty. The simplest way to state a policy on the use of ChatGPT and other AI composition software is to address it in your academic integrity statement.

A policy that encourages transparency in how students use AI might read: Before collaborating with an AI chatbot on your work for this course, please request permission by sending me a note that describes (a) how you intend to use the tool and (b) how using it will enhance your learning. Any use of AI to complete an assignment must be acknowledged in a citation that includes the prompt you submitted to the bot, the date of access, and the URL of the program.

If you think students' learning is best supported by avoiding AI altogether, your course policy might read: Collaboration with ChatGPT or other AI composition software is not permitted in this course.

Guidance for Teaching Fellows

As a teaching fellow, raising the issue of ChatGPT with your lead instructor can open a broader conversation about the goals of your course and how ChatGPT might impact those goals. For example, it is helpful to understand the intended purpose of both low-stakes and high-stakes assessments, in addition to possible redesigns to work students are doing outside of class.

Regardless of whether you make changes to assessments, you play an important part in making the goals clear to your students. Whether you hope that their work will help them explore new ideas, familiarize themselves with important concepts, or make progress toward a bigger project, articulating those learning outcomes and the purpose of the activities that support them will help students understand why using AI would be counterproductive to their own development and progress.

These conversations with students can happen in conventional sections, but they are also worthwhile to have in office hours and review sessions. Additionally, as you give feedback to students, think about ways that you can point them back to the larger goals of the course: by showing them how they can grow through the assessment process, you indicate its value.

Poorvu Center Sessions

Teaching with AI Writers: Conversation and Workshop on May 25, 2023 provided a space for faculty to hear the various changes colleagues have made to their teaching in response to new artificial intelligence writers, as well as a hands-on opportunity to consider their own assignment design approach.

Spring Teaching Forum: Teaching in the Age of ChatGPT on May 1, 2023 hosted Derek Bruff-an educator, author, higher ed consultant, and former director of the Vanderbilt University Center for Teaching-to briefly survey the landscape of AI text generation tools, then dive into the teaching choices they present.

Artificial Intelligence and Teaching: A Community Conversation on February 14, 2023 featured a panel of Yale faculty and students discussing the implications of AI-writing software for teaching and learning.

Incorporating AI in Teaching: Examples from Yale Instructors

Yale instructors are considering how to redesign elements of their courses to address ChatGPT and other AI platforms. Examples across the disciplines demonstrate some of the novel ways instructors are engaging their students as this technology evolves.

Recommended Reading

New articles on this fast-developing topic are appearing every day. Below is a selection of thoughtful pieces that address various elements of this tool.

The Department of Education released insights and recommendations on AI and the Future of Teaching and Learning (May 2023). Brief summary of the report is available on EdSurge.

OpenAI, the creator of ChatGPT, shares about safety and how they train their tool as well as their guidelines for educators.

Mollick and Mollick at UPenn published seven approaches to using AI in the classroom that aim to help instructors navigate the integration of AI-assisted learning in classrooms (June 21, 2023).

The Chronicle of Higher Education outlines professors' plans to be ready in the fall for ChatGPT (June 13, 2023), provides these highlights from their virtual forum on AI and academic integrity (June 12, 2023), suggests how ChatGPT could help or hurt students with disabilities, and offers some of the ways AI may change how professors assess learning (April 5, 2023).

The Intentional Teaching podcast featured an episode, Rethinking Teaching in an Age of AI, which raises some important questions for educators to consider as they retool courses and assignments to account for AI technology.

The team at NPR's Planet Money creates an episode using Artificial Intelligence and learns ChatGPT's limitations and advantages.

The Center for Teaching at Vanderbilt University and the University of South Carolina's Center for Teaching Excellence offer comprehensive AI guidance to instructors from revised learning goals and assessment to teaching strategies.

Jenny Frederick, Poorvu Center Executive Director, discusses the center's AI guidance with NBC Connecticut (February 14, 2023).

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Since OpenAI released ChatGPT in November 2022, the availability of generative AI tools that produce text, image, or code has expanded to include Microsoft's BingAI, Google's Bard, and OpenAI's more powerful GPT-4. The power of these tools to answer complex questions and generate coherent text continues to improve, and they've recently been integrated into widely used software like Google Docs and Microsoft Office.

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ChatGPT and Other AI Tools: A Guide for Faculty

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ChatGPT and Other AI Tools: A Guide for Faculty

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The WSU Office for Teaching and Learning (OTL) has gathered suggestions for using ChatGPT from POD listserv discussions and chats. Here are some ideas to consider: Embrace the tools used in the real world. Try to think about how or if this task would be done in the real world or workplace. Would they use AI to help them do some task? Then let students use it, too. Have students generate something with AI and then discuss how to improve on it. Or how it could be used to in the professional world to speed up tedious or repetitive tasks.

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Research Guides: ChatGPT and Other AI Tools: A Guide for Faculty: Using ChatGPT and AI in Teaching

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This guide provides an introduction to ChatGPT. It includes a list of readings, podcasts, and other informational sources on ChatGPT and AI. The guide includes ideas for using ChatGPT as a pedagogical tool and strategies for creating assignments

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University of Texas (Austin) - Acceptable use of ChatGPT and similar AI tools

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University of Texas (Austin) - Acceptable use of ChatGPT and similar AI tools

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This advisory provides guidance on how to acceptably use AI Tools safely, without putting institutional, personal, or proprietary information at risk. Additional guidance may be forthcoming as circumstances evolve.

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With the emergence of ChatGPT, Bard and other large language model generative artificial intelligence tools, hereinafter collectively referred to as "AI Tools", many members of our community are eager to explore their use in the university context. This advisory provides guidance on how to acceptably use these AI Tools safely, without putting institutional, personal, or proprietary information at risk. Additional guidance may be forthcoming as circumstances evolve.

At present, any use of ChatGPT or similar AI Tools cannot use any personal, confidential, proprietary, or otherwise sensitive information. In general, student records subject to FERPA , health information, proprietary information, and any other information classified as Confidential or Controlled university data must not be used with AI Tools.

Similarly, ChatGPT or similar AI T ools must not be used to generate output that would be considered non-public. Examples include, but are not limited to generating proprietary or unpublished research; legal analysis or advice; recruitment, personnel or disciplinary decision making; completion of academic work in a manner not allowed by the instructor; creation of non-public instructional materials; and grading.

Please also note that the company that owns ChatGPT, OpenAI , explicitly forbids the use of ChatGPT and their other products for certain categories of activity, including fraud and illegal activities. This list of items can be found in their usage policy . AI Tools of any sort may not be used for any activity that would be illegal, fraudulent or a violation of any state or federal law, or UT Austin or UT System policies.

For further guidance on the use of ChatGPT or other AI Tools for teaching and learning, please see the following guidance from the Center for Teaching and Learning .

Rationale for the Above Guidance:

No UT Agreement, No Privacy and Security Terms: All content entered into, or generated by, ChatGPT is available to ChatGPT, its parent company, OpenAI, and their employees. There is currently no agreement between UT Austin and OpenAI , Microsoft or other AI Tools that would provide data security and privacy protections required by UT policy with regard to ChatGPT , OpenAI's , or other AI Tools' programming interface. Consequently, the use of ChatGPT or other AI Tools at this time could expose individual users and UT to the potential loss and/or abuse of sensitive data and information.
As of May 2023, the UT Austin Business Contracts Office is working on this issue. We hope to see this addressed in the near future and will update this guidance when additional information is available.

Personal Liability: ChatGPT and other AI Tools use click-through agreement s . Click-through agreements, including OpenAI and ChatGPT and other AI Tools' terms of use, are contracts. Individuals who accept click-through agreements without delegated signature authority may face personal consequences, including responsibility for compliance with terms and conditions [1].

Guidance on Appropriate Use

For questions regarding the appropriate use of ChatGPT and other AI Tools, please contact the UT Information Security Office at security@utexas.edu .

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Acceptable Use of ChatGPT and Similar AI Tools

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With the emergence of ChatGPT, Bard and other large language model generative artificial intelligence tools, hereinafter collectively referred to as "AI Tools", many members of our community are eager to explore their use in the university context. This advisory provides guidance on how to acceptably use these AI Tools safely, without putting institutional, personal, or proprietary information at risk.

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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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The UCLA Student Conduct Code states, “Unless otherwise specified by the faculty member, all submissions, whether in draft or final form, to meet course requirements (including a paper, project, exam, computer program, oral presentation, or other work) must either be the student’s own work, or must clearly acknowledge the source.” Unless an instructor indicates otherwise, the use of ChatGPT or other AI tools for course assignments is akin to receiving assistance from another person and raises the same concern that work is not the student’s own. ... The ultimate decision and responsibility for how to teach about AI and the establishment of or revision of course policies related to its use lies with the instructor.

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About this guide:

Students and instructors are embracing ChatGPT (GPT-4 as of March 2023) and similar artificial intelligence (AI) technologies across disciplines for different learning goals. There is no one-size-fits-all best practice for their use. This document is meant as a guideline for instructors on what to consider as these tools evolve. We will provide strategies for adopting AI technologies in a responsible, ethical manner, and innovating within each discipline, major, and course. Exploring and communicating about the opportunities and limitations to using these tools will allow instructors and students to critically think about how knowledge is created.

Acknowledgements: This guide was a collaborative development which included contributions from the Center for Education Innovation and Learning in the Sciences, the Bruin Learn Center for Excellence, UCLA's Online Teaching and Learning, Excellence in Pedagogy and Innovation in the Classroom, the Center for the Advancement of Teaching along with input from members of the Academic Senate, the Center for Accessible Education, Advanced Research Computing (Privacy and Governance), and University Registrar. Special thanks to Ava Arndt, Jess Gregg, Ilana Intonato, and Rachel Kennison for leading the writing and editing process.

UCLA Virtual Town Hall: What is ChatGPT and How Does it Relate to UCLA's Academic Mission held on March 3, 2023, featured the following UCLA faculty panel:

Dr. Safiya Noble, Professor of Gender Studies and African American Studies; Interim Director, UCLA DataX Initiative

Dr. Ramesh Srinivasan, Professor of Information Studies; Director, UC Digital Cultures Lab.

Dr. John Villasenor, professor of Electrical Engineering, Public Policy, Law and Management; Faculty Co-Director, UCLA Institute for Technology, Law and Policy

What is ChatGPT?

ChatGPT is a "chatbot" developed by a private company called OpenAI. Users can enter question prompts and within seconds ChatGPT will produce text-based responses in the form of poems, essays, articles, letters and more. It can also create structured responses like tables, bulleted lists and quizzes. ChatGPT can provide translation and copy language style and structure. It can also be used to develop and debug programming code. New and expanded uses continue to be developed and launched. A similar tool called DALL-E uses AI to create art pieces, and other AI tools have been created or are in rapid development to do even more - music, animation, multimedia video, powerpoints - and the list goes on.

Yes, students have already been exploring and using it to support completion of their coursework. Some courses explicitly encourage the use of ChatGPT for assignments. However, one concern is that students may be using ChatGPT to draft responses to homework responses without learning the material, and this presents the challenges and opportunities for reflection on teaching and learning at this time.

CEILS Education Research Talk with Jess Gregg. Demo portion starts just after the 5 minute mark.

Revolutionizing Education with ChatGPT: How AI is Transforming the Way We Learn - Learning and Technology with Frank

Dig Deeper (article): How ChatGPT Works

Try out ChatGPT and Reflect: What are the opportunities?

Instructors can sign up for a free account at ChatGPT (or GPT-4) on Open AI's platform. A first step in exploring the tool may be to enter some of your assignment prompts and assess the accuracy of the output. Then reflect on how you might embrace the tool or implement strategies that make use of the tool unnecessary.

PRO TIP: Including your students in the reflective process is also a learning opportunity to help them understand the benefits and limitations of the tool.

A few things to try:

Ask ChatGPT a question - it could be a homework assignment or any question.

How would you evaluate the response provided by ChatGPT?

Try modifying the prompt and see how that changes the response.

Ask Chat GPT to synthesize text from large documents. For example, enter a 3500 word paper as a prompt, and ask ChatGPT to create an 18 slide PowerPoint presentation, with headings and bullet points, making a persuasive case for action.

Prompt for writing samples specific to your area of expertise. For example, ask ChatGPT to generate a nurse practitioner note for a 53 year old male with hypertension presenting with shortness of breath and dizziness. Another example is to ask for an email introducing your upcoming course to enrolled students.

Ask Chat GPT to translate something. Together with your students examine the translation to see how well it did. Ask for improvements, or consider when such translation capabilities might be

If you teach students how to code, ask ChatGPT to correct incorrect code (debug code). Consider ways this might help students who are learning to code in your course.

