AI Text Generators: Resource Page

Basic Academic Integrity Policy Statements

All faculty should identify one or more recommended statements to use regarding academic integrity and Generative-AI programs. These should be designed to complement or amplify a broad statement about general academic integrity. Your statement may, and likely should, vary depending on the course.  Departments, academic units, or programs are encouraged to help faculty create consistent and relevant statements.

Basic Academic Integrity Statement sample: 

 Mason is an Honor Code university; please see the Office for Academic Integrity for a full description of the code and the honor committee process. Three fundamental principles to follow at all times are that: (1) all work submitted be your own, as defined by the assignment; (2) when you use the work, the words, or the ideas of others, including fellow students or online sites, you give full credit through accurate citations; and (3) if you are uncertain about the ground rules on a particular assignment or exam, ask for clarification. No grade is important enough to justify academic misconduct. 

Generative-AI addendum, baseline: Use of Generative-AI tools should follow the fundamental principles of the Honor Code. 

Generative-AI addendum, expanded: Use of Generative-AI tools should be used following the fundamental principles of the Honor Code. This includes being honest about the use of these tools for submitted work and including citations when using the work of others, whether individual people or Generative-AI tools. 

Generating a more complete and accurate explanation of your goals and policies will take more time.

Since use of Generative-AI tools is likely to vary across disciplines, programs, courses, and even assignments, Stearns Center strongly recommends that faculty provide an addendum that fully articulates for students their policy about using tools like ChatGPT in their course. Academic units can also provide important leadership in this area. However, a one-size-fits-all statement is inappropriate.  

Faculty and leaders in academic units may use the chart below to identify language that should be used. Individual faculty should consult their academic unit or program leadership as they compose statements for their courses. 

• REQUIRED: Core policy language that explains the instructor’s or program’s particular stance on the use of Generative AI.  

• RECOMMENDED: Transparency statement that identifies faculty’s use of related tools to evaluate student work
• OPTIONAL: Expanded language on reasoning 
• OPTIONAL: Expanded language on discipline or course 
• OPTIONAL: Expanded language on ethics & access  

Please adapt these statements to the particular needs of a field, program, or course. See the next section for more information on communicating about the overall ethics and accessibility of Generative-AI tools.

For more suggestions about course policies, we recommend Dr. Lance Eaton’s evolving crowd-sourced collection of sample policies here.

There are multiple issues to be considered regarding generative AI (GenAI) use in classrooms. The following sections address some issues related to ethics, access, and detection tools. In all these matters, faculty should consider the learning environment they are creating for students. Instructors should consider the extent to which AI supports learning and innovative thinking for all students in their courses.

Detection Tools

Stearns Center recommends skepticism about tools or programs that indicate a reliable way of detecting whether text, artwork, or code is fully or partially generated by an AI tool.

Problems with detection technology:

• While there are some existing technologies, and more on the way, that claim to be able to “detect” writing from GenAI using them remains problematic in many ways.  

• These detection tools may provide some feedback to instructors, but they are unlikely to be anywhere near foolproof. (See this video of a faculty member experimenting with a flawed detector as just one example among many.) 

• For a thorough explanation of problems with relying on any kind of “AI detection” tool, see University of Rochester professor Whitney Gegg-Harrison’s overview here. 

Problems with detection as a core teaching approach:  

• Chasing after “cheating” students, particularly as technologies continue to develop additional capabilities, is a losing game. Stearns Center continues to recommend that faculty invest their time in updating their courses, messaging, assignments, and support structures to encourage and enable students to engage in meaningful assessments of their learning. In addition, we recommend that faculty generally avoid actions that over-prioritize “catching/preventing cheating.”
• Requiring handwritten submissions places additional burdens on students, especially those with disabilities.
• Adopting intensive surveillance tools or processes — such as requiring all steps of writing to be done under human or technological supervision — is likely to produce few gains while limiting students’ thinking and introducing additional biases and inequities into the learning process. Engaging in these activities creates a culture of surveillance rather than one of shared learning and inquiry. In addition, surveillance puts a focus on one type of cheating and does not prevent other forms of cheating, such as purchasing papers or hiring/having access to copy editors.
• Planning out systems to recognize original writing, either through eyeballing and comparing in-class samples to take-home samples by the same student, or by using an external text-evaluation tool, are likely to cause more problems than solutions, since these approaches are subject to bias and error that may reduce equity without significant gains in integrity.

