Generative AI, including large language models, reference enormous amounts of existing text from almost all available sources and then use a variety of algorithms and trained behaviours to generate coherent and human-like output. As such, copying said output and presenting it as original work without proper attribution can be considered plagiarism. Inadequate paraphrasing of researched material is also considered derivative and potentially plagiarism. 

Accuracy and transparency about the origins of ideas and knowledge support Brock University’s guiding values of: 

• Integrity and respect; 

• Inclusivity, diversity, and equity; 

• Reconciliation and decolonization; 

• Freedom of thought and expression coupled with academic responsibility; and  

• Generation and mobilization of knowledge. 

The following text outlines the use of Generative AI in this course and by this instructor. Be aware that policies may be different in other courses and with other instructors. Do not assume that policies in one course apply to another. 

[If appropriate, you may also consider inserting a statement on writing aids like Grammarly or language translation tools.]

The learning outcomes of this course focus on [insert skills and knowledge expectations of the course here]. The use of Generative AI tools would not support these outcomes and may lead to inaccuracies or sharing of misleading information. Therefore, using Generative AI tools in this class is not sanctioned or permitted and departure from these expectations can result in the initiation of an academic misconduct investigation.  

The learning outcomes of this course focus on [insert skills and knowledge expectations of the course here]. In the context of our assignments and coursework, use of Generative AI tools may be beneficial with idea generation and brainstorming, through summary and analysis, and with drafting, feedback, and review. In our discussions, understanding limitations and biases can form important areas of analysis, review, and critical engagement. 

The learning outcomes of this course focus on [insert skills and knowledge expectations of the course here]. Some of our assignments and discussions may benefit from input from Generative AI tools to demonstrate different perspectives as well as limitations or biases. If the use of Generative AI tools is suggested for a particular case or topic in this course, this will be clearly communicated by the instructor.  

• How effective is the ordering of the information provided in your outline?
• How can changing the order of the information provided in your outline change how your outline is received by learners? (Fuentes, Zelaya, & Madsen, 2021, Gurung and Galardi, 2022).

• What are the positionalities of the authors for course readings and resources?
• Are there opportunities for different modalities of information to supplement the outline document such as 1-minute video introduction of professor, 1-minute video overview of Brightspace shell and where to find course specific information, 1-minute audio file explaining assessment instructions?

• Does the outline clarify how each assessment aligns to course learning outcomes?
• Does the outline note how the course provides opportunities for learners to demonstrate learning in different ways (as per UDL principles)? For example, text, images, videos, audio assessments to support inclusion and academic integrity.
• Does the outline clarify information about or delivery of “Office Hours” or call them something more meaningful such as Student Hours, or Question Drop-in Times, Tea with Teacher etc.? (Mowreader, 2023, Nunn, 2019)
• Does the outline note how learners can communicate with each other to create a community of learners?
• Does the outline note opportunities and spaces for learners to share resources, prompts, or questions that relate to the course?