If you teach a writing intensive course, try asking ChatGPT to respond to a writing prompt in a specific style (like a popular author) or create a poem on a specific topic.

Reflect on the potential for ChatGPT to support student writing. Which writing skills do you feel are fundamental for students to do independent of artificial intelligence? How might ChatGPT facilitate the development of writing or problem solving skills?

Have students use ChatGPT to write a draft and then have them edit what it produces or check for errors

Note: If you do not wish to create an account, reach out to your local teaching support to set up a consultation and explore your assignments together. There are also many demonstrations of ChatGPT available online that you can search and watch.

Ideas for Updating Your Course Activities with AI in Mind

Consider utilizing ChatGPT and other AI tools explicitly: After experimenting with ChatGPT you may decide that you want to incorporate exercises where students are explicitly encouraged to interact with ChatGPT.
Example:

Ask students to use ChatGPT and "fact check" the response provided by finding primary and secondary sources to back up the information provided.

Ask students to generate a first draft using ChatGPT then keep track changes in a document to refine/edit.

Reflecting upon prompt engineering - -use prompting logic used by students to generate information and then provide a different prompt to help guide revision. Showcase that small changes can lead to major differences in output!

The University of Wisconsin, Madison provides some examples for how to integrate AI into the writing process in your classroom

View this collaborative Google Doc: AI Examples and Resources to see examples and resources curated by UCLA's teaching and learning community.

Adapt or create assignments that are not easily completed using AI: Be more explicit about having students provide references for assignments, use a social annotation tool like Hypothes.is or Perusall, utilize comments, Microsoft Word Track Changes or Google Docs Suggesting mode for individual or group annotation, have students complete written assignments in class, ask students to connect learning to their personal experiences and/or current events. Support students in developing oral communication skills by providing more opportunities for in-class presentations (or during discussion sections).

Explore Gradescope for implementing additional hand-written assignments: UCLA has a campus-wide license for Gradescope that integrates with Bruin Learn. One feature of this tool is that students can upload photos or PDFs of written work, and the system easily allows for streamlined grading (by question, by page) and digital commenting and rubrics.
You can watch UCLA's Will Conley, Department of Mathematics, provide a recorded overview of how to use Gradescope (approx. 56 mins). While we have now shifted to BruinLearn so the initial steps for linking to the gradebook will differ slightly, the within Gradescope interface is the same. At the 27 minute mark you can view what it looks like to see handwritten work submitted to Gradescope for streamlined digital grading by instructors and TAs.

Require students make a connection to class discussions: Prompt students to explicitly reference in-class discussions, lecture material and course readings in their homework assignments. (Example: Share three takeaways from our in-class discussion on the issues with how Covid-19 testing was implemented across the US at the onset of the pandemic.)

Communicate: Talk With Your Students About AI

Discuss the potential: Many of our students will go on to become leaders at organizations that utilize and/or develop new AI technologies. How will these tools support advancements in your field (medicine, science, art, music, humanities, health, and more)?

Prepare students for the future when they will work and interact with AI: This technology is likely to develop and become embedded in many parts of our lives. Preparing students to thoughtfully engage with it, co-create with it and be curious about and know how to interact with other technological developments as they occur.

Seize the opportunity to center the importance of critical thinking and digital literacy. Students will have the opportunity in the future to break the cycle of spreading disinformation, lack of journalistic integrity in news, and elevating accurate and factual research and scholarship. Emphasize the importance of digital literacy, research, and writing skills with students; connect students to library resources for research and writing. As educators, we have an obligation to help guide our students through many types of literacy, including digital media and AI literacy. UCLA's WI+RE has created the Understanding Misinformation: A Lesson Plan Toolkit, that is geared towards educators who want to prepare students to learn about misinformation. We can get students to vet information like experts.

Lean in to talking to your students about how learning happens: Learning happens when actively engaging with the course material, through conversations and dialogue leading to deepening conceptual understanding.

Discuss Academic Integrity with Your Students:
One of the main concerns instructors have expressed is how to uphold academic integrity and prevent the misuse of tools like ChatGPT (intentional or not). These concerns include:

Plagiarism (copying and pasting the response that the tool provides; running material through multiple AI generators to avoid detection)

Lack of proper citation of sources

Inaccurate, misleading, biased, false, or limited information in responses to question prompts. While some AI detectors have been recently developed, it is unclear how effective they will be long-term and early reports indicate that individuals can easily avoid detection through simple modifications to produced text.

The UCLA Student Conduct Code states, "Unless otherwise specified by the faculty member, all submissions, whether in draft or final form, to meet course requirements (including a paper, project, exam, computer program, oral presentation, or other work) must either be the Student's own work, or must clearly acknowledge the source." Unless an instructor indicates otherwise, the use of ChatGPT or other AI tools for course assignments is akin to receiving assistance from another person and raises the same concern that work is not the student's own. Please communicate this to your students, and consider incorporating this language into your syllabus.

Teaching Assistants will be seeking guidance on how to discuss ChatGPT with students and what to do if they suspect submitted work may be AI generated. In addition to talking with your students, make space for conversations with your TAs and other instructional team members to explore this topic and co-construct guidelines.
The ultimate decision and responsibility for how to teach about AI and the establishment of or revision of course policies related to its use lies with the instructor.
View this collaborative Google Doc: AI Examples and Resources to see examples ( including Syllabus Language and Policy Language) and resources curated by UCLA's teaching and learning community.

Discuss the ethical issues and limitations of AI

Facilitate discussions with your students on the impacts of spreading disinformation or biased information, lack of regulation of companies that develop these technologies, and other dangers. While students will likely still continue to use ChatGPT and other tools like it, it is crucial that our community has this shared understanding of both dangers and opportunities.

These technologies are not infallible and their accuracy is subject to a variety of factors, some listed below:

Prone to filling in replies with incorrect data if there is not enough information available on a subject.

Lack the ability to understand the context of a particular situation, which can result in inaccurate outputs.

Large, uncurated datasets scraped from the internet are full of biased data that then informs the models.

Data is collected from the past, it tends to have a regressive bias that fails to reflect the progress of social movements.

Our nation has yet to catch up to the regulation needed to prevent the potential for tremendous harm when false or biased information is taken as fact. Our community must continue to explore the value and innovation that can come from AI while simultaneously contributing to the dialog about these potential harms.
Share current examples of scholarly discussion on this topic.
As our own UCLA experts have shared with our instructional community during the recent UCLA Virtual Town Hall: What is ChatGPT and How Does it Relate to UCLA's Academic Mission, there are concerns about the ethics and practices around tools like ChatGPT. Algorithms can and do replicate and produce biased, racist, sexist, etc. outputs, along with incorrect and/or misleading information.

Additional Examples:

On Requiring the Use of ChatGPT: Creating an account to use ChatGPT requires sharing of personal information. Depending on context, the use of ChatGPT may also mean sharing student intellectual property or student education records with ChatGPT under their terms and conditions of use. Individual students may have legitimate concerns and therefore may be unwilling to create an account. Discuss these concerns and consider alternatives.

If you will be requiring use of ChatGPT, consider making this explicit in the syllabus (for a related example of considerations related to privacy, see Privacy Tips for Your Syllabus).

Protecting student privacy as required by FERPA: Academic records, such as examinations and course assignments, are considered a student record and protected by FERPA. For example, ChatGPT should not be used to draft initial feedback on a student's submitted essay that included their identifying information. Asking ChatGPT to respond to question prompts would not be a FERPA violation, as no student information is provided to ChatGPT.

ChatGPT is currently in the process of review through UCLA's Third Party Risk Management to understand where there will be gaps in accessibility and security.

As with any emerging technology, ChatGPT may not always be accessible by individuals with disabilities. Open a conversation with the Center for Accessible Education (CAE) for ideas on exploring accessible alternatives. As always, we encourage faculty to use this sample syllabus language to direct a student toward CAE to discuss their options for accommodations and support. Requests for support should be directed to caeintake@saonet.ucla.edu or the student's listed Disability Specialist on their accommodation letter.

As the technology evolves, there may be a cost to using it, so continuing to revisit your learning goals and activities with respect to access is a critical equity issue.

Examples and Resources

Featured UCLA Resources

WEBSITE: UCLA Online Teaching and Learning - Information on Chat GPT and AI - Resources from UCLA Online Teaching and Learning

CURATED RESOURCE LIST: Generative AI Tools and Resources - From Dr. Kim DeBacco, Senior Instructional Designer UCLA Online Teaching and Learning

SUBSTACK POST: "Carving out time to learn: A conversation with ChatGPT" - From Caroline Kong, Instructional Designer and Technologist at the Center for the Advancement of Teaching

RECORDED WEBINAR: "What's All the Buzz About ChatGPT and AI in Higher Ed?"

RECORDED WEBINAR: UCLA Virtual Town Hall: What is ChatGPT and How Does it Relate to UCLA's Academic Mission

RECORDINGS AND RESOURCES: AI in Action - Events brought to you by UCLA's Center for the Advancement of Teaching (CAT), the Center for Education, Innovation, and Learning in the Sciences (CEILS), the Excellence in Pedagogy and Innovative Classrooms program (EPIC), Online Teaching and Learning (OTL), the Bruin Learn Center of Excellence (CoE), the Writing Programs, and Humanities Technology (HumTech)

UCLA NEWSROOM ARTICLE: "Can AI and creativity coexist?" - In a joint interview, UCLA professors Jacob Foster and Danny Snelson discuss how chatbots could be used in teaching, offer historic analogs for the current AI explosion and opine about whether technology is actually capable of creativity.

Example Syllabi Language and Activity Ideas

View this collaborative Google Doc: AI Examples and Resources to see examples and resources curated by UCLA's teaching and learning community. This includes syllabus language, assignment ideas, and other strategies shared by instructors from UCLA and across the US.

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Students and instructors are embracing ChatGPT (GPT-4 as of March 2023) and similar artificial intelligence (AI) technologies across disciplines for different learning goals. There is no one-size-fits-all best practice for their use. This document is meant as a guideline for instructors on what to consider as these tools evolve.

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Stanford University - Generative AI policy guidance

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Stanford University - Generative AI policy guidance

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Absent a clear statement from an instructor, use of or consultation with generative AI shall be treated analogously to assistance from another person. In particular, using generative AI tools to substantially complete an assignment or exam (e.g. by entering exam or assignment questions) is not permitted. Students should acknowledge the use of generative AI (other than incidental use) and default to disclosing such assistance when in doubt.

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To give sufficient space for instructors to explore uses of generative AI tools in their courses, and to set clear guidelines to students about what uses are and are not consistent with the Stanford Honor Code, the BCA has set forth the following policy guidance regarding generative AI in the context of coursework:

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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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The Honor Council sent an email to all undergraduates on April 11th, 2023, 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 their classes. “Utilizing AI software to generate ideas and pass them off as one’s own will also be considered plagiarism and will be adjudicated as such by the Honor Council,” the email said. The email also clarifies that use of AI software “for your own study purposes is allowable.”

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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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Harvard Business School - Using ChatGPT and AI tools

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Harvard Business School - Using ChatGPT and AI tools

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Official policy from Harvard Business School. Faculty "may allow the use of ChatGPT and similar technologies in some circumstances". Citation is required and guidance will evolve as these technologies evolve.

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Student Handbook - MBA - Harvard Business School

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The Harvard Business School (HBS) MBA Student Handbook provides incoming and current MBA students the information necessary to successfully participate in the program. The Student Handbook also provides answers to many questions students have about academic and student life at HBS.

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A comprehensive AI policy education framework for university teaching and learning

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A comprehensive AI policy education framework for university teaching and learning

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This framework is organized into three dimensions: Pedagogical, Governance, and Operational.

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Abstract

This study aims to develop an AI education policy for higher education by examining the perceptions and implications of text generative AI technologies. Data was collected from 457 students and 180 teachers and staff across various disciplines in Hong Kong universities, using both quantitative and qualitative research methods. Based on the findings, the study proposes an AI Ecological Education Policy Framework to address the multifaceted implications of AI integration in university teaching and learning. This framework is organized into three dimensions: Pedagogical, Governance, and Operational. The Pedagogical dimension concentrates on using AI to improve teaching and learning outcomes, while the Governance dimension tackles issues related to privacy, security, and accountability. The Operational dimension addresses matters concerning infrastructure and training. The framework fosters a nuanced understanding of the implications of AI integration in academic settings, ensuring that stakeholders are aware of their responsibilities and can take appropriate actions accordingly.

Highlights

Proposed AI Ecological Education Policy Framework for university teaching and learning.

Three dimensions: Pedagogical, Governance, and Operational AI Policy Framework.

Qualitative and quantitative data collected from students, teachers, and staff.

Ten key areas identified for planning an AI policy in universities.

Students should play an active role in drafting and implementing the policy.

