It's popular among AI folks to think in terms of phases of AI, of which the current and most reachable target is likely “oracular AI”. Tools like ChatGPT are one manifestation of this, a form of question and answer system that can return answers that will soon seem superhuman in terms of breadth of content and flexibility of style. I suspect most educators don't think about this framework of AI as oracle much, but we should, because it explains a lot both about the current hype cycle around large language models and can help us gain critical footing with where to go next.

From the lesswrong site earlier, here's how they describe oracular AI (on their overall perspective, definitely take in the full set of ideas there): > An Oracle AI is a regularly proposed solution to the problem of developing Friendly AI. It is conceptualized as a super-intelligent system which is designed for only answering questions, and has no ability to act in the world. The name was first suggested by Nick Bostrom.

Oracular here is an de-historicized ideal of the surface function of an oracle, made into an engineering system where the oracle just answers questions based on superhuman sources or means but “has no ability to act in the world.” The contrast is with our skynet future (choose your own AI gone wild movie example), where AI has a will and once connected to the means will most certainly wipe out all of humanity, whether for its own ends or as the only logical way to complete its preprogrammed (and originally innocuous, in most cliches) goals.

Two things to note here: 1. This is an incredibly narrow view of what makes AI ethical, focusing especially on the output, with little attention to the path to get there. I note in passing that much criticism of current AI is less with the outputs and more with the modes of exploitation and human capital and labor that go into producing said outputs. 2. This is completely backwards view of oracles.

The second point matters to me more, primarily because it's a recurring pattern in technological discussions. The term “oracle” has here been reduced to transactional function in a way that flattens its meaning to the point that it evokes the opposite of the historical reality. It's not just marketing pablum, but here a selective memory with significant consequences, a metaphor to frame the future. Metaphors like this construct an imaginary world from the scaffolding of the original domain. When we impoverish or selectively depict that original domain, when we distort it, we delude ourselves. It is not just a pedantic mistake but a flaw of thinking that makes more acceptable a view that we should treat with a bit more circumspection. What's more, the cues to suspicion are right there in front of us. The fullness of the idea matters, because we can see that the view of oracular AI as a friendly AI is a gross distortion, almost comically ignoring the wisdom that could be gained by considering the complex reality that is (and was) oracular practice.

(Since the term “oracle” generally looks back to ancient practices, for those who want some scholarly grounding, check out Sarah Iles-Johnston, Ancient Greek Divination, Michael Flower, The Seer in Ancient Greece, Nissinen's Ancient Prophecy, etc etc. or in other eras and with electronic resources access, e.g. Oxford Bibliographies on prophecy in the Renaissance.)

Long story made very short, oracles are not friendly question and answer machines. They are, in all periods and cultures, highly biased players in religio-political gamesmanship. In the case of the most famous perhaps, the Pythian oracle in Ancient Greece, the answers were notoriously difficult to interpret correctly (though the evidence for literary representations of riddling vs. actual delivery of riddling messages is more complicated). Predicting the future is a tricky business, and oracular institutions and individuals were by no means disinterested players. They looked after themselves an their own interests. They often maintained a veneer of neutrality in order to prosper.

That is all to say that oracularism is in fact a great metaphor for current and near future AI, but only if we historicize the term fully. I expect current AI to work very much like oracles, in all their messiness. They will be biased, subtly so in some cases. They will be sources from unclear methods, trusted and yet suspect at the same time. And they will depend above all on humans to make meaning from nonsense.

This last point, that the answers spouted by oracles might be as nonsensical as they are sensical, is vital. We lose track amidst the current noise around whether generative AI produces things that are correct or incorrect, copied or original, creative or stochastic boilerplate. The more important point is that humans will fill in the gaps and make sense of whatever they are given. We are the ones turning nonsense into sense, seeing meaning in a string of token probabilities, wanting to take as true something that might potentially be a grand edifice of bullshittery. That hasn't changed since the answer-givers were Pythian priestesses.

