Generative Artificial Intelligence in Teaching and Learning

Scope and Outcomes

I. Purpose

The purpose of this task force is to investigate the impacts posed by generative AI on teaching and learning at McMaster University, and to provide strategic guidance and actionable recommendations for educators planning for fall courses.

II. Background

Generative AI, exemplified by models such as ChatGPT, Bing and Bard, is reshaping the landscape of education, bringing forth both opportunities and challenges. As a research-intensive university, it is imperative that we adapt to these developments and ensure that our pedagogical approaches and academic integrity standards remain robust, relevant, and effective.

III. Objectives

The task force will pursue the following objectives:

• Review the current state of generative AI technology and its implications for higher education, with a focus on potential benefits and challenges for the McMaster context
• Discuss risks to academic integrity that may arise from the use of generative AI and recommend proactive teaching strategies to mitigate these risks and communicate these with the community
• Draft and endorse guidelines, resources and training for educators enabling them to make informed decisions in their teaching strategies, including if/when/how to use generative AI in teaching practices (e.g. designing assignments, grading/feedback)
• Draft and endorse resources for students to familiarize them with generative AI tools to build both digital literacy skills and confidence in appropriate use of generative AI for learning.
• Comment on drafted recommendations to share with the Senate Committee on Academic Integrity for consideration on the responsible adoption of generative AI in teaching and learning.

While the Task force may discuss intersections with generative AI and research, service and staff activities at the University, its mandate is to address the impact of generative AI on teaching and learning, with a focus on student learning. Recommendations pertaining to research, staff work, or service work fall outside the scope of this task force.

IV. Membership

The task force will be co-chaired by the Vice-Provost, Teaching and Learning and Deputy Provost, with coordination provided by the MacPherson Institute. The task force will consist of members with expertise in the following areas:

• Faculty representatives from diverse disciplines
• Representatives from University Technology Services
• Educational developers
• AI and machine learning researchers
• Librarians and information specialists
• Student Affairs representatives
• Student representatives
• Privacy and academic integrity experts

V. Timeline

The task force will convene in May 2023 and aim to submit a final report with recommendations by August 2023. Updates will be shared with the Provost’s office and relevant stakeholders periodically throughout the process.

VI. Reporting

The task force will report directly to the Vice-Provost, Teaching and Learning and the Deputy Provost and will provide monthly updates to the Senate Committee on Academic Integrity through a summary report and the Academic Integrity Officer.

VIII. Support

The Generative AI Task Force in Teaching and Learning will be supported by MacPherson Institute staff that will:

• Schedule and coordinate logistics of task force meetings and events
• Circulate an agenda and documentation prior to each meeting
• Prepare and distribute minutes after the completion of each meeting
• Support the writing of the Task force recommendations

VIII. Review

The task force’s recommendations will be submitted by September 10^th and will be reviewed and assessed for implementation by the Vice-President, Academic in consultation with relevant stakeholders across the university.  The task force may be reconvened to address any subsequent developments in generative AI that warrant further examination or action.

Proposed Meeting Topics, Dates and Prework

1. Current State of AI in Teaching and Learning: May 16, 3:10-4:30
   1. Briefing package prepared and distributed a week in advance
   2. Outcome: Describe perceived benefits and challenges in McMaster context from both student and faculty perspectives

2. Academic Integrity and AI: June 26, 3:10-4:30
   1. Briefing package prepared and distributed a week in advance
   2. Outcome: Recommend proactive mitigation and communication strategies for educators to foster academic integrity at McMaster, including teaching practice and student approaches.

3. Policy Considerations for AI: July 18, 3:10-4:30
   1. Draft of considerations for policies distributed a week in advance
   2. Outcome: Feedback (and endorsement) of considerations for policies to be submitted to the Senate Committee on Academic Integrity for consideration

4. Student and Faculty Guidelines and Training in AI: August 29, 3:10-4:30
   1. Drafted Pressbook “AI in Teaching and Learning at McMaster University” and training options distributed a week in advance
   2. Drafted resource “AI in Learning at McMaster University” and conversation guides distributed a week in advance
   3. Outcome: Feedback (and endorsement) of drafted AI resources and training supports for faculty and students

• Meeting #1 Agenda (May 16th, 2023)

A McMaster specific citation guide is in development through the Library and will be ready for fall 2023. 

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. 

Use Prohibited 

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 

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.  

Unrestricted Use  

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

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. 

Analytic Rubric 2: Assessing Generative AI Use and Integration 

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.”