Introduction

In recent months, there has been a growing concern in the academic settings about the use of text generative artificial intelligence (AI), such as ChatGPT, Bing and the latest, Co-Pilot integrated within the Microsoft Office suite. One of the main concerns is that students may use generative AI tools to cheat or plagiarise their written assignments and exams. In fact, a recent survey of university students found that nearly one in three students had used a form of AI, such as essay-generating software, to complete their coursework (Intelligent.com, 2023). About one-third of college students surveyed (sample size 1000) in the US have utilized the AI chatbot such as ChatGPT to complete written homework assignments, with 60% using the programme on more than half of their assignments. ChatGPT types of generative AI tools is capable of imitating human writing, with some students using it to cheat. The study found that 75% of students believe that using the programme for cheating is wrong but still do it, and nearly 30% believe their professors are unaware of their use of the tool. The study also noted that some professors are considering whether to include ChatGPT in their lessons or join calls to ban it, with 46% of students saying their professors or institutions have banned the tool for homework. This has led to calls for stricter regulations and penalties for academic misconduct involving AI.

Another concern is that the use of generative AI may lead to a decline in students' writing and critical thinking skills (Civil, 2023; Warschauer et al., 2023), as they become more reliant on automated tools to complete their work. Some academics argue that this could have a negative impact on the quality of education and ultimately harm the students' learning outcomes (Chan & Lee, 2023; Korn & Kelly, 2023; Oliver, 2023; Zhai, 2022).

These concerns have led some universities to ban the use of generative AI in their academic programmes. Eight out of 24 universities in the prestigious UK Russell Group have declared the use of the AI bot for assignments as academic misconduct including Oxford and Cambridge. Meanwhile, many other universities around the world, are rushing to review their plagiarism policies citing concerns about academic integrity (Wood, 2023; Yau & Chan, 2023). Some Australian universities have had to alter their exam and assessment procedures back to pen- and paper-based (Cassidy, 2023; Cavendish, 2023).

However, there are also those who argue that generative AI has the potential to revolutionize education and enhance the learning experience for students. For example, some experts suggest that generative AI could be used to provide personalized feedback and support to students, helping them to identify areas of weakness and improve their skills in an adaptive manner (Kasneci et al, 2023; Sinhaliz et al., 2023).

Generative AI and generative pre-trained transformers

Generative AI is a subset of artificial intelligence (AI) that focuses on creating new data or content rather than analysing and interpreting existing data (McKinsey Consultant, 2023). Generative Pre-trained Transformers (GPT) are a type of generative AI model that use deep learning techniques to generate natural language text. The latest versions of GPT, GPT-3.5 and GPT-4, large language models which are trained on a large corpus of text data, are capable of producing human-like text with high levels of coherence, complexity, and diversity. GPT-3.5 and GPT-4 are both examples of artificial general intelligence (AGI), which is the ability of AI systems to perform any intellectual task that a human can do. Unlike artificial narrow intelligence (ANI), which is designed to perform a specific task, AGI is designed to perform multiple tasks and generalize knowledge across different domains. While GPT-3.5 and GPT-4 are not true AGI systems, they represent significant progress towards achieving AGI by demonstrating the ability to perform a wide range of language tasks and generate human-like text. The development of generative AI models like GPT-3.5 and GPT-4 has the potential to revolutionize many fields, including natural language processing, creative writing, and content generation.

Rationale for an artificial intelligence education policy

With generative AI tools becoming easily accessible to the public in recent months, they are rapidly being integrated into various fields and industries. This has created an urgent need for universities to develop an AI education policy that prepares students to work with and understand the principles of this technology. There are several rationales supporting this need.

Firstly, AI technology is becoming more prevalent in many sectors of the economy, such as finance (Bholat & Susskind, 2021; Buckley et al., 2021), healthcare (Eggmann et al., 2023; Yu et al., 2018), and transportation (Abduljabbar et al., 2019; Wu et al., 2022). As a result, graduates will need to have a strong understanding of AI principles in order to succeed in these fields. An AI education policy can provide students with the necessary knowledge and skills to work with AI in a professional capacity.

Secondly, AI has the potential to revolutionize many aspects of society, including education itself (Adiguzel et al., 2023). AI can be used to enhance student learning by providing personalized, real-time feedback and adapting to individual learning styles (Atlas, 2023; Chan & Hu, 2023; Luckin, 2017). By educating students on AI, universities can help prepare them to be active participants in the development and implementation of AI technology, ensuring that it benefits society as a whole.

Thirdly, as the use of AI in education and assessment becomes more prevalent, it is essential that students understand the principles behind the technology in order to maintain academic integrity and prevent cheating as mentioned previously (Chan, 2023; Cotton et al., 2023). An AI education policy can teach students about the ethical considerations surrounding AI, such as bias and fairness, as well as the potential consequences of using AI in academic contexts.

Fourthly, developing an AI education for university is important to prepare students for the future (Aoun, 2017). AI technology is rapidly advancing, and it is likely to play an increasingly important role in society in the coming years. By providing students and teachers with training in AI, universities can help ensure that graduates are equipped to contribute to the development of AI and to navigate the ethical, social, and economic issues that are likely to arise as AI becomes more widespread. Such training should also help students become competent and responsible users of AI in their daily lives.

Finally, it is also worth mentioning that previous AI policies in education did not anticipate the level of advancements that text-based GPT 3.5 and 4 can now achieved. Given the potential benefits and risks associated with the use of generative AI in education, it is important to develop a proper AI education policy that addresses these concerns and provides guidance on the responsible use of AI.

Conducting research in AI policy in education within Hong Kong is specifically justified due to the city's unique position as a global hub of technology, commerce, and education, coupled with its evolving education landscape. As a dynamic metropolis with a strong commitment to technological innovation and a richly diverse education system, Hong Kong presents a compelling case study for the exploration of AI policies in education. Its blend of Eastern and Western educational philosophies and practices offers a fertile ground for examining the impacts and opportunities of AI integration in varied educational contexts. Furthermore, as Hong Kong is actively striving to enhance its digital learning capabilities and infrastructure, studying AI policy could provide valuable insights into the challenges and best practices of implementing AI in education, thereby potentially informing AI education strategies not only in Hong Kong but also in other global contexts.

The study employed a comprehensive approach to data collection, gathering rich quantitative and open-ended survey data from a diverse range of stakeholders in the education community to ensure that it reflects the needs and values of all those involved. The combination of these data sources allowed for a holistic understanding of the topic under investigation, providing a nuanced and multifaceted view of the issues at hand. By doing so, we can help to ensure that the use of generative AI in education is both beneficial and ethical.

Existing policy on artificial intelligence

The aim of this study is to investigate the education policy related to AI, however, it is essential to also scrutinize the existing policies governing AI as a whole. As AI expands its sphere of influence to various sectors in our society, there are increasing concerns over the risks of its usage and how it might impact human activities (AI regulation, 2023; World Economic Forum [WEF], 2023). Some of the major issues of concern that have drawn the attention of governments around the world include discrimination and bias of AI, loss of privacy, violation of human rights, and malicious use of AI (Greiman, 2021; Hogenhout, 2021). Federspiel et al. ( 2023) caution that misuse of AI could encourage manipulation of people, create social division, and exacerbate inequalities, posing existential threats to the human race. In view of this, countries have been working on national policies and strategies to provide clearer guidance on AI usage in order to maximize its benefits while mitigating the threats brought by it.

To advocate the responsible and proper management of AI technologies, the centre of focus for most national policies on AI have fallen on the discussion of ethics, which deals with "the standards of right and wrong, acceptable and not acceptable" (Hogenhout, 2021, p. 11). Floridi ( 2021)'s framework for the ethical use of AI, which proposed the 5 core principles of "beneficence, non-maleficence, autonomy, justice and explicability", is referred to by most national policies on AI as a foundation to further develop on. In addition, Dexe and Franke ( 2020) summarized the AI strategy documents from the Nordic countries and identified various ethical principles as the implicit foundation for further developing policies. The official AI governance framework from Singapore also recognized the "explainable, transparent and fair usage of AI in decision-making process" and "human-centric AI solutions" as the guiding principles of ethical use of AI (IMDA & PDPC, 2020). Apart from individual countries, ethics has been the emphasis of the AI policies published by regional and international bodies. UNESCO developed its guidelines on the ethical use of AI technologies by emphasizing the key idea of human-centeredness and hence, human rights and values laid out in the Universal Declaration of Human Rights (UDHR) are advised to be adopted as the necessary foundation to further promote beneficial and appropriate use of AI technologies (UNESCO, 2021b, 2023). AI strategy in the European Union, as Renda's ( 2020) analysis pointed out, also focused on ethics and highlighted a human-centric approach to AI. In order to protect EU citizens from the danger of abusive use of advanced technologies, EU proposed its own pillars (legal compliance, ethical alignment and sociotechnical robustness) to ensure the trustworthiness of AI and established a specific AI expert group to work on specific policy recommendations and guidelines.

The heavy focus that these national and regional policies has placed on ethics demonstrates how limited they can do for the implementation of AI technologies. On the one hand, difficulty to lay down a universal definition on ethical principles becomes a hinderance for certain countries in formulating policies on the use of AI (Dexe & Franke, 2020). On the other hand, as AI can weave into the fabrics of everyday human activities, the resulting wide coverage of policy areas ranging from governance to education and even to environment makes it a challenging task for government to establish specific policies on AI usage (UNESCO, 2021b). Thus, as the Singaporean AI governance framework highlighted, model framework or ethical guidelines were in themselves directional and for reference only, and AI practitioners need to consider them with flexibility and according to the relevance of particular situations (IMDA & PDPC, 2020).

Moving forward, the ongoing efforts of national and international organizations to ensure the positive implementation of AI technologies will continue to prioritize discussions and the formulation of legal and ethical principles (AI regulation, 2023; UNESCO, 2023). However, until these principles are validated by real-time implementation of AI technologies, they will remain primarily predictive and prescriptive in nature (Chatterjee, 2020). Over time, it may become necessary for countries to establish institutional support systems to effectively manage AI practices in accordance with validated legal and ethical guidelines (Renda, 2020).

Below consists of a compilation of fundamental ethical principles for AI that have been extracted from multiple policies (IMDA & PDPC, 2020) (Table 1).

Table 1 Compilation of fundamental ethical principles (IMDA &PDPC, 2020)

Existing policy on AI in education

The integration of AI technologies into teaching and learning has begun as early as the 1970s and nowadays, different forms of these technologies are used in various educational contexts, such as the use of personalized applications for learning and assessment and information systems that help handle administrative and management tasks in schools (Al Braiki et al., 2020; Schiff, 2022; UNESCO, 2021a).

As mentioned above, the use of AI technologies has raised different issues of concern (Chan & Tsi, 2023; Chan & Zhou, 2023). In the educational contexts, other than the general risks brought by the use of AI, concerns are primarily centred on issues such as what changes can AI bring to the design of assessment and curriculum, equalities and universality in accessing these technologies, redefinition of teachers' role, and the lack of technological infrastructure for emerging economies (Pelletier et al., 2022; Popenici & Kerr, 2017; Swiecki et al., 2022; TEQSA, 2023; UNESCO, 2021a). Based on these concerns, AI policies in education fix their eyes on addressing a number of issues: literacy education to prevent inequalities in the use of digital technologies (Southgate, 2020; UNESCO, 2021b); essential values of traditional forms of teaching and learning such as teacher-student and student-student relationships (Luan et al., 2020; UNESCO, 2021b); inclusiveness and equity in the use of AI technologies (Tanveer et al., 2020; UNESCO, 2021a); professional development of teachers to enhance teachers' competence and assist them to transform their roles (Ocaña-Fernández et al., 2019; Wang et al., 2021); and training and enhancement of skills or "micro-credentials" for students that are important and necessary for harnessing technologies (Pelletier et al., 2022; UNESCO, 2021a). The roles of literacy education and skills training are having particular implications for the wider society as the population in general also needs to be prepared for the implementation of AI technologies in different sectors.

Despite identifying multiple issues of concern in the educational contexts, policies on AI in education are mostly generic and implicit because of the lack of concrete evidence of implementing AI technologies (UNESCO, 2021a). In Schiff ( 2022)'s review on 24 AI policy strategies focusing on the role of education in global AI policy discourse, it was found that policymakers view education largely as an instrumental tool to support workforce development and training of AI experts. The article finds that the use of AI in education is largely absent from policy conversations, while the instrumental value of education in supporting an AI-ready workforce and training more AI experts is overwhelmingly prioritized. The article suggests that if such a trend continues, policymakers may fail to realize AI in education's transformative potential and may fail to sufficiently fund, regulate, and consider AI in education's ethical implications. AI scholarship and education governance do not receive adequate attention in the current literature (Gellai, 2022), and public understanding of the policy implications of AI is limited (Feldstein, 2019). While more work is still to be done in order to formulate more comprehensive and focused policy documents on AI in education, ethics was reiterated again as a strategically plausible starting point for further discourse and researchers were especially encouraged to engage further with policymakers through their work on ethics in the use of AI in education (Sam & Olbrich, 2023; Schiff, 2022). In view of this gap, this research intends to propose a policy framework for integrating AI in higher education, taking into consideration aspects of teaching and learning as well as ethical and practical concerns.