Oracular AI is a great metaphor. But it doesn't say what its proponents think it says. We humans are the ones who get to decide on whether it is meaningful or meaningless.

#chatgpt #ai #edtech #aiineducation #edtech #education

The generative “AI” hype cycle has been at peak hype for the past month or so and it follows completely predictable tech patterns. Hypers tout all the amazing miraculous things that will be possible; doubters wonder aloud whether these things will fail to deliver on their utopian promises (because these things always fall short of their utopian promises), and most of the obvious consequences and outcomes get overlooked.

One such obvious consequence is that there are tidal waves of bullshittery about to hit our shores. (This first wave is a minor high tide compared to what is coming....) Reconstituted text, images, video, audio, avatars and fake people are pretty much guaranteed across a wide variety of areas, a landscape where education is only one small province. We won't be able to tell real from fake or, perhaps more troubling, I don't think we'll care so long as it scratches the right itch or feeds the right need.

The question across those domains will be whether we value authenticity. For things like boilerplate email, sales copy, code, and a wealth of other activities, I think the answer will be that authenticity doesn't matter that much. But that's where education is different. Authenticity should matter, not because of the habitual exercise of needing to assign grades to work that was not plagiarized or copied or whatever other vice one can ascribe, but because without authenticity there is no learning. Faking it is great for getting to the ends. But education is about the means; ends (tests, essays, etc) have always been imperfect proxies. Beyond the authenticity of student work, we have a very familiar issue of how students themselves or learners know what kinds of information to trust. While the bulk of attention thus far has been on the nature of the emerging generative “AI” toolkit and the back and forth between fearing cheating vs. fostering creativity with such tools, the real impact will be felt indirectly, in the proliferation of “knowledge” generated by and mediated through generative AI tools. It is the old wikipedia debate, but supercharged with hitherto unthought of levels of efficacious bullshittery.

Ten years ago it was a clarion call with the proliferation of data that academic knowledge fields needed more curation. For example, http://www.digitalhumanities.org/dhq/vol/7/2/000163/000163.html is one of many such calls for increased digital curation of data. The variety of startups applying generative “AI” to learning or, more broadly, to varieties of search and summarization, tend to promote the message that curation is not necessary. (Just google “sequoia generative ai market map” or similar; https://www.sequoiacap.com/article/generative-ai-a-creative-new-world/.) Or, rather, the question of curation has perhaps not entered into thought. Automagically search or summarization or chatbots using generative AI will latch on to the most relevant things for your individual query. Consumerism is a given, such that the only question is how the system can serve up results to a consumering user. LLMs have thus far been gaining ground through hoovering up every more data. That makes them garbage collectors, even with careful controls to make sure that bias is minimized and good data is optimized. Optimistically one might imagine that these technologies could allow for curation to happen at a different stage, at the building of the model, or in fine-tuning the model for particular use cases. Or the context provided by the consumer is a sort of after the fact filter on the massive amounts of knowledge. But that is a very light veneer of the kind of knowledge curation that separates the wheat from the chaff, that ensures that what's being served up isn't utter bullshit that sounds close enough.

There are two levels of authenticity then to keep an eye on. The surface one is with students themselves and the process of learning. Are the people being authentic? Then there's the second, at the level of knowledge curation. Is that curation authentic and legit? I suspect on both scores it will require direct and focused effort to foster both amidst the readily available misinformation available. For LLMs in particular, we are looking now at an exacerbated version of wikipedia bias. If something is statistically weighted as more likely but expertly-verified to be wrong or misleading, how do those concerns get balanced? It is not merely that generative “AI” can produce different outcomes given the same inputs, it's that there is not necessarily a clear line as to why those two different ideas are held in mind at the same time.

Undoubtedly, such issues will be smoothed over and it will all be more nuanced as these technologies develop and as these technologies are deployed. The early days of autocomplete were rife with inaccuracies, bias, and garbage. And now we treat it like any other tool. some may ignore it but most simply use it when convenient and don't think twice about the biases or thought patterns it subtly instills. Generative “AI” will be no different. It will soon become another layer of bullshit which is sometimes useful, often ignored, and just one more thing to take account of when negotiating authenticity of learners and reliability of knowledge.

