Artificial Intelligence

AI Resources

Browse this page to explore UFV AI Principles, AI Guidelines developed by TLC and other AI resources for teaching and learning. As the field of generative AI is evolving rapidly, this page will continue to be updated with additional guidance and resources for instructors in an effort to support them in navigating the changing educational landscape.

What is Gen AI?

A human hand and blue pixelated with lines artificial hand shaking hands Artificial Intelligence (AI) aims to create machines controlled by computers or software that mimic human cognitive functions (intelligence) such as learning, problem-solving, logical reasoning, perception or understanding natural language.

There are three types of AI:

Reactive AI responds to specific input with always the same output, without learning from past experiences. Examples of reactive AI are autonomous robotic vacuum cleaners.

Predictive AI analyzes data to predict future events or behaviors, looking for patterns in data collected from users. Examples of predictive AI are the personalized recommendation systems in platforms such as Amazon, Netflix or Spotify, where products are suggested based on user’s preferences.

Generative AI (GenAI) is a type of AI that has the capability to generate new content. It refers to algorithms or models that have been trained on large sets of existing data so that they can create text, images, code, and other forms of content. GenAI models learn to recognize patterns in the training data and build predictive models based on this learning. You can further refine the generated content by providing feedback to the AI tool, or by editing your original prompt to meet your specific needs. Examples of GenAI tools are ChatGPT, CoPilot or MidJourney.

AI Principles

Artificial intelligence is transforming the way we teach, learn and work. We must carefully consider both the benefits and challenges that come with it. Balancing human-AI interactions requires careful planning and consideration. UFV created the Artificial Intelligence Task Force (AITF) to gather input from all university stakeholders and develop institution-wide AI principles to ensure that its integration into education is beneficial and aligned with UFV core values. These seven overarching principles were developed through a receptive, flexible, and proactive lens, keeping in mind the diverse needs of the various sectors of the UFV community. Academic, research, and administrative units can apply these principles for guiding them in the degree and nature of AI use in their respective areas.

Seven Principles:

Integrity and Innovation
Flexibility, Adaptability, and Effectiveness
Informed, Balanced, and Appropriate Use
Data, Content, and Governance
Ethics, Digital Literacy, Regulation
Inclusion and Accessibility
Positive Mindset, Forward Leaning Approaches

UFV AI Principles (PDF)

AI Guidelines

TLC has focused on creating guidelines that support instructors in applying each of the UFV AI Principles in their pedagogy. The guidelines urge instructors to be open, innovative, and flexible to technology. They also encourage instructors to critically analyze the ethical implications of using generative AI tools and take steps to mitigate them.

AI Guidelines

Use of AI Detection Tools

A large number of detection tools have emerged in the market to try and assuage educators’ concerns around academic integrity that have resurfaced with the prevalence of generative AI tools. These tools currently fail to reliably distinguish between content which is original from that which is generated. They may frequently come up with false positives, incorrectly detecting AI generated content in a student’s original work (Dalalah & Dalalah, 2023; Elkhatat, Elsaid, & Almeer, 2023).

Moreover, when content is flagged as generated, detection tools do not produce a similarity report showing the sources from which that content may have come from (Dalalah & Dalalah, 2023). Of great concern is also the potential of bias in detection tools due to the language models used to develop them. Evidence shows that detection tools are more likely to wrongly label non-native English writing as generated (Liang et al., 2023). The ability of detection tools to mitigate against such bias has not been substantiated yet. Due to the fallibility of AI detection tools, UFV has not approved the use of any detection tools as of yet. This stance is consistent with that of several other post-secondary educational institutions across British Columbia.

Since detection tools are likely to be more detrimental to teaching and learning than beneficial, the university has decided to err on the side of caution until the effectiveness and accuracy of these tools has been widely established.

Research Articles: Unreliability of AI Detection Tools

Dalalah, D. & Dalalah, O. M. A. (2023). The false positives and false negatives of generative AI detection tools in education and academic research: The case of ChatGPT. The International Journal of Management Education, 21(2), https://doi.org/10.1016/j.ijme.2023.100822.
Elkhatat, A. M., Elsaid, K., & Almeer, S. (2023). Evaluating the efficacy of AI content detection tools in differentiating between human and AI-generated text. International Journal for Educational Integrity, 19. https://doi.org/10.1007/s40979-023-00140-5
Liang, W., Yuksekgonul, M., Mao, Y., Wu, E., & Zou, J.(2023). GPT detectors are biased against non-native English writers. Patterns, 4(7), https://doi.org/10.1016/j.patter.2023.100779

Additional Resources

A Guide to Generative AI for Educators: Ryan Mann at SAIT has compiled this guide to support instructors in using AI constructively for enhancing teaching and learning. The guide shares ways in which AI can serve as a teaching aid and a learning tool, while also cautioning instructors about challenges around data privacy, transparency, and appropriate use  of AI. It is a useful resource for faculty and instructors to use in conjunction with the AI principles and guidelines referenced above.
The Curious Educator's Guide to AI: This open educational resource authored by Kyle Mackie and Erin Aspenlieder aims to inform educators of the academic possibilities that generative AI offers, while also discussing the challenges involved in its application in education. It not only shares practical ways to use generative AI for enhancing teaching and learning, but also communicates the importance of reflection and evaluation in an effort to promote ethical and responsible use.
The Artificial Intelligence Assessment Scale (AIAS): Perkins et al. (2024) have designed this simple yet comprehensive tool for educators to use for determining the extent to which generative AI may be used in assessments within their courses. The tool is based on the foundational assertion that the use of generative AI should be permissible to the extend that it does not contradict learning objectives of the course, and provides a framework for educators to apply to their pedagogical practice.
Five Principles for the Effective Ethical Use of Generative AI: This website outlines five student-centered principles that foster effective and ethical use of generative AI in education. It also presents practical strategies that educators can adopt to implement each of these principles.
Introduction to Generative AI for Educators, by McMaster University: This online module aims to provide educators with an understanding of generative AI to help you think through how these technologies intersect with your teaching practices. Whether you have reservations or enthusiasm about AI in education, “Introduction to Generative AI for Educators” offers a space for exploration and thoughtful consideration. Topics include: what is GenAI is, what can/can’t GenAI tools do, how to use GenAI tools, and how GenAI is changing the teaching and learning landscape.