Ethics and Access  

Departments, academic units, or programs can help clarify how faculty should or may communicate about the challenges inherent in course-use of Generative-AI tools.  For instance, instructors should consider both access and privacy issues.  

• While there are some free GenAI programs, some do require paid subscription for access. Assignments should be designed to ensure equal access for all students.   
• Use of some GenAI programs requires accounts, and these programs may track or retain student input. Some students may not wish to create an account using their personal information, or to submit their original work to a GenAI program. Faculty should provide alternatives for students who may not wish to create personal accounts.  

Sample statement to students 

Generative AI (GenAI) tools have limitations: they can hallucinate (create incorrect statements and provide fake citations), create inaccurate code, and provide offensive images or examples. They have been trained on limited sources that may contain biases and create biased output. The use of these tools creates other ethical quandaries: the algorithms rely on work done by other humans and do not give credit to their sources, and the algorithm adjustments are often completed by workers who may not be fully compensated for their labor and emotional stress. Lastly, reliance on these tools may stifle your own creativity and impede the learning process. Your decision to use Generative-AI tools should always consider these limitations.  

Who owns the right to materials used with GenAI tools — including original student work submitted to a GenAI program—is currently unclear. If you do not wish to risk (or give up) the rights to your intellectual property, you should consult your instructor. 

See below for a list of assignment examples faculty can integrate into their classrooms:

• An Introduction to Teaching with Text Generation Technologies by Tim Laquintano, Carly Schnitzler, and Annette Vee https://wac.colostate.edu/repository/collections/textgened/
• ChatGPT Assignments to Use in Your Classroom Today by Kevin Yee, Kirby Whittington, Erin Doggette, and Laurie Uttich https://stars.library.ucf.edu/cgi/viewcontent.cgi?article=1097&context=oer

For your current courses, you should take some basic steps. Additional resources and examples follow this list. 

• Learn: Familiarize yourself with the kinds of text that AI tools can generate.
  [Note: We recommend not using language that suggests that AI tools “compose” or “write” texts; they are algorithms assembling words and producing text in using a prescribed data set.] 

• See a brief explanation of how AI text-generation works here or here. 
• See examples of AI-produced text in response to a prompt here and here (content disclaimer:  the second link is a public social media thread with examples). 
• Try an AI text-generation tool out for yourself here or here. 
• Read about the limitations of “AI Text Detection” tools here and here.
• Consider some of the implications for teaching: There’s a great short IHE article here.

