This winter I have spent a lot of time thinking, writing, and talking about GenAI in post-secondary education. As a faculty developer I designed and co-facilitated a workshop for NIC faculty called “GenAI (Generative Artificial Intelligence) as a Collaborator for Quality Student Learning”. In the workshop faculty members got a chance to learn about and explore three GenAI tools – Padlet AI image generator, Quillbot, and Microsoft Copilot. My co-facilitator and I shared how GenAI can be seen and treated as a team-member or collaborator, sitting with you at the table to work together with you on a task. In my role as a faculty member in Human Services I presented a 10-minute lightning talk at the VCC Conference for Teaching and Learning entitled “Use of GenAI to develop customized, aligned, and intersectional case scenarios for social work education”. In that talk I described how I was experimenting with Microsoft Copilot to develop case scenarios for my students that were aligned with assessment and included intersectional and diverse representations of families, and why that was important to me. 

So far, my thinking, experimentation, and work with GenAI has focused largely on instructor use. I have used it to develop case scenarios as well as evaluation templates. With my students I practice discussing AI with them, I include a blurb about it in my syllabus, and I model transparency, showing them when and what for I’m using GenAI. I have also dipped my toe into some basic classroom engagement activities using GenAI.  But I have not really crossed the bridge into incorporating GenAI right into student assessment or teaching how to use it in my courses.  

I know that knowing how to use GenAI well is an important workplace skill. As a social worker I have used GenAI to help with grant writing and website content development. I also heard anecdotally from a practicum student that GenAI was being widely used at their practicum site, and the practicum supervisor was hoping we would be teaching our students how to use it. So my next goal as an instructor is to really move GenAI into the classroom and get students learning how to use it effectively and ethically, analysing prompt and outputs, and using GenAI as part of their assessments.  

Jason Lodge developed this taxonomy of GenAI in education that he published on LinkedIn in May 2023 that has been widely referred to in many GenAI workshops I’ve attended over the past year. The taxonomy includes 6 categories along a continuum that describe the integration of GenAI in post-secondary education: Ignore, Ban, Invigilate, Embrace, Design around, Rethink. According to this taxonomy I would put myself firmly in the “Embrace” category, however Lodge suggests that over the long term we need to take the “Rethink” approach and really ask ourselves what assessment is for: “This challenging approach requires asking how and why students are assessed in the first place. If assessments feel like chores and do not encourage creativity or inspire actual learning, or there is substantial time pressure to complete tasks, there is increased motivation to cut corners. Further, if assessment tasks are not designed to align with the developmental process that is learning and continue to view this process through snapshots provided by the production of artefacts, the methods of assessment need a rethink” (Lodge, 2023).  

So what does this all mean and where am I now with my journey with GenAI? Reflecting back on my thinking and work on GenAI over the winter, along with this quote on “Rethinking” from Lodge, this chapter of my journey would be entitled “GenAI in teaching and learning; its not about the AI”. What does that mean? It means I believe that that right now the current iteration of GenAI  that is available for use by the general public (Microsoft Co-pilot, Chat GPT) is a fun, shiny, fancy tool, but I feel that pretty soon it will be just about as novel and exciting as google, i.e. not very novel or exciting. This means that talking to faculty and teaching faculty about how to use GenAI isn’t really about the AI. They will learn how to use GenAI and it will become commonplace. What these conversations are really about is this rethinking of assessment that Lodge refers to. What can we do to build trust between instructors and students? What can we do to create a learning environment that respects our students as adult learners and gives them choice and autonomy? What kind of assessments can we design that are authentic, engaging, and meaningful to our students? I’ve been grateful these past few months to have GenAI as a vehicle for all these conversations.  

In this article from Educause Review, high school student William J. Yin (2024) challenges us instructors and faculty developers to rethink education within the GenAI context with bold questions like “AI is changing the way we access information. How will the educational system adapt?” and “Personal interests and curiosity are the driving forces of learning. Are we ready to fully step into personalized education?” As I rethink and redesign assessments with GenAI in mind I am increasingly focusing on the personal, the self, and the use of “I”. GenAI can’t tell students what is happening in their own minds, so I am trying to give students more and more explicit opportunities for self-reflection, and practice articulating what they think about their learning and how it relates to their own identity and life experience. My hope is that by developing self-awareness and critical thinking skills in my courses they can move forward from their education with the strong foundation needed to use GenAI as a collaborator and partner to chase their own personalized dreams and learning goals. 

My hope for other instructors is that they can access Lodge’s taxonomy to ask themselves where they fit in with it now and where they want to work towards being along that GenAI continuum. If they choose Embrace or Rethink, what does that mean for them and what actions can they take in the classroom with their students to move in that direction? Like me, they may find that when they plot their course, it’s not about the AI. 

References

Lodge, J.M. (2023). Assessment redesign for generative AI: A taxonomy of options and their viability. LinkedIn. Retrieved on March 12, 2024 from https://www.linkedin.com/pulse/assessment-redesign-generative-ai-taxonomy-options-viability-lodge.

Yin, W.J. (2024). Will our educational system keep pace with AI? A student’s perspective on AI and learning. Educause Review. Retrieved on March 12, 2024 from https://er.educause.edu/articles/2024/1/will-our-educational-system-keep-pace-with-ai-a-students-perspective-on-ai-and-learning.

Image from padlet.com AI image generator.