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University of Manitoba
Winnipeg, Manitoba Canada, R3T 2N2
At the University of Manitoba, academic integrity is defined as a commitment to the fundamental values of honesty, trust, respect, fairness, responsibility, and courage when engaging in academic work. Recent technological advancements have challenged educators and administrators to re-think teaching and learning practices, promotion of academic integrity, and definitions of academic misconduct.
This resource provides a brief overview of automated content generation applications or tools (more commonly known as artificial intelligence), and associated challenges and opportunities for teaching and learning with integrity.
Artificial intelligence technologies include software containing algorithms that assist the user in producing text, images, video, music, mathematical computations, or other content based on keywords, phrases, and questions provided to it. ChatGPT, GPT-3, and DALL-E are just three examples of content generation applications that are freely available to the public.
Similar algorithms can be found in the applications that we use in our day-to-day work and personal lives (e.g. spelling and grammar check). The capabilities of these technologies are advancing rapidly necessitating flexibility in how we approach their use within educational, social, cultural, political, legal, and economic contexts.
Automated content generation applications currently use data extracted from the internet by humans to “create” new content, but use of this data is not “live” as the applications do not search/scrape the internet regularly for new information. The quality of the output is dependent on the quality of the prompts that are entered into the applications as well as the information that the application accesses. The output can be prone to errors, omissions, and/or biases.
Automated content generation applications and other technological innovations (e.g. calculator, textbook, course notes, search engines, ChatGPT) may enable students to offload their academic work. Cognitive offloading using certain tools may be (in)appropriate depending on the specific learning outcomes of an assessment, course, or program of study. When using such tools is inappropriate, it can be difficult for educators to assess the learning of their students accurately.
Review of the Responsibilities of Academic Staff with Regard to Students (ROASS) will help you to identify the educator’s role in articulating assignment, course, and program expectations (including technology use) to students at the UM.
The guided use of technologies in higher education may increase engagement and support learning. Carefully designed learning activities and assessments that incorporate the use of automated content generation application can help students to think differently, more critically, and at a higher level as they synthesize and analyze ideas.
Explore these links to learn more about incorporating artificial intelligence applications into teaching and learning:
• Prochaska, E. (2023, Jan 23). Embrace the bot: Designing writing assignments in the face of AI. Magna Publications.
• Contact North/Nord. (n.d.). AI in higher education resource hub. https://teachonline.ca/ai-resources
Given that use of technology in education is context dependent, communicating your expectations to students about what is or is not permitted in your context is paramount for promoting academic integrity. Consider the following points when developing your communication plan about your expectations.
If you decide that the use of automated content generation tools are not appropriate for your teaching and learning context, and you suspect its use by a student, you may be wondering if you can report and address these violations of integrity at the University of Manitoba. The short answer is “Yes.”
According to the Student Academic Misconduct Procedure, “’Academic Misconduct’ means any conduct that has, or might reasonably be seen to have, an adverse effect on the academic integrity of the University . . . ”
This definition does not limit violations (including those related to the use of artificial intelligence technologies) to the six categories of academic misconducted listed in the document (i.e., plagiarism, cheating on quizzes, texts, and exams, inappropriate collaboration, personation, duplicate submission, academic fraud).
Review of the Student Discipline Bylaw can help you understand how to approach the reporting and adjudication of academic misconduct related to unpermitted use of technologies to complete assessments.
This resource has been adapted from Supporting Academic Integrity: Ethical Uses of Artificial Intelligence in Higher Education Information Sheet and Eaton & Anslemo (2023). Teaching and Learning with Artificial Intelligence Apps
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
For more information: Brenda.Stoesz@umanitoba.ca at The Centre for the Advancement of Teaching and Learning, University of Manitoba.
The Centre for the Advancement of Teaching and Learning
65 Dafoe Road
University of Manitoba (Fort Garry Campus)
Winnipeg, MB
R3T 2N2, Canada
