As the previous chapter highlighted, the risks and challenges to post-secondary education created or energized by generative AI are significant and wide-reaching from how we assess student learning, to promoting academic integrity, to considering what we want students to learn and what their future will be after graduation. Taken together these challenges are significant, and required a full institutional response.
This chapter reviews how McMaster responded to this challenge, and what will come next as an institution.
Generative AI at McMaster University
While generative AI emerged as a transformative technology tool after McMaster’s Institutional Priorities and Strategic Framework (2021-2024) and McMaster’s Partnered in Teaching and Learning Strategy (2021-2026) were launched, the impact of generative AI nevertheless aligns with existing institutional strategic priorities and ongoing efforts to enhance teaching and learning.
In McMaster’s Institutional Priorities and Strategic Framework (2021-2024), for example, Teaching and Learning, one of five priorities listed, identifies the development of active and flexible learning spaces as one key objective. It notes that in “recognizing the ways that online and virtual classrooms have changed the teaching and learning environment for both our educators and our students, [McMaster must] use evidenced based research to make decisions about tools and platforms to optimize learning in the digital environment” (p. 10). While there is little peer reviewed literature yet available on generative AI in post-secondary teaching and learning, McMaster is staying abreast of such research, and even engaging in research of its own in an effort to develop and maintain guidelines and good practices with respect to the usage of generative AI at the institution.
Likewise, McMaster’s Partnered in Teaching and Learning Strategy (2021-2026) connects to generative AI via not one, but two of its four strategic pillars: 1) Fostering Inclusive Excellence and Scholarly Teaching strategy, via the themes Teaching as a Professional and Innovative Practice, and Assessment and Evaluation of Student Learning, and 2) Developing Active and Flexible Learning Spaces, via the Digital Learning theme.
McMaster’s Task Force on Generative AI in Teaching and Learning
Recognizing that the initiatives in these strategies alone could not respond quickly enough to the challenges presented by generative AI, on May 1, 2023, McMaster University struck a Task Force on Generative AI in Teaching and Learning to consider impacts posed by generative AI on teaching and learning at McMaster.
The Task Force was also to provide strategic guidance and actionable recommendations for educators planning for fall courses. Co-chaired by Kim Dej, Vice-Provost, Teaching and Learning, and Matheus Grasselli, Deputy Provost, the Task Force includes students, faculty, and staff from across the university. Recommendations made by the Task Force will be submitted to Susan Tighe, Provost and Vice-President (Academic) in the fall of 2023.
The following overarching principles have guided the work of the Task Force and will continue to be updated through conversations with the McMaster campus community.
- 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.
- Disciplinary differences and departmental cultures will vary around the use of generative AI.
On June 5 the Task Force released Provisional Guidelines: The Use of Generative Artificial Intelligence (AI) in Teaching and Learning at McMaster University (June, 2023) for McMaster students and educators. The guidelines are intended to offer a starting point for instructors to understand the potential uses of generative AI in their teaching and student learning and for developing courses for the fall term.
These guidelines will continue to be updated as the Task Force explores additional topics and as technology changes. Members of the Task Force invite feedback and suggestions on these guidelines through this form. It is expected these guidelines will be updated again in time for winter course preparation. Potential policy changes implied by these guidelines will be addressed by the relevant governance bodies.
Staff at the MacPherson Institute are available to consult with instructors regarding these guidelines; Instructors can email email@example.com for support.
Appendix A: Citation and Reference Guidelines
A McMaster specific citation guide is in development through the Library. Until then, please consider citation options such as:
“[Generative AI tool]. (YYYY/MM/DD of prompt). “Text of prompt”. Generated using [Name of Tool.] Website of tool”
E.g. “ChatGPT4. (2023/05/31). “Suggest a cookie recipe that combines oatmeal, chocolates chips, eggs and sugar.” Generated using OpenAI’s ChatGPT. https://chat.opeani.com”
Instructors may also consider requiring students to include a reflective summary at the end of each assessment that documents what generative AI tools were used, what prompts were used – including a complete chat log – and how generated content was evaluated and incorporated.
Other citation guidelines can be viewed at:
- MLA Guidelines on citing generative AI
- APA Guidelines on citing generative AI
- Chicago FAQ on generative AI
- A quick guide provided from the University of Waterloo, with a McMaster version coming in Fall 2023.
Appendix B: Sample McMaster Syllabus Statements
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.
Some Use Permitted
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.
Appendix C: Sample Rubrics
Sample Rubrics Developed with ChatGPT:
I acknowledge the use of ChatGPT 4.0 to create sample analytic and holistic rubrics. The prompts included “Imagine you are a rubric generating robot who creates reliable and valid rubrics to assess university-level critical thinking skills. You have been tasked with generating a rubric that evaluates students critical thinking skills and incorporates their use of generative AI. Create two holistic rubrics and two analytic rubrics to assess these skills.” The output from these prompts was to provide examples of the kind of rubrics that could be used to assess the integration of generative AI in course assignments.
Rubric 1: Assessing Generative AI Use and Integration
|Argument Structure||The argument is clearly articulated and logically structured.||The argument is generally clear and logical, with minor inconsistencies.||The argument is somewhat unclear or inconsistently structured.||The argument lacks clarity and logical structure.|
|Evidence||Evidence is thorough, relevant, and convincingly supports the argument.||Evidence is generally strong and relevant, with minor lapses.||Evidence is somewhat sparse, irrelevant, or does not fully support the argument.||Evidence is lacking or largely irrelevant.|
|Use of Generative AI||AI is used effectively to support arguments, demonstrating a high understanding of its capabilities and limitations.||AI is used effectively, but understanding or integration could be improved.||AI is used, but not effectively integrated or misunderstood.||AI is not used or its use does not contribute to the argument.|
|Reflection on AI||The student clearly articulates how AI contributed to their critical thinking process and considers its limitations.||The student generally explains how AI contributed to their thinking, with minor lapses in considering its limitations.||The student’s explanation of how AI contributed to their thinking is unclear or superficial.||The student does not explain how AI contributed to their thinking.|
Rubric 2: Assessing Generative AI Use and Integration
|Understanding of AI||The student demonstrates a deep understanding of the capabilities and limitations of the AI.||The student demonstrates a good understanding of the AI, with minor misconceptions.||The student shows a basic understanding of the AI, but has significant misconceptions.||The student shows little to no understanding of the AI.|
|Integration of AI||AI is seamlessly integrated into the work, effectively augmenting the student’s critical thinking.||AI is generally well integrated, though at times it may seem somewhat forced or awkward.||AI integration is inconsistent or superficial, not effectively augmenting the critical thinking process.||AI is not effectively integrated into the work.|
|Reflection on AI||The student clearly reflects on the role of AI in their work, considering both its contributions and its limitations.||The student generally reflects well on the AI’s role, though considerations of its limitations may be superficial.||The student’s reflection on the AI’s role is minimal or lacks depth.||The student does not reflect on the AI’s role in their work.|
|Innovation with AI||The student uses AI in novel or innovative ways to enhance their argument.||The student uses AI effectively, though it may lack innovation.||The student uses AI in a straightforward or predictable way, not enhancing the argument.||The student does not use AI in an innovative or meaningful way.|
Appendix D: Honour Pledges
Forthcoming Guidelines and Resources
- Resources for students to understand generative AI risks and opportunities
- Overview of generative AI tools including privacy and security assessments
- Digital literacy learning outcomes and digital literacy resources