Stephen Downes

This is a useful article meant for journalists but worth reflecting on for all of us. It outlines the PEER mnemonic (based on a previous post) for remembering the following four types of source: Power, Expertise, Experience, and Representative. Part of the issue with journalism (in my own opinion) is that writers unimaginatively return to the same old sources in each of these groups. That's why I prefer the PEER'D alternative, where D stands for diversity. Good journalism tries to get sources from a spectrum of each type of source - not always the same authorities, not always the same expertise. I think especially they should avoid deferring to (what they believe) is the 'elite' level for each of these, because this usually reflects wealth, connections and influence more than it does actual power, expertise, experience or representation.

Jon Dron applies something like a systems analysis to the question in the title, asking essentially, "what does it mean for higher education to work?" In one role, teaching, it doesn't perform especially well, due to conflicts with its other roles. But in another role, 'surviving', it has done remarkably well, having persisted for centuries and having expanded around the globe. I do question, though, whether this is true: "The main technological features that universities acquired in the first century of their existence are still fully present, in virtually unaltered form.  Courses, classes, terms/semesters, professors, credentials, methods of teaching, organizational structures, methods of assessment, and plenty more are visibly the same species as their mediaeval forebears, and remain the central motifs of virtually all formal higher education." Are they really? I wonder about that. (I suppose I could ask ChatGPT...)

This tool, which the website source reveals was created by Perplexity computer, compares 20 AI literacy frameworks across 9 domains. The domains are: technical understanding, practical application, critical evaluation, ethical reasoning, societal and systemic awareness, human agency and identity, governance and participation, cognitive and metacognitive processes, and sociocultural and critical orientation. My question is: what makes these good dimensions of AI literacy, over and above the fact that they may be extracted from the 20 literacy frameworks viewed? Minimally, a typology should be comprehensive and non-overlapping. But more to the point, what exactly is a 'literacy'? My own view is that a 'literacy' is a type of pattern recognition. What alternative characterization would a presentation like this offer? It seems circular - a 'literacy' is what people say is a literacy.

Colin Beer is usually sharper than this, so while I agree that knowledge and skills (as he defines them) are not enough, I think we need some clarity regarding what he calls 'dispositions' (it's not that he's wrong so much as he's fuzzy). He writes, "Dispositions represent the values, tendencies, and attitudes, such as motivation, mindset, professional identity and agency, that dictate how a professional actually navigates the "swampy lowlands" of practice. In simple terms, dispositions are the habits of mind and heart that shape how we show up when work gets hard." Dispositions are best described as tendencies, which may result from habits, or which may be subconscious tics. They should be contrasted with attitudes, which are states of mind regarding such things as values and truth. Expertise (in, say, the Dreyfus sense) is a matter of disposition, while professionalism is a matter of attitude. It's certainly arguable that an education should (help) shape both, but they are very distinct things, and are approached very differently.

I spent some time thinking about this short article that explores "the difference between good relationships and good transactions" where a 'good relationship' is "unique, organic, and empathetic, helping us understand when to invest in building relationships versus when a transaction suffices." What made me think wasn't the distinction itself, which seems straightforward, but the terminology used. The way 'relationship' is defined blends elements of different constructs - we have 'unique' and 'sustained', which to me describes a 'connection', but in addition there is the presumption that relationships are embodied, as evidenced by 'organic' and 'empathetic'. The connection describes the relationship itself, while the embodied element describes the thing that is related. The transaction side, meanwhile, describes the exchange that happens between two entities, as opposed to the connections between them. The world view of this article doesn't grant (or doesn't require?) embodiment for transactions to occur. I would ask whether the author intended to distinguish between embodied and non?-embodied entities here, or whether it's just phrasing.

The current issue of The Batch introduces readers to Context Hub, Andrew Ng's new tool to provide API documentation to your coding tools. The purpose of this is to make the tool aware of new tools and updates to existing tools, so they're not depending on out-of-date models. "Chub is built to enable agents to improve over time. For example, if an agent finds that the documentation for a tool is incomplete but discovers a workaround, it can save a note so as not to have to rediscover it from scratch next time." It's available on GitHub and installed using the node package manager (npm). It's the lead article in this issue; you can also read other AI news from the week.