Pedagogy without Borders: New Frontiers of Teaching and Technology

Many models of AI literacy have emerged in the past few years, which emphasise the need for educators to understand the fundamentals of AI technologies, use AI tools effectively, analyse the output critically and make ethical decisions on use (Kassorla et. al., 2024; Hibbert et. al., 2024; Ng et. al., 2021). However, very few of these frameworks explicitly engage with the emotional impact of Generative AI, and how the discomfort, distrust, and uncertainty that these technologies can present influence the development of new teaching and learning practices. Developing Generative AI literacy requires a level of emotional intelligence, both recognizing our own emotional responses to Generative AI technologies and empathizing with the emotional responses of others, including learners and peers. Although discussion around uniquely human skills and how these need to be considered in educational and assessment design are on the rise (Oregon State University Ecampus), AI literacy also requires engaging with the existential questions that Generative AI raises about what it means to be human and how Generative AI technologies fit into our relationships with each other, society, and the environment.

At Western University, our Generative AI programming explicitly addresses these additional considerations through a holistic framework for AI Literacy. A holistic approach to Generative AI literacy for education requires engaging with multiple intersecting lenses: knowledge, skill, morals, values, affect and interconnectedness. To make an informed decision on if and how to integrate Generative AI tools into teaching and learning practices, educators need to understand how Generative AI technologies work (knowledge), how to use them (skill), the ethics of AI development, training and use (morals), the pedagogical potential of General AI technologies (values), our own emotional response to Generative AI (affect), and our beliefs around how Generative AI technology and use connects with our own humanity and our place in the world (interconnectedness). Our approach to helping instructors foster Generative AI literacy engages with all of these elements, through a reflective self-assessment on “AI-Readiness” built into our asynchronous training modules and through fulsome discussions being facilitated in our Community of Practice. In this session, we will share this holistic framework for Generative AI Literacy, briefly reviewing the domains of knowledge, skill, morals and values, and focusing on the additional complexities of affect and interconnectedness. We will explore some of the ways that we have engaged with these elements of AI Literacy through interactive activities.


Hibbert, M., Altman, E., Shippen , T., & Wright , M. (2024, June 3). A framework for AI literacy. EDUCAUSE Review. https://er.educause.edu/articles/2024/6/a-framework-for-ai-literacy#:~:text=Academic%20and%20technologies%20teams%20at
Kassorla, M., Georgieva , M., & Papini, A. (2024, October 17). AI Literacy in Teaching and Learning: A Durable Framework for Higher Education. EDUCAUSE. https://www.educause.edu/content/2024/ai-literacy-in-teaching-and-learning/executive-summary
Ng, D. T. K., Leung, J. K. L., Chu, K. W. S., & Qiao, M. S. (2021). AI Literacy: Definition, Teaching, Evaluation and Ethical Issues. Proceedings of the Association for Information Science and Technology, 58(1), 504–509. https://doi.org/10.1002/pra2.487
Oregon State University Ecampus. (2024) Bloom’s Taxonomy Revisited.

Generative artificial intelligence (AI) is transforming higher education, and its applications to the Scholarship of Teaching and Learning (SoTL) are no exception. In this interactive 40-minute virtual workshop, three teaching and learning experts, working at multiple institutions in Ontario, will guide participants in exploring how AI-based tools, such as ChatGPT, scite_, or others, can enhance SoTL-related work. We will delve into the possibilities generative AI offers for supporting literature reviews, research design, data analysis, and the dissemination of findings, while also addressing the challenges and ethical considerations that come with adopting these tools. Participants will identify practical ways to integrate generative AI into their SoTL workflows and engage in critical discussions on responsible generative AI use.

The workshop will begin with a 10-minute introduction and overview where the facilitators will briefly introduce themselves and outline the capabilities and limitations of generative AI tools relevant to their own SoTL. This will be followed by a 15-minute interactive exploration in breakout rooms, where participants will engage in hands-on activities to explore different generative AI applications. Each breakout room will focus on one of three topics: leveraging generative AI for generating research ideas and questions, using generative AI for data analysis, and employing generative AI for dissemination tasks such as generating abstracts, presentations, or written materials.

After the breakout sessions, participants will reconvene for a 15-minute debrief and group discussion. This segment will provide an opportunity to share key takeaways, insights, and concerns with facilitators highlighting common themes, opportunities, and ethical challenges. The discussion will cover important considerations such as academic integrity, transparency, and the responsible use of generative AI in research on teaching and learning.

Participants will leave the workshop with a deeper understanding of how generative AI can support SoTL activities, along with practical ideas for implementation and a critical awareness of ethical responsibilities. The session will use Zoom features like breakout rooms, screen sharing, and chat to ensure an engaging and interactive experience. This workshop is ideal for faculty, educational developers, and students interested in the intersection of generative AI and SoTL. Join us for a dynamic exploration of how generative AI tools can inspire, assist, and challenge our SoTL work!

This workshop addresses a problem we face in the classroom. For instance, I always introduce Ai to my students and have assignments for them where using Ai is a part of the work. However, some students use Ai to produce meaningless results - for instance, a student who played an entrepreneurial simulation and was required to write a "lessons learned report" - asked Ai to write the report for them - and as a result, the report was like: you probably asked the Venture Capitalists for this and they offered that ... etc., and she ended up having the report just like that! This paper will explore training students on using a holitsic approach where prompt engineering is only a part of wider process to integrate AI meaningfully in academic tasks. It will provide provide guidelines for selecting the appropriate strategies, designing the process, recognizing when AI should or should not be used in specific tasks. It will introduce case studies on the productive use of AI for tasks like first drafts or writer's block and metrics for evaluating the quality and authenticity of AI-enhanced outputs.

In today's dynamic educational landscape, teachers are often challenged with the dual demands of meeting diverse student needs while managing their own stress and time constraints. This seminar offers practical solutions to these challenges. Participants will explore effective techniques for differentiating instruction that cater to varied learning styles and abilities, ensuring all students can thrive. The session will also cover streamlined project planning methods that save time without sacrificing quality or creativity. Additionally, we will delve into efficient assessment and evaluation strategies that provide meaningful feedback while reducing the workload on educators. By the end of this seminar, attendees will be equipped with actionable insights and tools to enhance their teaching practices, improve student outcomes, and foster a more balanced professional life. Join us to discover how small changes can lead to significant improvements in both teacher well-being and student success.

As generative AI becomes a more significant part of our world, the need for technical literacy and adaptability in teaching and learning is more critical than ever. In addition to concerns about privacy, ethical use, and equitable access, educators must navigate the challenge of understanding and applying these transformative technologies. This session introduces the concept of running AI models locally on personal devices, providing a safer and more accessible way to explore AI’s potential while building technical skills and fostering adaptability in an evolving educational landscape.

Key discussion points include:

1. Building Technical Literacy: An introduction to the foundational skills required to set up and use AI models locally, helping educators gain confidence in engaging with AI technologies.
2. Educational Applications: Examples of how locally-run AI can support activities like content creation, formative assessment, and real-time feedback, while ensuring transparency and control over outputs.
3. Ethical AI Use: Addressing the challenges of bias, misinformation, and over-reliance on AI tools while emphasizing the importance of fostering AI literacy among faculty and students.
4. Hands-On Setup: A step-by-step guide for participants to deploy a small AI model on their own laptops or a classroom computer, along with troubleshooting tips and resource recommendations

Session Goals:

1. Demystify the technical process of running AI locally.
2. Equip educators with actionable strategies for implementing AI in a secure, ethical, and accessible way.
3. Foster dialogue around the role of locally-run AI in creating inclusive and equitable learning environments.