New findings from a Harvard Business School study suggest that Bloomian cognitive tasks can integrate with Maslow's emotional needs to create flow states
I found this post particularly thought-provoking, especially when read alongside Nick Potkalitsky’s discussion of David Winter’s typology. I’m often asked why students need formalized instruction in AI use. But the growing body of empirical research suggests that effective use doesn’t arise intuitively just from exposure and need. Unlike traditional software like Microsoft Office, AI seems to require a different kind of mastery—one that develops through a structured, sequential learning process rather than through isolated encounters. This implies that both optimal learning and productive application depend on a carefully staged trajectory of acquisition. I wasn’t initially convinced that we need concepts like cognitive bleed to understand AI pedagogy, but this post (and Nick’s) reinforces the idea that AI now functions as a partner in understanding the world—much like mathematics or language.
Hi Scott. Many thanks for the comment. I am glad you are seeing AI pedagogy needs to go beyond the concept of tool use to something more like a "partner in understanding the world". In that sense, I like your analogy to language or mathematics -- they are both infrastructural to understanding the world. But I still think genAI's responsive/dialogical and generative capacities go beyond math and language.
The question is much more than that. What are we doing? And by "we", I mean you and me. Are we just bystanders watching AI companies put out products? Are we churning out endless papers about AI pedagogy that will disappear with the next big product? Is our work really relevant, or are we just part of the background noise? What are you and I, and people like us, doing that is really new? I believe it's crucial to keep this in mind all the time.
I found this post particularly thought-provoking, especially when read alongside Nick Potkalitsky’s discussion of David Winter’s typology. I’m often asked why students need formalized instruction in AI use. But the growing body of empirical research suggests that effective use doesn’t arise intuitively just from exposure and need. Unlike traditional software like Microsoft Office, AI seems to require a different kind of mastery—one that develops through a structured, sequential learning process rather than through isolated encounters. This implies that both optimal learning and productive application depend on a carefully staged trajectory of acquisition. I wasn’t initially convinced that we need concepts like cognitive bleed to understand AI pedagogy, but this post (and Nick’s) reinforces the idea that AI now functions as a partner in understanding the world—much like mathematics or language.
Hi Scott. Many thanks for the comment. I am glad you are seeing AI pedagogy needs to go beyond the concept of tool use to something more like a "partner in understanding the world". In that sense, I like your analogy to language or mathematics -- they are both infrastructural to understanding the world. But I still think genAI's responsive/dialogical and generative capacities go beyond math and language.
The question is much more than that. What are we doing? And by "we", I mean you and me. Are we just bystanders watching AI companies put out products? Are we churning out endless papers about AI pedagogy that will disappear with the next big product? Is our work really relevant, or are we just part of the background noise? What are you and I, and people like us, doing that is really new? I believe it's crucial to keep this in mind all the time.