Stephen Downes

You may not think of children's vaccines as educational technology, but I think of it as the second educational technology, following only the first: proper nutrition. This tech stuff we do? That only helps once we've addressed the rally major issues created by child poverty and misinformation about health. I'm old enought that I got all the diseases when I was a kid - measles, German measles, chickenpox, mumps. Everybody got them, and a few of the kids died. What would have been better than running that gauntlet? Immunization. "We have forgotten how many children used to die before their fifth birthday," writes Kristen Panthagani. I haven't forgotten. That's why I get a flu shot every year and make sure I'm up on my Covid shots. I've remained flu-free for years and years, and entirely covid-free. I hope to stay that way. Via Robin DeRosa.

It's not that this article is wrong. But as Gerald Ardito comments, "there is more than a little irony in this post, given EdSurge's long time tech boosterism." Tanner Higgin writes, "Within this technocentrist frame, education is sick and edtech is like medicine." How many studies do we see like this, asking "what works?" in education, as though the same goal were shared by all. But as Seymour Papert writes, "The content for human development is always a culture, never an isolated technology." And in particular, "The potential of technology is significantly affected by the humans that use it and their context."

This short article references a 2022 paper that studies how the order of questions in a test impacts how well the test-takers do. "A perfectly logical test-taker should do equally well no matter the order of questions," writes the author, "but research shows that humans are influenced by the order." In particular, "students answered more... questions (correctly) when they began with easy questions compared to when they started with difficult ones." The relevant question to ask here is this: if the result depends so much on the order of the questions, what is it exactly that the test is measuring?

I often ask what it is we're doing when we're teaching and learning, that is, what counts as success? This paper offers some insight into the sort of question I have in mind. To 'learn' something is more than just to come to know that something is true. That's just memorization. No, as David Bourget would argue here, it's to grasp what is being taught. "It is one thing to believe something, and it is another to grasp it. For example, everyone knows that life is short, but most of us arguably do not fully grasp this fact." But what is it to 'grasp'? He argues, "we grasp to the extent that our thoughts are grounded in experience, whether occurrent or non-occurrent... , what we experience matters to how we reason because that is how we are wired: consciousness isn't a late addition to our minds; it is the
most central, causally potent form of mental activity." I think this argument works, overall. Image: Ding.

This paper (21 page PDF) is a review of papers proposing AI competence frameworks for teachers (CFT) and organizes them into five categories: integrating AI competencies in existing CFT; modelling new AI knowledge domains; process-driven; AI systems-driven; and competence level-driven. True to their field, the authors stress the need to theorize. "The empirical and design-based nature of AIED requires a solid theoretical foundation. The adoption of theoretical frameworks serves as a unifying force, fostering shared concepts and terminology among researchers and designers." But it's not clear there is (or is going to be) agreement on just what to theorize. This depends on what we want teachers to do, and as the authors note, "it is important to understand what kind of problems AI teachers' competencies will solve and what kind of solutions AI teachers' competencies convey."

This article offers three examples of the use of AI in a learning concext (it's a bit too early to call them 'success stories' since we don't really have a story of what counts as 'success' in this context). In one example, students are provided "with customisable prompts that they can cut-n-paste from a shared GoogleDoc to save them typing the prompt in." In the second, a 'learning lead' "used Microsoft Copilot to create imaginative discussion prompts for students." In the third, a senior lecturer aims "to normalise the use of GenAI" by putting it in the handbook and offering training sessions.