hello and welcome to my second update on my topic, “can adaptive algorithms replace traditional instructors?”.
this time around, i wanted to take another look at the topic of the ’empathy gap’, and how it might look to actually teach digital emotional literacy. as a reminder, ’empathy gap’ is described as: how models, chatbots, etc, ‘fake’ empathy without actually having any.
now, i personally know nothing about curating, crafting, and making a course outline, and or syllabus for any topic, let alone this. so instead, i’ve tried to look around and see what’s out there and what current educators are doing. so, i started my search around bccampus.ca for some info!
in doing so, i did find some interesting resources on this topic which i’ll share here!
the first source i looked at was this: Digital Pedagogy Toolbox: Cultivating (Gen)AI Literacy – Moving Past AI Pedagogy’s Hype.
Gwen Nguyen open this article up with a perfect scenario; “Picture this: you walk into your classroom and instead of rows of eager students you’re greeted by a sea of screens, each displaying an Artificial Intelligence chatbot ready to learn or teach at lightning speed.”. not quite the ideal world, but sets the tone of this article; as its all about AI pedagogy.
before reading this, i was unaware of the exact definition, so if you’re like me, ill share it here: Pedagogy is the method and practice of teaching; . It serves as an umbrella term that encompasses teaching strategies, learning activities, and assessments. More than just delivering information, it considers the psychological, social, and cultural contexts of learners to help them engage meaningfully with the material.
right off the bat, this term encapsulates what i’ve been researching. Gwen describes it as “the art of walking alongside learners”, saying it places emphasizes on the importance of listening, respecting, supporting, and guiding learners; and sets up examples where AI’s replication is limited;
- a student visits an office to discuss his/her reading challenges. the AI can modify these readings, putting it into a more accessible or alternative style, but it cannot provide the empathy or understanding of a teacher.
- some tools are capable of generating personalized learning paths based on student data. however, lots of these tools lack ethical reasoning about issues such as student data protection and privacy’s. additionally, they are uncapable of discussing ethics, culture and human experiences.
- AI is capable of grading student work based on a criteria. however, it isn’t capable of understanding creativity, original thinking, or emotional experience that might be present in the work.
from here, Gwen discusses what she thinks educators should know in order to teach AI literacy. essentially, educators should learn about the uses, and functionality and limitations of these tools. they should have experience within them, yet also be mindful of how they incorporate them. lastly, she talks about how instead of trying to create new terms, or train AI to be more human, we ourselves need to evolve by learning, and promoting the learning of GenAI literacy.
i initially had planned to run through a couple of more sources, but i’m sadly short on time and will need to post another update. overall however, i agree with everything in this article. teach educators how to teach students how to work with these AI tools; and instead of making the AI for human like, teach us to understand their faults. like i said, i’ve run a bit short here, so for my next update i’ll do a bit more diving in on this topic.