In this research, we will employ the guidelines put forth by UNESCO ( 2021a) as the starting point for crafting a more accurate AI policy for university teaching and learning. The rationale for employing UNESCO recommendations as the basis is multifaceted. First, UNESCO is an esteemed international organisation with significant expertise in education, their recommendations are supported by thorough research and knowledge from experts worldwide. These recommendations are designed to be relevant and flexible for a variety of educational systems and cultural settings, making them suitable for diverse institutions. UNESCO's guidelines also take a comprehensive approach to incorporating AI in education, addressing important ethical, social, economic, and technological aspects essential for creating effective policies. Using an existing framework like UNESCO's recommendations saves time and resources and provides a well-organized starting point for examining specific AI policy issues in university teaching and learning. Finally, anchoring the study in UNESCO's recommendations enhances the credibility of the research.

The UNESCO framework for AI in education is centred around a humanistic approach, which aims to safeguard human rights and provide individuals with the necessary skills and values for sustainable development, as well as effective human-machine collaboration in life, learning, and work. The framework prioritizes human control over AI and ensures that it is utilized to improve the capabilities of both teachers and students. Moreover, the framework calls for ethical, transparent, non-discriminatory, and auditable design of AI applications. From the UNESCO's AI and Education: Guidance for Policy-Makers document, the following recommendations are provided:

1. 1.
Interdisciplinary planning and inter-sectoral governance: This recommendation suggests that AI and education policies should be developed through collaboration between different sectors and disciplines to ensure a comprehensive approach. For example, policymakers could work with experts in education, technology, ethics, and other relevant fields to develop policies that take into account all aspects of AI in education.

2. 2.
Policies on equitable, inclusive, and ethical use of AI: This recommendation emphasizes the importance of ensuring that AI is used in an ethical and inclusive manner that benefits all learners. For example, policymakers could develop policies that address issues such as bias in AI algorithms or access to AI tools for learners from disadvantaged backgrounds.

3. 3.
Develop a master plan for using AI for education management, teaching, learning, and assessment: This recommendation suggests that policymakers should develop a comprehensive plan for using AI in various aspects of education to ensure its effective implementation. For example, a master plan could include specific goals for using AI in areas such as personalized learning or teacher professional development.

4. 4.
Pilot testing, monitoring and evaluation, and building an evidence base: This recommendation highlights the importance of testing and evaluating the use of AI in education through pilot projects to build an evidence base for its effectiveness. For example, policymakers could fund pilot projects that test the use of AI tools in specific educational contexts or with specific learner populations.

5. 5.
Fostering local AI innovations for education: This recommendation suggests that policymakers should encourage the development of local innovations in AI for education to ensure that it meets the specific needs of their communities. For example, policymakers could provide funding or support to local startups or research institutions working on developing new AI tools or applications specifically designed for their region's educational needs.

Using UNESCO's recommendations as a basis, this study aims to examine higher education stakeholders' perceptions of text generative AI technology. Based on their ideas, recommendations, and concerns, an AI education policy framework will be developed to promote ethical and effective integration of AI technologies in higher education.

Methodology

In this study, a survey design was utilized to gather data from students, teachers, and staff in Hong Kong to develop AI education policy framework for university teaching and learning. The survey was administered through an online questionnaire, featuring a mix of closed-ended and open-ended questions. The questionnaire was designed based on a review of current literature on AI use in higher education. Topics covered in the survey were major issues concerning the use of AI in higher education, which included the use of generative AI technologies like ChatGPT, the integration of AI technologies in higher education, potential risks associated with AI technologies, and AI's impact on teaching and learning.

Data were collected via an online survey from a diverse group of stakeholders in the education community, ensuring that the results reflect the needs and values of all participants. A convenience sampling method was employed for selecting the respondents, based on their availability and willingness to participate in the study. Participants were recruited through an online platform and provided with an informed consent form prior to completing the survey.

The survey was completed by 457 undergraduate and postgraduate students, as well as 180 teachers and staff members across various disciplines in Hong Kong. Descriptive analysis was used to analyse the survey data, while a thematic analysis approach was applied to examine the responses from the open-ended questions in the survey.

Quantitative data (survey data) and descriptive analysis

A range of survey items was included to capture different aspects of participants' usage and perception of generative AI technologies like ChatGPT. For example, participants were asked whether they have used ChatGPT or similar generative AI technologies before and how they envision using these technologies in their teaching and learning practices.

Descriptive analysis was employed to analyse the survey data collected from students and teachers in Hong Kong, in order to gain a better understanding of the usage and perception of generative AI technologies like ChatGPT in higher education. Descriptive analysis is an appropriate statistical method for summarizing and describing the main characteristics of the sample and the data collected. It is particularly useful for analysing survey data and can provide an overview of the distribution, central tendency, and variability of the responses.

Qualitative data (open-ended data) and thematic analysis

Aside from the quantitative part of the survey, respondents were also asked about their apprehensions regarding the integration of generative AI technologies and their recommendations for university strategic plans through open-ended questions. Different perspectives and experiences were gathered from the respondents across various disciplines. The data from the open-ended questions were analysed using a thematic analysis approach, which involved identifying patterns and themes in the data. An inductive approach was used to analyse the responses, where the themes emerged from the data rather than being predetermined by the researcher.

The combination of the quantitative and qualitative data enabled a more holistic understanding of the usage and perception of generative AI technologies in higher education. This allowed for the pinpointing of potential requirements, recommendations, and strategies for AI policy in university teaching and learning. This understanding is essential for ensuring that the use of these technologies is both beneficial and ethical.

Results

Findings from the quantitative data

The survey was conducted among 457 students and 180 teachers and staff from different disciplines in Hong Kong universities. The goal was to explore the kinds of requirements, guidelines and strategies necessary for developing AI policies geared towards university teaching and learning. The findings reveal valuable insights into the perception of generative AI technologies like ChatGPT among students and teachers (refer to Table 2).

Table 2 Descriptive analysis for quantitative results

Regarding the usage of generative AI technologies, both students (mean = 2.28, SD = 1.18) and teachers (mean = 2.02, SD = 1.1) reported relatively low experience, suggesting that there is significant room for growth in adoption. Both groups demonstrated a belief in the positive impact of integrating AI technologies into higher education (students: mean = 4, SD = 0.891; teachers: mean = 3.87, SD = 1.32). This optimism was also reflected in the strong agreement that institutions should have plans in place associated with AI technologies (students: mean = 4.5, SD = 0.854; teachers: mean = 4.54, SD = 0.874).

Both students and teachers were open to integrating AI technologies into their future teaching and learning practices (students: mean = 3.93, SD = 1.09; teachers: mean = 3.92, SD = 1.31). However, there were concerns among both groups about other students using AI technologies to get ahead in their assignments (students: mean = 3.67, SD = 1.22; teachers: mean = 3.93, SD = 1.12). Interestingly, both students and teachers did not strongly agree that AI technologies would replace teachers in the future (students: mean = 2.14, SD = 1.12; teachers: mean = 2.26, SD = 1.34).

The respondents acknowledged the importance of learning to use generative AI technologies well for their careers (students: mean = 4.07, SD = 0.998; teachers: mean = 4.1, SD = 1.08). However, both groups expressed doubt about teachers' ability to accurately identify a student's usage of generative AI technologies for completing assignments (students: mean = 3.02, SD = 1.56; teachers: mean = 2.72, SD = 1.62).

The responses to the remaining questions suggest that students and teachers recognize potential benefits and drawbacks of AI technologies, including providing guidance and personalized feedback, improving digital competence and academic performance, and offering anonymity in student support services. However, there were concerns about over-reliance on AI technologies, limited social interaction, and the potential hindrance to the development of generic skills.

These findings highlight the need for a comprehensive AI policy in higher education that addresses the potential risks and opportunities associated with generative AI technologies. Based on these findings, some implications and suggestions for university teaching and learning AI policy include:

1. 1.
Training: Providing training for both students and teachers on effectively using and integrating generative AI technologies into teaching and learning practices.

2. 2.
Ethical Use and Risk Management: Developing policies and guidelines for ethical use and risk management associated with generative AI technologies.

3. 3.
Incorporating AI without replacing human: Incorporating AI technologies as supplementary tools to assist teachers and students, rather than as replacements for human interaction.

4. 4.
Continuously Enhancing Holistic Competencies: Encouraging the use of AI technologies to enhance specific skills, such as digital competence and time management, while ensuring that students continue to develop vital transferable skills.

5. 5.
Fostering a transparent AI environment: Fostering a transparent environment where students and teachers can openly discuss the benefits and concerns associated with using AI technologies in higher education.

6. 6.
Data Privacy and security: Ensuring data privacy and security while using AI technologies.

Overall, the survey results indicate an openness to adopting generative AI technologies in higher education and a recognition of the potential advantages and challenges. Addressing these issues through informed policy and institutional support will be crucial for maximizing the benefits of AI technologies in university teaching and learning.

Findings from the qualitative data

The qualitative data collected from students, teachers, and staff yielded valuable and rich suggestions and comments. There are 10 main themes and 25 subthemes that emerged from the qualitative data as presented in Table 3. From the data, we identified ten key areas (i.e., the main themes) that are directly relevant to the planning of an AI policy for teaching and learning in universities. These areas align well with the quantitative data and are as follows:

Table 3 Main themes and subthemes of qualitative data

To address academic misconduct, universities must develop clear guidelines and strategies for detecting and preventing the misuse of generative AI. Teachers emphasize the importance of creating university-wide policies on how to test students suspected of using AI to complete tasks in which AI use is prohibited or misused. As one student stated, " A clear set of rules about what happens if AI is used and resources on informing students about the rule set are needed." They also suggested, " Clearly stipulate in which areas generative AI technologies are allowed and which are not. What are the procedures to handle suspended cases? What are the consequences?" Another student mentioned that " the level of restriction should be clarified." Both teachers and students have also suggested the use of assessments that minimize opportunities for AI misuse, such as oral examinations or controlled settings where internet access is limited, to help maintain academic integrity. Both teachers and students have also questioned " what is the definition of cheating? " in this AI era.

Teachers highlight the importance of identifying ethical dilemmas and recommend familiarizing students with ethical issues, such as the boundaries between plagiarism and inspiration and appropriate situations for seeking help from AI. Establishing clear policies around AI use, including ethical guidelines and legal responsibilities, will help students and staff navigate these complex issues. One teacher noted, " The education on academic and research ethics should be strengthened." Explicitly stipulating the areas where AI is allowed and the procedures for handling suspected cases of misuse will help maintain a transparent and equitable learning environment.

Universities must take responsibility for decisions made regarding the use of generative AI in teaching and learning, which includes being transparent about data collection and usage, and being receptive to feedback and criticism. By disclosing information about the implementation of generative AI, including the algorithms employed, their functions, and any potential biases or limitations, universities can foster trust and confidence among students and staff in AI technology usage. Teachers emphasize the importance of addressing ethical concerns, privacy, security, and other related issues when using generative AI technologies. Teachers commented " In general, its impact is inevitable. It may negatively affect social consciousness and responsibility. Depending on climate change management and its consequences, it may contribute to the demise of a significant portion of humanity. It may also protect and advance the interests of those who benefit from chaos. "

Privacy and Security: AI technologies rely on vast amounts of data, which raises concerns about privacy and security if the data is not adequately protected. " Institutions should ensure that the data used by generative AI technologies is kept private and secure. This includes ensuring that any data used in training or testing the technology is de-identified, and that appropriate security measures are in place to prevent unauthorized access or use of data. "

Transparency and Accountability: Universities should be transparent about the use of generative AI in teaching and learning, which includes disclosing information about the algorithms and their functions, as well as any potential biases or limitations of the AI tools. " It is essential to recognize ethical dilemmas and consider privacy, security, and related issues when employing generative AI technologies. "

The complexity of AI technologies can make it difficult to hold organizations and individuals accountable for their decisions and actions. Institutions should address ethical issues, such as potential discrimination, bias, and stereotypes, while ensuring data privacy and security.

