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
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
- Instructors are not required to use generative AI tools for teaching.
- 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.
- 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.
- Instructors with courses that incorporate generative AI should:
- 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.
- 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.
- 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”.
- Unless otherwise stated, students should assume use of generative AI is prohibited.
- 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.
- 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.
- 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.
- Until more is understood about generative AI detection tools, instructors should not submit student work to generative AI detection tools.
- 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.
- If you do suspect student work may have violated the academic integrity policy, please review the steps to take.
- 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.
- Instructors should fact-check any generative AI produced materials.
- Instructors should not submit student work to generative AI tools for feedback without students’ consent and ability to opt-out.
- Course instructors have three options for directing teaching assistant use of generative AI
- 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).
- 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.
- Prohibiting teaching assistants from using generative AI for teaching tasks
- 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.
- Instructors who include assessments that incorporate generative AI should:
- Consider including reflective components that invite students to comment on the use of/experience with generative AI in the assessment
- Explicitly review criteria and/or rubrics in ways that demonstrate how the use of generative AI is being assessed.
- 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.
- Where possible, courses that incorporate generative AI should rely on free versions of generative AI tools (e.g. Microsoft Copilot, ChatGPT 3) for student use.
- Alternatives should be provided for Generative AI tools that are restricted to users 18+ (e.g. ChatGPT).
- 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.
- Instructors may consider adding an honour pledge to assessments.
- 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.
- 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.
- McMaster will explore an annual donation to carbon offsetting programs to address the environmental impact of training large AI models.
- 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.
- These guidelines will be regularly reviewed and revised.
Information Box Group
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.
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.
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.
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 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.”