November 9, 2026
This week in class, we read and listened to the work of Bianca Wylie and had the opportunity to hear from her directly. Her writing and speaking were engaging, particularly her examination of technology, public governance, privacy, and democracy. However, the idea that stayed with me most was a distinction she made between AI learning and AI literacy.
AI learning teaches us how to use the technology. AI literacy asks us to think critically about why we are using it, how it works, who controls it, who benefits from it, and what its use might cost us. This distinction gave me language for something I have been working toward throughout this course. I have become fairly comfortable using generative AI. I can write and refine prompts, question its responses, troubleshoot problems, and use it as a partner in planning and creating. These skills matter, but they represent only one part of learning about AI. Knowing how to operate a tool does not necessarily mean that I understand the systems of power surrounding it.
Wylie’s article, “In Toronto, Google’s Attempt to Privatize Government Fails—For Now,” illustrates why that distinction matters. Her discussion of Sidewalk Toronto is not simply about whether a particular technology worked. It is about who should control public infrastructure, how data is collected and used, and what happens when governments transfer public decision-making power to private technology companies. This brought me back to ideas I encountered much earlier in my undergraduate and graduate studies. In a 2017 post about New Literacy Studies and curriculum, I explored the difference between autonomous and ideological understandings of literacy. An autonomous view treats literacy as a neutral set of skills that people acquire. An ideological view understands literacy as a social practice shaped by culture, context, and power. Literacy is never just the ability to decode words; it influences whose knowledge is valued, who can participate, and who can make themselves heard.
AI literacy can be understood in much the same way. If we teach it as an autonomous skill, the focus becomes learning how to prompt effectively, produce useful results, and participate successfully in an increasingly automated world. Those skills may help students become effective users or future employees, but they do not necessarily help them question the systems into which they are being invited. An ideological approach to AI literacy asks different questions. Who created the system? What information was used to train it? Whose work and knowledge were included? Who was excluded? What happens to the information we provide? What values are embedded in the tool? What environmental resources does it consume? Who earns money from its use? Who is accountable when it causes harm? These are literacy questions because they are also questions about understanding, interpretation, participation, and power.
I returned as well to a post I wrote in 2023 asking whether schools should be responsible for teaching students to be good digital citizens. At the time, I argued that the question went beyond online safety or employability. It was connected to the larger question of what schools are for: Are we preparing students to become workers and consumers, or are we helping them become thoughtful citizens capable of participating in society and shaping the world they inherit? Wylie’s connection between technology and democracy makes that question feel even more urgent. When we give private technology companies our data, information, habits, creative work, and decision-making processes, we are not only making individual consumer choices. We may also be transferring forms of power. If governments, schools, and public institutions become dependent on privately controlled platforms, democratic decisions can gradually become technical or corporate ones. Wylie’s account of Sidewalk Toronto and her conversations about similar ideas in our class demonstrate how technological systems can influence public life while avoiding the forms of public accountability expected of governments.
Her connection to literacy, in this way, and democracy resonated with me. Literacy has long been connected to power. The printing press expanded access to texts and ideas, but being able to read, write, publish, and distribute information also affected who could participate. As technologies advanced, literacy had to change with them. Traditional literacy developed into media literacy, information literacy, and digital literacy. AI literacy is the next stage of that development, but it carries forward the same fundamental question: Who has the knowledge and power necessary to participate, question authority, and influence society? In my opening AI learning record, I wrote that there is a difference between knowing how to get a useful response from AI and understanding the larger system that produced it. I also recognized that students need more than rules about online behaviour. They need to make judgments about information that appears polished and authoritative but may not be reliable. Wylie’s distinction helped sharpen that uncertainty. I was describing the difference between AI learning and AI literacy without yet having the language to name it.
So, what was my AI learning this week? It was the realization that knowing how to use AI is only part of what it means to learn about AI. Prompting, generating, revising, and troubleshooting are useful skills, but they are not enough. AI literacy also requires us to examine privacy, data ownership, corporate power, public accountability, environmental consequences, bias, and the ways technological systems reshape our choices. Sure, this matters a bit for my own practice, but it matters even more for students and young people. They are learning to navigate a world in which powerful technological systems are increasingly presented as invisible or inevitable. Teaching them only how to use those systems prepares them to participate in the world as it has been designed for them. Teaching them to question those systems may help them participate in deciding how the world should be designed.