• Consider adapting some of your assignments to increase the value of tasks where students do the work without AI assistance and to limit the opportunity for AI assistance to meaningfully contribute to student success.
  • Increase the specificity of your question or task: For example, rather than an open-ended writing prompt (“compare X and Y”), ask for specific information or examples (ask students to expand on one point discussed in class, create a scenario and ask for specific resources/sources, or provide an extended reading and have students critique/analyze it) 
  • Require the application of key concepts to specific local cases, problems, or contexts: For example, provide students with a reading and ask them to analyze and expand on the topics or issues in order to focus the students’ thoughts and responses on that paper, example, or case study. 
  • Require inclusion, citation, and direct response to specific course texts: For example, prompt students to cite the textbook or another reading in class, in addition to X number of additional sources that they find in Y database in your field. 
  • Require proposals, drafts, and revisions.  Multiple, sequential stages of a larger assignment can be useful to help students scaffold their understanding as well as help them produce their own content. For example, credit/noncredit activities can be integrated for topic development (and you can provide time in class on this or with paired peer activities). Low-stakes activities can be useful for writing out outlines of papers (for grading, these can be completion/noncompletion or similar). Peer reviews and iterative development, particularly when combined with a short  (one-paragraph) reflection on what changes were made as a result of a first submission or peer review help students continue to frame and develop their learning. 
  • Shift grading criteria, especially for formative writing such as discussion boards or drafts, to place additional value on how students respond to and/or integrate ideas or feedback from their peers or from you. For example, integrating more specifics in discussion prompts (see above), then having students make peer comments and respond to each other, will help continue the conversation and allow students to develop their thoughts on the topic more fully. 
• Consider adopting activities that include AI text generation as a sample, starting point, and/or opportunity for critique-and-revision
• Continue learning about AI text generation in higher education settings
  • Begin to explore what “competency” will mean in your field/profession: In 2025 or 2030, as AI tools continue to expand, what do you imagine will separate “average” professionals in your field from leaders in the field? What will experts be expected to have memorized, to do “by hand” without tools, to do quickly in one try? What kinds of complex problems will they need to address using all the tools available? Mason students should be learning (and being assessed on) high-value skills.

Individually, within your academic unit, and/or with colleagues participating in Stearns Center events, keep exploring the larger questions raised by these new tools, such as:

• What is “original” student work, and when do we most want it, and for what (educational/growth/ethical) purposes?
• What will students need to do to be “good writers” as educated professionals, as AI text generators continues to gain facility in generating accurate and relevant text?
• How are current AI text generators in a “free” mode likely to progress toward a for-profit approach, and how will that affect students and young professionals?
• How can AI text generators be used to support inclusion and equity in higher education?
• How can responsible writers incorporate the use of AI text generators going forward? For instance, would giving citations and building further or editing the content be sufficient?  Are there any “baseline” uses for composing early drafts? 
• How can faculty shift their use and assessment of student writing to continue to encourage students to use writing for learning, communication, and documentation of their thinking?  
• What kinds of privacy and accessibility concerns might be raised concerning AI text generators? 
• What are the ways that bias and various “-isms” (for example, racism, sexism) appear in AI generated texts?  How do educators consider the potentials of hate speech?

• Stanford faculty weigh in on ChatGPT’s shake-up in education.  Stanford Graduate School of Education.  December 20, 2022.  https://ed.stanford.edu/news/stanford-faculty-weigh-new-ai-chatbot-s-shake-learning-and-teaching 
• Five chats to help you understand ChatGPT.  The Atlantic.  December 8, 2022.  https://www.theatlantic.com/technology/archive/2022/12/openai-chatgpt-chatbot-messages/672411/  
• Robo-writers:  the rise and risks of language-generating AI.  Nature.  March 3, 2021.  https://www.nature.com/articles/d41586-021-00530-0  
• AI Will Augment, Not Replace. Inside Higher Ed. December 14, 2022 https://www.insidehighered.com/blogs/just-visiting/guest-post-ai-will-augment-not-replace 
• AI and the Future of Undergraduate Writing. Chronicle of Higher Education. December 13, 2022 https://www.chronicle.com/article/ai-and-the-future-of-undergraduate-writing  
• Hidden biases and societal risks from People of Color in Tech, Christian Ilube, Dec. 13, 2022
• “On the Dangers of Stochastic Parrots” by Emily M. Bender, Timnit Gebru, et al, FAccT ’21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, March 2021. Association for Computing Machinery, doi: 10.1145/3442188.
  • A blog post summarizing and discussing the above essay derived from a Critical AI @ Rutgers workshop on the essay: summarizes key arguments, reprises discussion, and includes links to video-recorded presentations by digital humanist Katherine Bode (ANU) and computer scientist and NLP researcher Matthew Stone (Rutgers). 

Additional resources will be added to this page as they become available.