The Many Faces of Artificial Intelligence (AI)
(Photo Credit: ChatGPT 4o)
AI is often the elephant in the room, surfacing in everything from high-stakes policymaking and academic research to casual conversations with my barber. It permeates so many aspects of life that it’s hard to escape its presence. Yet, because AI is so vast and varied in its applications, it remains a “hyper-object”—something so complex and multi-dimensional that it feels impossible to fully grasp. It impacts education, healthcare, law enforcement, entertainment, and more, each in dramatically different ways. AI is not just one thing; it’s many things, depending on where and how it is deployed.
Let’s complicate the idea of infrastructure in conversations about GenAI
First, a blog post
Rethinking AI Infrastructure: Beyond the Surface-Level Conversations
The current excitement around generative AI has sparked a wave of discussions about its potential to transform various aspects of our lives. Amidst the hype, there’s a growing tendency to refer to AI as “infrastructure.” While this comparison might seem fitting, it oversimplifies the complexity of AI systems and, more importantly, obscures the critical questions surrounding control, benefit, and impact. As we hurtle towards an AI-driven future, it’s essential to pause and reexamine our assumptions about AI infrastructure.
Putting Generative AI in Classrooms
It was more than one year ago when I mused on the topic of integrating AI in education. Now, it feels like that post was made a decade ago!
Yesterday, I got a chance to be on a panel on this topic, moderated by my great colleague Dr Susan Yoon and participated by Drs. James Lester and Ryan Baker, two known experts in the area of AI in Education. The panel was part of the launch event for the new McGraw Center for Educational Leadership at Penn GSE. We had a good conversation, followed by rich discussions with a room of educational leaders from the Penn community and school districts.
Integrating AI in Knowledge Building
Earlier this week, I had the good fortune of joining a panel organized by my colleageus Seng Chee Tan and Alwyn Lee at the 2022 Empowering Learners in AI conference. Several colleagues including Leanne Ma (University of Tornoto), Chew Lee Teo (National Institute of Education in Singapore), and Mutlu Cukurova (University College London) were also on the panel.
While my colleagues on the panel presented some really exciting new research, I used my time to ask a series of questions. My “slides” can be found on natto.dev.
Network Motifs as Codes
I’ve been working on a framework of applying socio-semantic network analysis to discourse data.
Socio-semantic networks are two-mode, dual-layer networks that are made of actors (e.g., learners), semantic entities (e.g., words), and their relations. Socio-semantic network analysis brings together the study of relations among actors (human networks), relations among semantic elements (semantic networks), and relations among these two orders of networks (Basov et al., 2020). Such a dual-layer network analysis approach is not only useful for examining the duality of socio-semantic relations, it also applies to other settings such as socio-ecological analysis that’s interested in the interactions between social structures and ecological resources (Bodin & Tengö, 2012).
What role should A.I. play in education?
Listening to podcasts has become a new habit of mine during the pandemic, when cooking or doing dishes, on the way to pick up my toddler, or when my eyes need a break from the screen. Last week, I listened to an interesting episode of the EdSurge Podcast titled What Role Should AI Play in Education? A Venture Capitalist and an EdTech Critic Face Off. This episode features a discussion between Neil Selwyn and venture capitalist Ryan Craig. I thought the conversation was fantastic in many ways and encourage you to take a listen.
Knowledge Infrastructures: Initial Thoughts
Some initial thoughts on knowledge infrastructures and what it means for knowledge building.
- ← Newer
- 1 of 4
- Older →