To ensure the success of AI integration in university teaching and learning, continuous monitoring and evaluation of its implementation are necessary. Teachers recommend conducting longitudinal experiments in different areas to better understand how AI affects students' learning processes and outcomes. Regular assessments of AI's impact on teaching practices and student performance will help identify areas for improvement and ensure that the technology is being used effectively and ethically. One student mentioned, " The plan should include more experiments on conducting the AI technologies on teaching. " By regularly collecting feedback from both teachers and students, universities can make informed decisions about how to improve AI implementation. Evaluating the effectiveness of AI tools in enhancing learning outcomes is vital in determining their value and making adjustments as needed.

Ensuring equitable access to AI technologies is crucial for fostering an inclusive learning environment. Universities should work to provide resources and support to all students and staff, regardless of their background or access to technology. This may involve the procurement of AI tools, including AI detectors, for use by the entire university community. By promoting equal access to AI technologies, universities can help level the playing field and ensure that all students and staff have the opportunity to benefit from the advantages offered by AI integration.

Equal access to AI technologies is essential for maintaining fairness in the educational environment. One teacher commented, " Same as all other resources, to incorporate this into current industries (especially education), fairness should be a top priority. If the usage involves any kind of competition, e.g. access to ChatGPT should be equal for all involved parties." Another student highlights " Ethical dilemma includes ensuring that the technology is not used to discriminate against individuals or groups, and that it does not reinforce bias or stereotypes. " Universities should consider how to ensure that all students have access and training to AI tools and resources, regardless of their socio-economic backgrounds, in order to level the playing field and promote inclusivity.

Attribution is an important aspect of AI policy in university teaching and learning. One student remarked, " They are welcome to use AI for academic purposes while requiring students to state clearly which part was helped by AI. This is similar to the references and citation of current academic practice." By requiring students to attribute AI-generated content, universities can promote academic integrity and ensure that AI technologies are used ethically in the learning process. Furthermore, there is a need for guidelines on how to fairly attribute generative AI's contribution to student work. " Ethics of use, knowledge of affordances, effective use, critique/evaluation of outputs, and role/integration in workflows/product in study and professional settings " may be included in the attribution.

To ensure successful integration of AI in teaching and learning, universities must provide adequate training and support for teachers, staff, and students. Teachers express concerns about coping with this new trend, helping students use AI effectively, and learning from student usage. As one teacher puts it, " Staff and students need an educative approach to its ethical use." Investing in training and resources can help educators feel more confident and capable in navigating the complexities of AI in their classrooms. This is supported by many students and teachers who believe that institutions should provide training to faculty and staff on the appropriate use of generative AI technologies in teaching and learning. " This training should include information on selecting appropriate technologies, using them effectively, and managing the risks associated with their use," they say. In addition, " teaching students how to use the technology and how to critique it is probably central to successfully planning for the integration of AI in education." Students suggest that " teaching students the potential of using generative AI properly and critically can benefit from students using AI hiddenly" and " relying on tools able to detect the use of a generative language model, while being aware of the limits of such tools. "

AI literacy is crucial for both students and staff as they navigate the use of generative AI in teaching and learning. Teachers emphasize the need for education on ethics, knowledge of AI tool affordances, effective use (e.g., prompt engineering), critique and evaluation of outputs, and the role of AI in study and professional settings. A comprehensive AI literacy programme will help students and staff better understand and responsibly utilize AI technologies in their academic and professional lives. By providing training and resources on AI technologies, universities can empower students and staff to make informed decisions about their use and potential applications in teaching and learning.

The integration of generative AI in education calls for a re-evaluation of assessments and examinations. Teachers suggest designing assessments that allow AI technologies to enhance learning outcomes, rather than solely producing outputs. For example, one teacher recommends " Promote assessments and activities where students can by themselves discover the limits of such techniques-and relativize the idea that they could be useful to 'cheat'." This shift may necessitate the development of new assessment methods that balance the benefits of AI with the need to maintain academic integrity. A student stated, " Change of assessment methods to measure the true 'understanding' of students instead of the ability to collect information (which can easily be done with AI tools)." Universities may need to develop new assessment strategies that focus on students' understanding, critical thinking, and analysis to prevent AI-generated content from compromising the assessment process. A teacher noted that " ... it is hard to assess most of them, so we fall back on regurgitation ", a change is necessary.

A balanced approach to AI adoption in university teaching and learning involves recognizing both the potential benefits and limitations of generative AI technologies. One teacher suggests, " Be positive about this technological evolution and incorporate it to develop new assignments and assessment." This approach requires flexibility, striking a balance between embracing new technology for its potential to enhance efficiency and productivity while maintaining a focus on critical thinking and ethical considerations. It is also important to encourage a balanced approach to AI adoption to avoid over-reliance on these technologies. " We should learn how AI can assist us, but not replace schoolwork," one teacher advised. This approach involves using AI technologies as complementary tools to support learning rather than relying on them as a substitute for traditional teaching methods. Students should be encouraged to use AI as an aid to their learning process and not solely depend on it for academic success.

Preparing students for an AI-driven workplace involves teaching them how to use AI responsibly, ethically, and effectively. Universities should develop curricula that reflect the increasing prominence of AI in various industries, ensuring that students are equipped with the skills and knowledge to navigate the evolving workplace landscape. This includes teaching students how to integrate AI into their workflows, evaluate the effectiveness of AI tools, and understand their role in professional settings. As one teacher notes, " Teaching students how to use it properly and understanding its limitations and strengths would be useful."

Integrating AI technologies into teaching and learning involves familiarizing students with AI tools they will likely encounter during their university studies and in the workplace, as mentioned by a student who said, " Teach students how to best use AI tools and make AI tools a common part of education, just like PowerPoint and Excel. " Teachers suggest guiding students to recognize ethical issues and helping them self-appropriate AI in study and work settings.

As the workplace increasingly adopts AI technologies, universities should prepare students for this shift. One student stated, " Plans should be implemented to assist students in making better and more constructive use of AI in learning, career planning, and personal development. "

Teachers have highlighted the importance of enhancing critical thinking, digital literacy, information literacy, and professional ethics among students to help them make effective and ethical use of AI technologies. To harness the potential of generative AI technologies, teachers advocate for an emphasis on teaching students to assess the reliability of content, understand biases, and evaluate the accuracy and relevance of AI-generated information. One teacher suggests that " Enhancement on critical thinking among students is definitely a must, in order to make good use of such AI technologies." Another teacher emphasizes the importance of " Teaching students how to use the technology, and how to critique it, is probably central to successfully planning for the integration of AI in education. "

To successfully embrace generative AI technologies, universities should prioritize fostering critical thinking among students. One student suggested, " Plans on how to maintain students' interest and motivation to engage in deep and critical thinking, diversify perspectives and expand horizons. "

Developing holistic competencies and generic skills in students is an essential goal of education. " However there are many things, even far more important things, to education...: the education of character; rhetoric and analytical skills; public speaking; creativity; memorisation; all embodied skills," a teacher argued. Incorporating AI technologies into teaching and learning may hinder students' development of competencies such as teamwork, leadership, empathy, and creativity skills. Therefore, universities need to continuously find opportunities for students to develop these skills, preparing them for the AI-driven workplace where they need to be adaptable, resilient, and transformational.

Discussion

Triangulating quantitative and qualitative data

The quantitative findings support the key areas found in the qualitative data for AI integration in education. The quantitative data reveals that both students and teachers share concerns about the potential misuse of AI technologies, such as ChatGPT, in assignments (students: mean 3.67, teachers: mean 3.93). This emphasizes the need for guidelines and strategies to prevent academic misconduct. Furthermore, there is significant agreement among students and teachers on the necessity for higher education institutions to implement a plan for managing the potential risks associated with using generative AI technologies (students: mean 4.5, teachers: mean 4.54), highlighting the importance of addressing data privacy, transparency, accountability, and security. The overall positive perception of AI technologies integration within education implies that proper policies should be in place to ensure responsible AI incorporation in higher education.

The concern that some students might use generative AI technologies to gain an advantage in their assignments (students: mean 3.67, teachers: mean 3.93) underscores the importance of ensuring equal access to AI technologies for all students. Moreover, the consensus that students must become proficient in using generative AI technologies for their careers (students: mean 4.07, teachers: mean 4.1) highlights the need for AI literacy and training for all stakeholders in the educational process, preparing students for the AI-driven workplace.

Interestingly, teachers and students are unsure if teachers can accurately identify a student's use of generative AI technologies to partially complete an assignment (students: mean 3.02, teachers: mean 2.72), yet they also believe that AI technologies can provide unique insights and perspectives and personalized feedback. This suggests that rethinking assessment methods may be necessary.

Data indicating that neither students nor teachers believe AI technologies will replace teachers in the future (students: mean 2.14, teachers: mean 2.26) supports the need for a balanced approach to AI adoption, utilizing AI technologies as complementary tools rather than substitutes for traditional teaching methods. Finally, concerns that generative AI technologies could hinder students' development of generic or transferable skills, such as teamwork, problem-solving, and leadership (students: mean 3.3, teachers: mean 3.74), emphasize the importance of focusing on students' holistic competencies and generic skills in preparation for the AI-driven workplace.

Key areas versus UNESCO's recommendations on AI education policy

The original plan for the study was to use UNESCO's recommendations as a basis for developing AI education policy framework for university teaching and learning through inputs from various stakeholders to identify any gaps in the framework and modified accordingly. Although the recommendations from UNESCO can provide a high-level guideline for this study, it was clear that there are several key differences between UNESCO recommendations and the ten key areas that were identified for integrating AI in university teaching and learning.

As the UNESCO's AI and Education: Guidance for Policy-Makers was written before the availability of GPT 3.5 and 4, the recommendations would not have fully addressed the current opportunities and threats of the advances in the GPT technologies for education.

Moreover, the UNESCO recommendations are intended for education in general and do not specifically cater to the needs of university teaching and learning. The UNESCO recommendations are high-level and general aimed at helping policy-makers better understand the possibilities and implications of AI for teaching and learning to help achieve Sustainable Development Goal 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all (UNESCO, 2021a), while the ten key areas are more specific, practical, and tailored to university teaching and learning. Furthermore, most of the existing AI education policies tend to emphasize the instrumental role of AI for workforce development (Schiff, 2022), but the key areas based on the findings of this study also address the transformative potential and ethical considerations relating to AI use in higher education in addition to its instrumental purposes.

The ten key areas were developed based on direct input from stakeholders who have vested interests in university teaching and learning, which makes them more relevant and grounded in practice. For example, UNESCO's recommendation to develop a master plan for using AI for education management, teaching, learning, and assessment emphasizes the need for a comprehensive and strategic approach to integrating AI in various aspects of education. This includes not only teaching and learning but also the broader aspects of education management, such as administration, resource allocation, and policy development. The focus here is on creating an overarching framework that guides the implementation of AI in education as a whole. On the other hand, the key area derived from the qualitative findings of rethinking assessments and examinations delves deeper into a specific aspect of education: the evaluation of students' learning. This area acknowledges that the integration of generative AI in education necessitates a re-evaluation of traditional assessment methods. The focus here is on designing assessments that allow AI technologies to enhance learning outcomes while maintaining academic integrity. This involves developing new assessment strategies that focus on students' understanding, critical thinking, and analysis rather than just their ability to collect information. In short, UNESCO's recommendations highlight the "what", whereas the ten key areas of this study detail the "how" of AI education policy.

In essence, the difference between these two areas lies in their scope and focus. UNESCO's recommendation is broader, encompassing various aspects of education and advocating for a comprehensive master plan. Its primary target is policymakers. In contrast, the key area on rethinking assessments and examinations is more specific and user centred, catering for various higher education stakeholders including students and teachers as well as addressing the challenges and opportunities associated with AI integration in university teaching and learning.

AI ecological education policy framework

In order to turn policy recommendations into action plans, the ten key areas have been further organised into three dimensions-Pedagogical, Ethical, and Operational into the AI Ecological Education Policy Framework. Each dimension is led by a responsible party (see Fig. 1). This framework allows for a more nuanced understanding of the multifaceted implications of AI integration in university settings and ensures that stakeholders consider the broader context of AI adoption and its impact on various aspects of teaching and learning.

