Artificial Intelligence And Generative And Emerging Technologies Support For The Classroom

• Marquette does not have a policy around the use of artificial intelligence and predictive technologies, as it is an evolving and quickly changing technology.
• As faculty and instructors, you can create your own “policy” around this in the classroom.
• However, we have come up with some recommended language that might support you in the classroom along with some additional information.
• NOTE: If you do not provide any guidance for students in their potential use of these technologies the default of the university will be that any students using AI without permission, without proper citation and credit, and without faculty approval will be vulnerable to an academic integrity violation.
• Set expectations early and often in the classroom via the syllabus and verbally.
• Consider talking with students with complete transparency about the do’s and don’ts of this evolving technology. In other words, provide them with the WHY. For example, we are trying to get students to consider that although these are evolving and oftentimes exciting technologies, we still have the responsibility to help them become citizens of the world with a value system that has the process of work as having as much importance as a final product for a grade. We need to also consider that what they may think of as real data may not in fact turn out to be that.
• Consider the ways in which we have traditionally as universities measured learning. Are there new ways that remove the influence of newer technologies while maintain the learning outcomes and values we are trying to create with them?
• AI and Predictive Technologies RESOURCE PAGE

• More generally, to provide provisional guidance to the university community, the current baseline expectation remains that, unless otherwise clearly attributed, a student is expected to have produced their own text and other content in submitted coursework. Like the unattributed use of any other source, the unattributed use of LLMs in coursework violates academic integrity. Colleges, departments, and instructors are welcome to invite the use of LLMs in their coursework, but if they do so, they ought to make explicit in syllabi (and, ideally, in assignment sheets and verbally as well) what is expected of students regarding this LLM use on specified assignments. In keeping with the necessary honesty and transparency of academic work in general, academic work that allows for LLM use should still attribute such use, as with any other source that scholars use to aid their work. Failure to cite the use of LLMs falls under the usual definition of plagiarism. (Where appropriate, LLMs should be cited using the instructions found on this or similar sites, and adapted as needed for different models.) Instructors permitting or requiring LLM use should also make clear that such permission does not apply outside the assignment(s) or course(s) for which the exception has been made.

• It is hoped that this general way forward will allow instructors in the various disciplines to utilize and experiment with LLMs in their courses if they so choose, while maintaining a baseline of clarity on expectations regarding LLM use about course work, learning outcomes, and academic integrity broadly understood. The deployment, development, and integration of these technologies into various sectors of society will continue to change and evolve, and this statement of guidance will be revised and altered as the university deems appropriate considering the changing situation.
• Finally, we encourage instructors to begin from a place of trust and transparency, inviting students into dialogue about the goals of higher education and working toward them together in these new circumstances. We believe that increased surveillance and suspicion will not lead us to improved student learning and a culture of academic integrity, but rather the fostering of candor and cooperation will do so as we engage in the labor of academic work side by side.

Note: Those who view plagiarism as an unwarranted categorization for LLM use that lacks attribution are asked to revisit the definition of plagiarism and to note in addition that—while the specific text produced by LLMs for a particular prompt may be superficially novel—LLMs do not generate their own responses whole-cloth but are trained on prior humans’ texts and other data, and guided by teams of workers who label that data. That is, other humans’ labor and intellectual property are always implicated and always in use when LLMs are employed, however anonymous and depersonalized those humans become in the black box mediation of an LLM. In addition, the initial human labor and intellectual property was used without those humans’ consent.

• Give thought and consideration to determine what use of any generative technologies if permitted or prohibited. State this CLEARLY in your syllabus and verbal announcements in a consistent manner so there is no confusion on the student end.
• We highly recommend you continue to include (or include if you haven’t already) the Marquette Academic Integrity policy on your syllabus which includes copyright and plagiarism language as the use of these technologies will fall within these policies:

  STATEMENT ON ACADEMIC INTEGRITY

  We, the scholars of Marquette University, recognize the importance of personal integrity in all aspects of life and work. We commit ourselves to truthfulness, honor and responsibility by which we earn the respect of others. We support the development of good character in our academic community and commit to uphold the highest standards of academic integrity as an important aspect of personal integrity. Our commitment obliges us as students, faculty and staff to conduct ourselves according to the Marquette University Honor Code set forth below. We do this in pursuit of Marquette University’s mission, which is the search for truth, the discovery and sharing of knowledge, the fostering of personal and professional excellence, the promotion of a life of faith and the development of leadership expressed in service to others.
  Students are asked to commit to academic integrity through the following honor pledge. Faculty may require students to sign the pledge in their courses or for any individual assignment.

• If as an instructor, you decide to allow students to use a form of predictive technology such as ChatGPT you should tell them they must cite any content taken from said technology that will comply with all citation guidelines and copyright requirements. Here is a guide on citing ChatGPA in APA format (as a side note, this is also an opportunity to talk to students about making sure AI-generated information/citations are real and correct as it is not uncommon for ChatGPT-generated information to be nonsense).

From Director of Academic Integrity, Jacob Riyeff

1. Remember that automation bias shapes many of our behaviors and responses in the world we share. While we value the tools we use regularly, this doesn’t necessarily mean that machines and algorithms are “better” at certain work than you are. Even in view of the “product” of finished writing, humans are still often far superior to llm-based chatbots in specific insight, precision of response, and grounding in basic facts.
2. I want to hear your own voice in your assigned work, because your voice is important and because I can’t help you develop and strengthen your voice unless I hear it. Using systems like Grammarly and QuillBot obscure your voice, and chatbots like ChatGPT and Bard can remove your voice completely from the process of doing assignments. Your voice is unique because you have a particular history that no one else has, so please let me hear it. Also, if you rely on automated services to “smooth” or “fix” your voice, you will only learn to rely on those services rather than learning how to express your ideas for yourself in dialogue with me and your other instructors.
3. While the interfaces of llm-based chatbots are designed to make the responses they give look like whole new answers that have been thought out, they are not. They are statistically derived word sequences based in the intellectual work of many people who did not give their consent to have their work used in this way. This process is further supported by teams of people who continue to “fix” the algorithms to work the way the parent companies desire. In this way, massive amounts of human labor are hidden from view whenever you receive a response from a llm-based chatbot, much of it underhanded and/or exploitative. A response from a chatbot is not “new” and “original” but derivative and very much “from a source.”
4. I know that a big part of the cultural discourse out there is that “generative AI” is inevitable and that teachers should encourage students to use it since they’ll be using it all the time in the professional world. First, that’s marketing that the media has largely accepted uncritically and magnified. Second, human actions are not inevitable, but the result of responsible decisions. Marshall McLuhan said, “There is absolutely no inevitability as long as there is a willingness to contemplate what is happening.” And even if you will be using these systems in your professional lives, you need your own background knowledge and critical skills in order to use them properly anyhow, and that’s what I’m trying to help you develop. To use “AI” responsibly, ethically, and effectively, you need to have the kinds of knowledge and skills that a university education aims to provide you.