Wonderful talk, Lance. So glad you picked up those themes (from 3 Years ago!!) and returned to them. This piece remind me of two ideas that I think are useful when thinking about change in higher ed. They have been on my mind, even more so lately.
Charlie Stross, the scifi writer, talks about corporations as "old, slow AI" and colleges and universities are among the oldest and slowest AIs around. Arvind Narayanan and Sayash Kapoor talk about AI as normal technology, meaning that when we think about the processes by which a new tech is adopted and adapted--that is diffused through society--they happen according to human norms and rules. After all, we've had self-driving cars for a while now...we just don't let them on the roads because we don't have confidence that they work well enough.
Institutions of higher ed are responding slowly to the threats and opportunities afforded by large AI models, and that's what we should expect. As you say, the work is not to speed things along, it is to think and take care, especially with how we engage students, in the work of figuring out how to use this technology to create educational experiences that are valuable in the broadest sense of value.
I'm seeing the same pattern in K-12. Individual teachers are adapting, but schools as institutions are struggling to move with any coordination. Meanwhile parents are sidelined, trying to figure out what their kids actually need while the system deliberates behind closed doors. Your point about students living inside the consequences of institutional indecision is important. For younger kids, it's parents absorbing those consequences on their child's behalf.
Looking back to look forward is genuinely the most useful lens for AI in education right now. Three years in and the split is becoming clear: the educators who kept asking "what is this for the learner?" have navigated it far better than those who chased features. I keep thinking about all the ed-tech waves that came before, the LMS obsession, the MOOCs, and how institutions that held onto their pedagogical anchors came through those transitions better. Same pattern here.
Wonderful talk, Lance. So glad you picked up those themes (from 3 Years ago!!) and returned to them. This piece remind me of two ideas that I think are useful when thinking about change in higher ed. They have been on my mind, even more so lately.
Charlie Stross, the scifi writer, talks about corporations as "old, slow AI" and colleges and universities are among the oldest and slowest AIs around. Arvind Narayanan and Sayash Kapoor talk about AI as normal technology, meaning that when we think about the processes by which a new tech is adopted and adapted--that is diffused through society--they happen according to human norms and rules. After all, we've had self-driving cars for a while now...we just don't let them on the roads because we don't have confidence that they work well enough.
Institutions of higher ed are responding slowly to the threats and opportunities afforded by large AI models, and that's what we should expect. As you say, the work is not to speed things along, it is to think and take care, especially with how we engage students, in the work of figuring out how to use this technology to create educational experiences that are valuable in the broadest sense of value.
I'm seeing the same pattern in K-12. Individual teachers are adapting, but schools as institutions are struggling to move with any coordination. Meanwhile parents are sidelined, trying to figure out what their kids actually need while the system deliberates behind closed doors. Your point about students living inside the consequences of institutional indecision is important. For younger kids, it's parents absorbing those consequences on their child's behalf.
Looking back to look forward is genuinely the most useful lens for AI in education right now. Three years in and the split is becoming clear: the educators who kept asking "what is this for the learner?" have navigated it far better than those who chased features. I keep thinking about all the ed-tech waves that came before, the LMS obsession, the MOOCs, and how institutions that held onto their pedagogical anchors came through those transitions better. Same pattern here.