AI ecological education policy framework

Pedagogical dimension (teachers)

This dimension focuses on the teaching and learning aspects of AI integration. It includes the following key areas:

1. a.
Rethinking assessments and examinations

2. b.
Developing student holistic competencies/generic skills

3. c.
Preparing students for the AI-driven workplace

4. d.
Encouraging a balanced approach to AI adoption

Governance dimension (senior management)

This dimension emphasizes the governance considerations surrounding AI usage in education. It encompasses the following key areas:

1. a.
Understanding, identifying, and preventing academic misconduct and ethical dilemmas

2. b.
Addressing governance of AI: data privacy, transparency, accountability, and security

3. c.
Attributing AI technologies

4. d.
Ensuring equity in access to AI technologies

Operational dimension (teaching and learning and IT staff)

This dimension concentrates on the practical implementation of AI in university settings. It includes the following key areas:

1. a.
Monitoring and evaluating AI implementation

2. b.
Providing training and support for teachers, staff, and students in AI literacy

For the Pedagogical dimension, the framework emphasizes the need to adapt teaching methods and assessment strategies in response to AI's growing capabilities, preparing students for an increasingly AI-driven workplace. By focusing on pedagogy, the framework ensures that AI technologies are harnessed to enhance learning outcomes and develop critical thinking, creativity, and other essential skills, rather than undermining academic integrity. Teachers are the initiator for the Pedagogical Dimension, as they are the ones who design and implement lesson plans, activities, and assessments that utilize AI technologies. They will need to have the expertise to determine how AI can best support and enhance students' learning experiences. At the same time, they must possess the necessary awareness and knowledge to educate students on the potential risks associated with the use of generative AI in learning, particularly in assessments and assignments, such as plagiarism and contract cheating. Teachers need to foster ethical use of AI, for example through proper attribution to acknowledge the contributions of AI technologies in student work, and develop assessment tasks that require critical and analytical thinking to avoid AI-assisted plagiarism. By assigning teachers the responsibility for this dimension, we ensure that AI tools are used in a way that is pedagogically sound and enhances the learning outcomes of students.

The Governance dimension highlights the importance of addressing issues related to academic misconduct, data privacy, transparency, and accountability. The framework ensures that stakeholders understand and address the ethical challenges associated with AI technologies, fostering responsible use and helping to maintain trust within the university community. This focus on governance encourages universities to develop clear policies and guidelines, ensuring that students and staff can navigate the complex ethical landscape surrounding AI.

Senior management will be the initiator for the Governance Dimension of the AI Ecology Framework. As they hold decision-making authority, they are tasked with developing and enforcing policies, guidelines, and procedures that address the ethical concerns surrounding AI use in education. These include academic integrity, data privacy, transparency, accountability, and security. Senior management's role is to ensure that AI is used responsibly and ethically, fostering a learning environment that is fair, equitable, and inclusive.

The Operational dimension of the framework underscores the need for ongoing monitoring, evaluation, and support to ensure the effective and equitable implementation of AI technologies. By considering operational aspects, the framework encourages universities to provide training, resources, and support to all stakeholders, promoting equal access to AI technologies and fostering an inclusive learning environment. Furthermore, the operational dimension emphasizes the importance of continuous improvement and adaptation, enabling universities to refine their AI integration strategies in response to new insights and changing needs.

Teaching and Learning and IT staff will be tasked to look after the Operational Dimension. They play a crucial role in managing and maintaining the AI technologies used in the educational setting. Their tasks include providing training and support for both students and staff, ensuring the proper functioning of AI tools, and addressing any technical issues that may arise. They can ensure that AI technologies are seamlessly integrated into the educational environment, minimizing disruptions and maximizing their potential benefits.

It is crucial to recognize that the responsibility of each dimension in the ecological framework should not be viewed in isolation. Collaboration and communication among all stakeholders (universities, teachers, students, staff and external agents such as accreditation, quality assurance bodies) are essential to ensure the successful implementation of any policy. Each group should actively participate in the development and execution of AI-related initiatives and work together to achieve the desired outcomes in university teaching and learning.

Conclusions

This study aims to establish an AI education policy for university teaching and learning, addressing concerns related to the use of text-generating AI in academic environments, such as cheating and plagiarism. The findings of this study yield ten key areas in AI education policy planning, from which an AI Ecological Education Policy Framework is constructed to fulfil the objective of the study. However, this study has some limitations, including a relatively small sample size that may not be representative of all educational institutions. Additionally, the research only focused on text-based generative AI technology and did not explore other types or variations. Lastly, the study relied on self-reported data from participants, which may be subject to bias or inaccuracies.

This study proposes an AI Ecological Education Policy Framework to address the diverse implications of AI integration in university settings. The framework consists of three dimensions-Pedagogical, Governance, and Operational-each led by a responsible party. This structure allows for a more comprehensive understanding of AI integration implications in teaching and learning settings and ensures stakeholders are aware of their responsibilities. By adopting this framework, educational institutions can align actions with their policy, ensuring responsible and ethical AI usage while maximizing potential benefits. However, more research is necessary to fully comprehend the potential advantages and risks associated with AI in academic settings. Merely advocating for AI implementation in education is insufficient; stakeholders need to carefully evaluate which AI technologies to employ, determine the best methods for their use, and understand their true capabilities.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Acknowledgements

The author wishes to thank the students and teachers who participated the survey.

Author information

Authors and Affiliations

University of Hong Kong, Hong Kong, Hong Kong, HKSAR, China

Cecilia Ka Yuk Chan

Contributions

The study is conducted solely by the author.

Corresponding author

Correspondence to Cecilia Ka Yuk Chan.

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Competing interests

The author declares that one has no competing interests.

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# data[].attributes.attachment_link.title
A comprehensive AI policy education framework for university teaching and learning - International Journal of Educational Technology in Higher Education

# data[].attributes.attachment_link.description
This study aims to develop an AI education policy for higher education by examining the perceptions and implications of text generative AI technologies. Data was collected from 457 students and 180 teachers and staff across various disciplines in Hong Kong universities, using both quantitative and qualitative research methods.

# data[].attributes.body
Any student work submitted using AI tools should clearly indicate what work is the student's work and what part is generated by the AI. In such cases, no more than 25% of the student work should be generated by AI.

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Policy

Acceptable and Unacceptable Use of AI
[This syllabus statement is useful when you are allowing the use of AI tools for certain purposes, but not for others. Adjust this statement to reflect your particular parameters of acceptable use. The following is an example.]
The use of generative AI tools (e.g. ChatGPT, Dall-e, etc.) is permitted in this course for the following activities:

Brainstorming and refining your ideas;

Fine tuning your research questions;

Finding information on your topic;

Drafting an outline to organize your thoughts; and

Checking grammar and style.

The use of generative AI tools is not permitted in this course for the following activities:

Impersonating you in classroom contexts, such as by using the tool to compose discussion board prompts assigned to you or content that you put into a Zoom chat.

Completing group work that your group has assigned to you, unless it is mutually agreed upon that you may utilize the tool.

Writing a draft of a writing assignment.

Writing entire sentences, paragraphs or papers to complete class assignments.

You are responsible for the information you submit based on an AI query (for instance, that it does not violate intellectual property laws, or contain misinformation or unethical content). Your use of AI tools must be properly documented and cited in order to stay within university policies on academic honesty. For example, [Insert citation style for your discipline. See these resources for APA guidance, and for other citation formats.]. Any assignment that is found to have used generative AI tools in unauthorized ways [insert the penalty here*]. When in doubt about permitted usage, please ask for clarification.

Use Encouraged and Permitted
[This syllabus statement is useful when you are allowing, and perhaps encouraging, broad use of generative AI tools.]
You are welcome/expected to use generative AI tools (e.g. ChatGPT, Dall-e, etc.) in this class as doing so aligns with the course learning goal [insert the course learning goal use of AI aligns with]. You are responsible for the information you submit based on an AI query (for instance, that it does not violate intellectual property laws, or contain misinformation or unethical content). Your use of AI tools must be properly documented and cited in order to stay within university policies on academic honesty. For example, [Insert citation style for your discipline. See these resources for APA guidance, and for other citation formats.]

Use Prohibited
[This syllabus statement is useful when you are forbidding all use of generative AI tools for any purpose in your class.]
The use of generative AI tools (such as ChatGPT, DALL-E, etc.) are not permitted in this class; therefore, any use of AI tools for work in this class may be considered a violation of Temple University's Academic Honesty policy and Student Conduct Code, since the work is not your own. The use of unauthorized AI tools will result in [insert the penalty here*].

* We highly recommend that you have conversations in your department about the appropriate penalties for unauthorized use of an AI. It is important to think about the appropriate level of penalty for first-time offenders and those who repeatedly violate your policies on the use of AI.

Source / Other Info

Center for the Advancement of Teaching | Sample Syllabus Statements (PDF)

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Class Policies for AI Tools | Center for Innovative Teaching and Learning | Northern Illinois University

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This guide includes a wide range of class policies shared by faculty, provided as examples that your might use or modify in developing your own policies for navigation AI tools (such as ChatGPT, MidJourney, Dall-E, etc.)

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Data Classification - Administrative Examples - Harvard University

# data[].attributes.subject
Data Classification - Administrative Examples - Harvard University

# data[].attributes.body
Classification Levels

L1 - Information intended and released for public use.

The University intentionally provides this information to the public.L1 Examples

Published research

Course catalogs

Published faculty and staff information

Student directory information*

Basic emergency response plans (life safety)

University-wide policies

Harvard publications

Press releases

Published marketing materials

Regulatory and legal ﬁlings

Published annual reports

Code contributed to Open Source

Released patents

Plans of public spaces

* Directory information about students who have requested FERPA blocks must be classified and handled as L3,
at minimum.

L2 - Low Risk Confidential Information that may be shared only within the Harvard community.

The University chooses to keep this information private, but its disclosure would not cause material harm.L2 Examples

Department policies and procedures

Employee web/intranet portals

Harvard training materials

Pre-release articles

Drafts of research papers

Work papers

Patent applications

Grant applications

Non-public building plans or layouts (excluding L3 or L4 items)

Information about physical plant (excluding L3 or L4 items)

L3 - Medium Risk Confidential Information intended only for those with a “business need to know.”

Disclosure of this information beyond intended recipients might cause material harm to individuals or the University.L3 Examples

Non-directory student information

Non-published faculty and staff information

Information protected under FERPA, in general

HUID tied to an individual

Personnel records**

Donor information (excluding L4 data points or special handling)

Non-public legal work and litigation information

Budget /financial transactions information

Non-public financial statements

Information specified as confidential by vendor contracts and NDAs

Information specified as confidential by Data Use Agreements

Security findings or reports (e.g. SSAE16, vulnerability assessment and penetration test results)

Most Harvard source code

Non-security technical  specifications/architecture schema

Library/museum object valuations

IRB records

​​​​​​​**Employees have the right to discuss terms and conditions of their own employment, including salary and benefits, with each other or with third parties.

L4 - High Risk Confidential Information that requires strict controls.

Disclosure of this information beyond specified recipients would likely cause serious harm to individuals or the University.L4 Examples

Passwords and PINs

System credentials

Private encryption keys

Government issued identifiers (e.g. Social Security Number, Passport number, driver’s license)

Individually identifiable financial account information (e.g. bank account, credit or debit card numbers)

Individually identifiable health or medical information***

Security system procedures and architectures

Trade secrets

Systems managing critical Operational Technology

*** Harvard units or programs that qualify as "covered entities" under the Health Insurance Portability and Accountability Act (HIPAA) must comply with HIPAA’s data security rules.

L5 - Reserved for Research Data only, as determined by IRB or Data Use Agreement.

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The UC Berkeley Data Classification Standard is issued under the authority vested in the UC Berkeley Chief Information Officer by the UC Business and Finance Bulletin IS-3 Electronic Information Security (UC BFB IS-3). Issue Date: November 7, 2019 Originally issued July 16, 2012 (Administrative revision: April 22, 2013)Responsible Executive: Associate Vice Chancellor for Information Technology and Chief Information OfficerResponsible Office: Information Security Office

Contact: Information Security Policy Manager, security-policy@berkeley.edu
Effective Date: November 7, 2020 for Protection Levels; July 1, 2022 for Availability Levels.

The UC Berkeley Data Classification Standard is UC Berkeley's implementation of the UC Systemwide Data Classification Standard.

UC BFB IS-3 establishes that Institutional Information and IT Resources must be protected according to their classifications. This Standard is a framework for assessing the adverse impact that loss of confidentiality, integrity or availability of Institutional Information and IT Resources would have upon the Campus . It provides the foundation for establishing security requirements for each classification of data.

Summary definitions and examples are included below. Full definitions and additional examples are available in the UC Systemwide Standard and Guides . Additional, UC Berkeley-specific guidance is available in the Campus Data Classification Guideline.

The Berkeley Data Classification Standard covers UC Berkeley Institutional Information and IT Resources. This Standard does not apply to Individually-Owned Data, which is defined as an individual's own personal information that is not considered Institutional Information

Note: Data classification does not alter public information access requirements. California Public Records Act or Federal Freedom of Information Act requests and other legal obligations may require disclosure or release of information from any category.

III. Definitions

Definitions of Key Terms (capitalized and italicized) used in this Standard are included in UC Berkeley's Information Security Policy Glossary

IV. Data Classification Levels

Business Impact

Considerations for evaluating potential adverse impact to UC Berkeley due to loss of data or resource confidentiality, integrity, or availability include:

Loss of critical Campus operations

Negative financial impact (money lost, lost opportunities, value of the data)

Damage to the reputation of the Institution

Risk of harm to individuals (such as in the case of a breach of personal information)

Potential for regulatory or legal action

Requirement for corrective actions or repairs

Violation of University of California or UC Berkeley mission, policy, or principles

Protection Level Classification Table

Proprietors may raise Protection Levels for a specific use case. An exception is required to lower a published Protection Level. Please contact the Information Security Office if an item below appears to be misclassified, or if you are unable to determine a Protection Level.

Data Classification Adverse Business Impact Definition
Examples (not an exhaustive list)
May be updated in response to changes in UC systemwide policy and UC Berkeley campus-level risk decisions.

High

Institutional Information and related IT Resources that require notification to affected parties in case of a confidentiality breach. This category also includes data and systems that create extensive "Shared-Fate" risk, where a compromise would cause further and extensive compromise among multiple (even unrelated) sensitive systems.

Unauthorized disclosure or modification of P4 data or resources could result in significant fines or penalties, regulatory action, or civil or criminal violations. There is also an inherent significant risk to UC reputation and business continuity, along with harm or impairment to UC students, patients, research subjects, employees, or guests/program participants.

Moderate

Institutional Information and IT Resources whose unauthorized use, access, disclosure, acquisition, modification, loss, or deletion could result in moderate fines, penalties, or civil actions. This classification level also includes lower risk items that, when combined, represent an increased risk.

Unauthorized disclosure or modification of P3 data or resources could result in legal action, harm the privacy of a group, cause moderate financial loss, or contribute to reputational damage.

500 or more records of personally identifiable information not specifically classified elsewhere in this Standard

Personal information as defined in the General Data Protection Regulation (GDPR) or China's Personal Information Protection Law (PIPL) regardless of record count. Must not include GDPR special categories of data (Article 9 'sensitive') - see P4 section

FERPA-Protected Student Records not containing P4 information. Does not include:

Security camera recordings, body-worn video system recordings, and cameras recording cash handling or payment card handling areas

Building entry records

Individually identifiable location data that tracks an individual's movement or building/room-level location

Animal research protocols, and data related to animal researchers and members of the Animal Research Committee

Attorney-Client Privileged Information

Individually identifiable human subject research data not containing P4 data elements and that the IRB or Campus Privacy Office have not determined is high risk. This includes human genomic data that can be re-identified using publicly available data.

Low risk export controlled data or technology (EAR/ITAR). Contact the Export Control Office for a determination.

IT security information, exception requests and system security plans

Staff and academic not containing P4 information. Does not include P2 Public Directory Information

Medical devices supporting diagnostics (not containing P4 information)

Industrial control systems affecting operations

Low

Institutional Information and IT Resources that may not be explicitly protected by statutes or other contractual regulations, but are not commonly intended for public use or access and should only be accessed on a need-to-know basis.

Unauthorized disclosure or modification of P2 data could result in minor damage or small financial loss, or cause a minor impact on the privacy of an individual or group.

Minimal

Information intended for public access, but whose integrity is important. For P1, unauthorized modification is the primary protection concern. The application of minimum security requirements is sufficient.

Public-facing informational websites

Course listings and prerequisites

Public event calendars

Hours of operation

Parking regulations

Press releases

Published research

Availability Level Classification Table

Proprietors may raise or lower the Availability Level based on use case. An exception is not required for Availability Level changes. Please contact the Information Security Office if an item below appears to be misclassified, or if you are unable to determine an Availability Level.

Data Classification Adverse Business Impact Definition
Examples (not an exhaustive list)
May be updated in response to changes in UC systemwide policy and UC Berkeley campus-level risk decisions.

High

Definition: Loss of would have a significant business impact to the Campus, a Campus Unit, and/or essential services. It may also cause serious financial losses. IT Resources that are required to be available by statutory, regulatory and/or legal obligations fall into this risk level. Critical IT Infrastructure also falls into this category.

Central Campus authentication systems

Student learning management system

Backup data systems for A4 resources

Central system management consoles

Core network services, e.g. DNS, border/core routing, and firewalls

Enterprise email

Official financial, accounting, and payroll systems of record and authoritative source systems; also systems that handle and process financial transactions, and the account codes, account numbers, PINs, etc., necessary to make the transactions

Building access systems

Medical records system

IT infrastructure and industrial control systems affecting life and safety (e.g. emergency 911 location system)

Campus procurement system

Campus time reporting system

Moderate

Definition: Loss of availability would result in moderate financial losses and/or reduced customer service.

Event ticketing systems

Point-of-sale (POS) systems

Issue tracking systems

Security logs

Building management systems

File servers supporting business operations

Operational knowledge base

Clinical trial management system

Medical devices supporting diagnostics

Industrial control systems affecting operations

Collaboration services such as calendaring, file sharing, code repositories

Low

Definition: Loss of availability may cause minor losses or inefficiencies.

Departmental websites

Student life management system

Staff learning management system

Informational knowledge base

Minimal

Definition: Loss of availability poses minimal impact or financial loss.

Individual workstations, laptops, and other mobile devices

Public directory

Copy machines or printers

V. Additional Information

Statutory Requirement for Notification

See definition in UC Berkeley's Information Security Policy Glossary

The following registration and approval requirements apply to information with a statutory requirement for notification ("Notice-Triggering" information):

VI. Responsibilities

The following roles have key responsibilities related to this Standard. Details are available in UC Berkeley's Roles and Responsibilities Policy

VII. Related Documents and Policies

Oct. 11, 2019: Draft posted on Information Security Office website

Nov. 7, 2019: Updates endorsed by Information Risk Governance Committee

Nov. 27, 2019: Clarification on passport data classification added

Dec. 12, 2019: Clarification on P2 d e-identified human subject or patient information added

Jan. 20, 2020: Clarification on P4 human subject and human genetic information added; clarification on P4 and P3 export controlled data or technology added

Jan. 23, 2020: Update to the P4 "notice-triggering data" list to reflect changes in California law

Mar. 5, 2020: Clarification on P4 financial, accounting, and payroll systems; P3 Individually identifiable location data; and P4 Passwords, PINs and passphrases added

Mar. 23, 2020: Clarification on A4 examples; moved "Campus time reporting system" from A3 to A4

Jul. 6, 2020: Clarified protection level of P3 and P4 human genomic data

Oct. 7, 2020: Clarified that UCPath Employee ID is P2, not P3

Dec. 2, 2020: Added information about when Proprietors are able to raise and lower published classification levels for a specific use case.

Jan. 29, 2021: Updated effective date for Availability Levels.

Apr. 11, 2021: Clarified that either the IRB or Campus Privacy Office can determine whether human subject data is "high risk".

Jul. 28, 2022: Updated P3 examples for personally identifiable information, FERPA-protected student records, security camera recordings, and animal research data.

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The UC Berkeley Data Classification Standard is issued under the authority vested in the UC Berkeley Chief Information Officer by the UC Business and Finance Bulletin IS-3 Electronic Information Security (UC BFB IS-3).

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Data Classification - Clemson University 

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Data Classification - Clemson University 

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The Example Data Elements table contains a list of commonly used data types. This list is not all inclusive but does provide guidance on how to classify data.

Example Data Elements

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Clemson University is committed to protecting the privacy of its students, alumni, faculty, and staff while protecting the confidentiality, integrity, and availability of information important to the University's mission. To meet that commitment, the University has developed this policy which will be used to categorize University information and determine what security controls are required for related systems and applications.

2. Roles & Responsibilities

For functional areas with a Data Trustee assigned, data classification is the responsibility of the Data Trustee. Data Stewards appointed by the Data Trustee will create an inventory of data under their purview and will ensure relevant information security policies and procedures are followed to protect the confidentiality and integrity of the data. If a system or application handles data of multiple classifications, it should be protected according to the higher classification. For areas without an appointed Data Trustee, classification and compliance with policies and procedures will be the responsibility of the data owner. This includes owners of shared drives, databases, and applications that collect or process data.

Data Trustee: Officer-level individual with oversight responsibility for University data related to the functional area.

Data Steward: Individuals (assigned by Data Trustee) who are responsible for the accuracy, privacy and security of the data.

3. Definitions

The University, in alignment with the State of South Carolina, has adopted four data classification categories: Restricted, Confidential, Internal Use, and Public. These classifications are defined below.

Restricted

1. The information is highly sensitive and is to be kept protected as a matter of law, regulation, contractual obligation.

2. A breach of confidentiality, integrity, or availability could have a significant adverse impact on the University's mission, safety, finances, or reputation.

3. The University is subject to statutory or regulatory penalties or notification provisions in the event of any unauthorized access or disclosure.

Confidential

1. The information is sensitive and is to be kept protected as a matter of University policy, procedures or contractual obligation.

2. A breach of confidentiality, integrity, or availability could have an adverse impact on the University's mission, safety, finances, or reputation.

Internal Use

1. The data is not Confidential or Restricted, but not generally available to the public.

2. A breach of confidentiality, integrity, or availability could have minimal adverse impact on the University's mission, safety, finances, or reputation.

3. The information pertains to or is used in the daily operations of the University.

Public

Data is developed and intended for public disclosure.

4. Data Elements

The Example Data Elements table contains a list of commonly used data types. This list is not all inclusive but does provide guidance on how to classify data.

Public facing websites

Policies and procedures designed for public use

Publicly available campus maps

Published research data

University contact information not designated by the owner as private

Non-public University policies

Training materials

Unpublished research data (at data owner's discretion)

Non-public contracts

University internal memos, emails, reports, and budgets

Donor contact and non-public gift information

Engineering, design, and operational information regarding University infrastructure

HR records

Birth date, addresses, personal contacts and other Personally Identifiable Information (PII)

Survey or assessment data collected which includes identifiers

FERPA protected data

Driver's License numbers

Controlled Unclassified Information (CUI)

Protected Health Information (PHI) governed by HIPAA

Export-controlled data governed by ITAR/EAR

Federal tax info received or derived from the IRS

Individual financial information subject to Gramm-Leach-Bliley Act (e.g. Financial Aid)

Social Security Numbers/ Employee Identification Number

Debit or credit card numbers

Bank accounts or information with personal identification numbers (PINS)

Passport and Visa numbers

For some data elements, the context will affect the classification. A name, photo and birthdate are Personally Identifiable Information (PII) and are Confidential but may be included in a University newsletter (with approval from the individual) and be considered Public in that context. Other records containing the same data will still be considered Confidential.

As data ages, the classification may also change. For example, University budgets may be classified as Internal Use, but become Public once published.

5. Data Classification Support

Questions regarding Data Classification, Data Trustees and Data Stewards can be directed to ithelp@clemson.edu.

6. Data Storage

Data must be stored in an approved IT Solution or storage location based on the classification. IT solutions that store or process data must be approved through the IT Vendor Management Process. Clemson also provides students, faculty, and staff a variety of File Storage Options.

Responsible Division

CCIT

Reviewed Date

November 1, 2018

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Clemson University is committed to protecting the privacy of its students, alumni, faculty, and staff while protecting the confidentiality, integrity, and availability of information important to the University's mission. To meet that commitment, the University has developed this policy which will be used to categorize University information and determine what security controls are required for related systems and applications.

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Guiding principles

Learning-Centered: Like any tool students might use to engage in the work of a course—from library books to research databases to internet search engines—AI systems present opportunities for students to learn important skills, including creativity, critical thinking, ethical decision-making, and the skillful use of resources, among others. We encourage instructors to talk explicitly with students about the pluses and minuses of AI systems as they help or hinder learning in a course.

Transparent: Faculty and GE instructors have flexibility in how they approach newly available AI tools, making it essential to prioritize transparency with students about your choices. Students will shift among a variety of expectations and need to clearly know the policy in each individual class. We encourage faculty and GE instructors to talk explicitly with students about what policies they’ve set and why, emphasizing the relationship between their policies and their goals for students’ learning.

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Artificial intelligence (AI) systems such as ChatGPT and DALL*E2 are language models programmed to respond to prompts or queries by using large data sets to generate, summarize, translate, predict, or recognize text, images, or other content. Many of these systems are now available (e.g. Sudowrite, Jasper, Quillbot, Wordsmith, Writesonice, Article Forge, Midjourney, etc.), and major search engines are becoming integrated with them (e.g. Microsoft Bing now uses ChatGPT, and Google Search will soon include Bard).

The wide availability of AI systems and the ease and speed with which they can generate content raises important considerations for teaching and learning in higher education. This resource provides instructors with suggestions and options for how to address AI use in their courses, plus links to additional resources.
Because the AI field is fast evolving, this resource will be continually updated. We welcome suggestions for additional resources and information.
Guiding principles

Learning-Centered: Like any tool students might use to engage in the work of a course-from library books to research databases to internet search engines-AI systems present opportunities for students to learn important skills, including creativity, critical thinking, ethical decision-making, and the skillful use of resources, among others. We encourage instructors to talk explicitly with students about the pluses and minuses of AI systems as they help or hinder learning in a course.

Perhaps, for example, if a learning objective is to develop students' own voices and perspective through reflective writing, AI systems would undermine that work. Alternatively, if the goal is to work through writing processes to best structure and refine arguments, an AI system might play a supportive role early in the process. Center a teaching and learning rationale for welcoming or restricting AI use.

Transparent: Faculty and GE instructors have flexibility in how they approach newly available AI tools, making it essential to prioritize transparency with students about your choices. Students will shift among a variety of expectations and need to clearly know the policy in each individual class. We encourage faculty and GE instructors to talk explicitly with students about what policies they've set and why, emphasizing the relationship between their policies and their goals for students' learning.

Policies and Considerations

We encourage instructors to have an explicit policy about AI use in the course syllabus, and to reinforce it in conversation with students. Below are sample course policies that you can use or adapt, plus some considerations about what you might want to convey to students about AI use. We also encourage exploration of the Syllabus Resources from the Sentient Syllabus Project, which offers a variety of considerations and example policies that can be used or adapted.

Allow AI for certain tasks:

Learning to use artificial intelligence systems such as ChatGPT (and others we'll discuss together) and recognize their pluses and minuses are important emerging skills. Their use will be allowed and encouraged for certain activities and tasks in this course (e.g., brainstorming and outline), which I'll explicitly note along the way. I'll also clearly indicate when such systems are not allowed or are discouraged (e.g. final paper draft). To support our collective learning about these systems, I will post resources about them in Canvas. I also welcome questions you may have about their use and am happy to meet and discuss how they might help or hinder your learning.

==

Variation: Be explicit if you will require AI use for certain tasks, for instance, "You will use artificial intelligence systems in this course as part of the requirements to complete certain activities and assignments."

Allow AI with permission only:

Use of artificial intelligence systems such as ChatGPT is allowed in this course only with explicit instructor permission. Either I will indicate if you are allowed to use such systems for particular tasks or you will need to ask for permission. If permission is given, I will also require you to document your use and provide clear guidelines for how to do so.

Allow AI with transparent acknowledgement:

Use of artificial intelligence systems (e.g., ChatGPT, iA Writer, etc.) is allowed provided you note explicitly where in your work process you used AI (e.g. generating an outline or first draft) and which platform(s) you used. If you use text generated by an artificial intelligence system as part of an assignment submission, such as a paper, you must attribute the text to the AI-based system that is its source. For example, if you include text generated by ChatGTP, you must cite the source as follows:

ChatGPT. (Year, Month, Day of query). "Text of your query/prompt." Generated using OpenAI. https://chat.openai.com/

==

Note: See "How to Cite AI" from UO Kenneth M. and Kenda H. Singer Science Librarian Annie Zeidman-Karpinski for other options.

Allow only original student work but not AI:

All work you submit for this course toward completion of course requirements must be your own original work done specifically for this course and without substantive assistance from others, including artificial intelligence systems (e.g., ChatGPT). Work you've completed for previous courses or are developing for other courses this term should not be submitted for this course. Please note that your work may be submitted to AI or plagiarism detection tools to ensure all work is human-created and original. Please also carefully read the academic integrity policy concerning plagiarism.

In addition to a clear policy, we encourage instructors to communicate with students about the use of AI and its relationship to learning in the course. Below are some considerations you may want to highlight.

Potential benefits of using AI systems such as ChatGPT:

improving equity, since more students can have access to personalized learning, tutoring strategies, and scaffolding;

saving time, e.g., when brainstorming or troubleshooting;

motivating learners when they feel stuck or unsure of how to move forward with a certain task

developing certain critical thinking skills.

source: AUA ChatGPT (AI) in Education Guide

Limitations of using AI systems such as ChatGPT:

AI platforms, such as ChatGPT, are not 100% accurate. ChatGPT will even make up information that sounds convincing but isn't real.

ChatGPT is not currently plugged into the internet to access "real-time" information. It has been trained on information that's current through 2021 but not the present.

AI platforms have bias. They have been trained on datasets that contain worldviews and assumptions and will replicate those ways of thinking about the world. Critical thinking strategies are especially important when engaging with AI-generated text.

AI platforms rely on language patterns to predict what an answer to a prompt should look like. They aren't "thinking" about the right response in a way a student would.

Apps such as ChatGPT tend to depersonalize your writing. Overreliance may lead to a lack of voice and distinctive style-rhetorical strategies that are crucial for effective writing.

source: AUA ChatGPT (AI) in Education Guide

Activities and Assignments

Instructors are experimenting with a variety of ways to include use of AI systems in their courses, including both classroom activities and assignments. Below are a few ideas to consider, including examples from UO instructors, and we also list a few ideas for those wanting to mitigate use of AI.

Examples from UO Instructors:

Kara Clevinger, English: Students use ChatGPT to foster critical literacy, argumentation, and writing. Specifically, they assess the strengths and weaknesses of ChatGPT-generated prose; consider how they would provide revision feedback on such prose (as if it were a classmate's work); and then use AI text as a departure point for analysis and articulation of their own thesis and argument. You can view Kara's ChatGPT assignment activity here.

Promoting analysis and critical thinking:

1. Ask students to draft a thesis statement and submit it to ChatGPT so that it uses the statement to generate a persuasive essay. Then, ask students to evaluate the essay according to the expectations outlined in the rubric. Where does the essay meet or fall short of the criteria in the rubric? As a key component of their evaluation, students are to assign the essay a grade and provide a written explanation of their decision that references the specific language in the rubric for support. Instructors teaching online looking to add a more personal, humanized touch can ask students to record a video in which they explain their evaluation. This assignment can be adapted to encourage student-to-student collaboration; organize students in small groups and task them with sharing their respective thesis statements with the goal of choosing one to be entered into ChatGPT. Upon entering the chosen thesis statement and generating an essay, members of the group work together to evaluate the essay according to the expectations outlined in the assignment rubric. Groups then share their findings with the whole class.

2. Students can also practice analyzing and synthesizing texts using ChatGPT. For example, the instructor can task ChatGPT with generating a comparative essay that analyzes the gendered norms in "The Story of an Hour," by Kate Chopin, and "The Yellow Wallpaper," by Charlotte Perkins Gilman. Once the essay is generated, students can analyze its synthesis of the two stories and identify opportunities for improvement. Where might the essay's argument be expanded or improved with more details from the stories or from researched support? In what ways is the essay's argument or synthesis insightful and/or flawed? Do you find the essay's synthesis to be persuasive? Why or why not? The activity can be adapted to multiple genres of texts as well as online formats. For example, an instructor teaching an online class could submit a ChatGPT essay to a social annotation tool like Hypothesis or Perusal, at which point students can use the annotation tool to discuss their thoughts and findings with their peers.

Assisting research and brainstorming:

Use Chat GPT to generate a list of terms and concepts relevant to a particular text or topic. Students can use these terms as the first step in a multi-tiered research process. Which of the terms sparks your curiosity? Which of the terms seem essential to our understanding of this text, argument, or even their rhetorical and/or historical context? Which would you be interested in researching further and why? What types of sources do you think are best suited to defining or explaining a given term? In a supplemental step, students could also use ChatGPT to generate definitions and/or explanations of these key terms. After doing so, students could conduct their own research to fact-check the ChatGPT answers and even compare/contrast their findings with those of ChatGPT.

Contributing to writing and revision:

ChatGPT can also be useful in supporting critical revision activities. For example, students can submit an essay rough draft to the AI and ask it to generate an outline of the essay. With the outline in hand, students can evaluate the organization of the outline or their essay with the goal of identifying opportunities to clarify key points or expand particular paragraphs or lines of thought. Students might also use the outline as a discursive map that helps them plan where in their essay they could address potential counterarguments or include researched support.

Facilitating icebreakers:

Instructors can also turn to ChatGPT to construct engaging icebreakers that build community and illustrate the limits of AI-generated content. For instance, students can be paired with a partner or a small group of peers; each student or group is responsible for interviewing a peer and constructing a list of their "favorites." What is your favorite book? What is your favorite memory? If you could only eat one ice cream flavor for the rest of your life, which would you choose? Once a short list of "favorites" has been drafted, students can input them into ChatGPT and ask the AI to describe the student's personality. Students can either discuss the AI's results as a group, noting what they find interesting and/or what the AI gets right and wrong, or they can present the AI's conclusions - as well as their thoughts about those conclusions - to the class, either in-person or by using a Canvas discussion board.

Mitigating use of AI:

1. Have students reference course-specific moments and materials such as lectures, discussions, labs, notes, handouts, or sources not otherwise available on the internet

2. Have students respond to image-based or sound-based texts in your assignments, albeit be certain to include alt-text for accessibility.

3. Ask students to apply or connect personal experience and knowledge in relation to key concepts or topics.

4. Have students use social annotation tools such as Hypothes.is or Perusall to engage with texts.

5. Consider having students submit audio files, a podcast, a video, infographics, or other multimedia texts instead of written essays.

6. Chunk major assignments, such as essays, into multiple due dates for key steps, such as an outline, notes on sources, drafts, etc.

Data Privacy and Security Considerations

UO Information Services reminds all faculty, staff, and students that:
The University of Oregon does not have a contract with popular AI systems such as ChatGPT, Bard and DALL*E2, which would include the security compliance assessment of such systems to gauge whether they satisfy requirements associated with the university academic, operational, business and research needs. In addition, the privacy policies associated with the use of these systems, requires the users acknowledge that the data input into the AI system will remain a part of the environment. As such we recommend that users exercise caution when interacting with these systems, to avoid unintended release of intellectual property, copyrighted materials or trade secrets.
We therefore strongly recommend that instructors who ask or encourage students to use any AI system remind students that they should avoid providing any personal or other sensitive data to AI prompts. We also advise that instructors consider making AI use voluntary or, if AI use is part of a required course assignment or activity, include an opt-out alternative for students who do not want to create an account with an AI system or interact with them. Such a recommendation is in alignment with UO's policy on external vendor digital tools, for instance use of social media such as blogs as part of course assignments, which must include an option for students to keep their information and identities private.

Tools and Resources

The following tools and resources can help instructors learn more about AI systems and better prepare for how they might address AI in their courses.

Citing AI systems in academic work: https://blogs.uoregon.edu/annie/2023/02/01/how-to-cite-ai_part-1/

Tools that can assist in distinguishing AI work from human work: AI Classifier, GPT-2, GLTR, GPTZero

Understanding AI systems and current capabilities in the context of higher ed: Future Trends Forum panel discussions - What Might Chat GTP Mean for Higher Education (Youtube video) and the follow up to the initial panel

Curated resources for educators, including sample policies, assignments, informational articles, etc.: AI Text Generators: Sources to Stimulate Discussion among Teachers

Practices that engage with AI, including critical practices: Prior to (or instead of) using Chat GPT with your students

Courses and assignment strategies that may de-incentivize AI use: How do we support learning loss due to AI text generators?

The Ethics of AI Explained, featuring Ramón Alvarado, UO Assistant Professor of Philosophy and member of the University of Oregon Presidential Initiative in Data Science.

New Modes of Learning Enabled by AI Chatbots by Ethan Mollick and Lilach Mollick

Using AI to Implement Effective Teaching Strategies in Classrooms by Ethan Mollick and Lilach Mollick

ChatGPT Advice Academics Can Use Now by Susan D'Agostino

Critical AI: Adapting College Writing for the Age of Large Language Models such as ChatGPT: Some Next Steps for Educators by Anna Mills and Lauren M.E. Goodlad

Embrace the Bot: Designing Writing Assignments in the Face of AI by Eric Prochaska

Decoding the Hype About AI: A conversation with Arvind Narayanan by Julia Angwin

Everybody is cheating: Why this teacher has adopted an open ChatGPT policy by Patrick Wood and Mary Louise Kelly

Teaching: What You Need to Know About ChatGPT by Beth McMurtrie

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Artificial intelligence (AI) systems such as ChatGPT and DALL*E2 are language models programmed to respond to prompts or queries by using large data sets to generate, summarize, translate, predict, or recognize text, images, or other content. Many of these systems are now available (e.g.

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As you read these syllabi statements and begin writing yours, consider:

What kind of relationship do you want to have with your students?

What do you want your students to know about you and your philosophy?

How can you foster trust?

How much support from AI is too much?

Where and how should students disclose their use of AI?

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Examples of AI & ChatGPT Syllabi Statements

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At WID, we know there is no “one-size-fits-all” syllabi statement; each of your disciplines and courses have particular goals and needs. UVM’s Academic Integrity policy is clear that each of us has the responsibility to clearly communicate our own policies in the syllabus. Below, you will find illus…