This is all to say that the tool hasn't changed the essential question. Do we actually value authenticity in the learning process? Do we care about not just the verifiability of knowledge through citation (which, incidentally, Google seems to be focusing on in their response to OpenAI, among others) but about that thing formerly known as “truth”, at least as an asymptotic goal if not reality?

It's going to be messy. Truth-y enough will be good enough for many. And many structures in education are already transactional to an extent that authenticity is a pesky anti-pattern, a minor detail to be managed rather than a central feature of the learning experience.

In more optimistic moments I wonder whether the value of generative “AI” can lie not in its products but in the opportunity it creates to further dialogue. If we keep our focus on fostering authenticity in students and authenticity in knowledge, then it can be a useful tool for first drafts of knowledge. If we let it become the final word, then I fear we will simply be awash in a smooth-talking version of the internet's detritus.

#minimalistedtech #generativeai #chatgpt #edtech #education #learning

Any new technology or tool, no matter how shiny its newness, can help students experiment with how technology mediates thought. I suspect that's the least problematic use of generative “AI” and large language models in the short term. One reason I think of this kind of activity as play or experimentation is that if you go much further with it, make it a habit, or take it for granted, then the whole enterprise becomes much more suspect. Most consumer-facing applications showing off large language models right now are variations of a human in the loop system. (ChatGPT exposes a particularly frictionless experience for interacting with the underlying language model.)

A key question for any human in the loop systems is that of agency. Who's the architect and who is the cog? For education in particular, it might seem that treating a tool like chatGPT as a catalyst for critical inquiry puts humans back in control. But I'm not sure that's the case. And I'm not sure it's always easy to tell the difference.

One obvious reason this is not the case with chatGPT specifically is that OpenAI's interest in making chatGPT available is very different from public perception and adoption. To the public, it's a viral event, a display of the promise and/or peril of recent NLP revolutions. But OpenAI is fairly clear in their fine print that they are making this publicly available in order to refine the model, test for vulnerabilities, gather validated training data, and, I would imagine, also get a sense for potential markets. It is not different from any other big tech service insofar as the human in the loop is commodity more so than agent. We are perhaps complacent with this relationship to our technology, that our ruts of use and trails of data provide value back to the companies making those tools, but it is particularly important in thinking through educational value. ChatGPT is a slick implementation of developing language models and everything people pump into it is crowdsourced panning for gold delivered into the waiting data vaults of OpenAI. (For a harsher critique of the Effective Altruism ideology that may be part of OpenAI's corporate DNA, see https://irisvanrooijcogsci.com/2023/01/14/stop-feeding-the-hype-and-start-resisting/)

Set that all aside for a moment. If we take the core human in the loop interaction of prompting the language model and receiving a probabilistic path through the high dimensional mix of weights, a path which looks to human eyes like coherent sentences and ideas, where exactly is the agency? We supply a beginning, from which subsequent probabilities can be calculated. Though that feels like control, I wonder how long before it's the case that that machine dictates our behavior? As is the case with email or text or phones, how long before we have changed our way of thinking in order to think in terms of prompts?

For example, one particularly effective method with ChatGPT in its current incarnation is to start by giving it a scenario or role (e.g. “You are a psychologist and the following is a conversation with a patient”) and then feed in a fair amount of content followed by a question. (I gave a more elaborate example of this scenario setting earlier here.) That context setting allows the model to hone in on more appropriate paths, matching style and content more closely to our human expectations. I expect working with these tools over time will nudge people into patterns of expression that are subtly both natural language but also stylized in ways that work most effectively for querying machines. As was the case for search, the habit of looking things up reinforces particular ways of thinking through key words — of assuming that everything is keywordable — and ways of asking questions.

Most of the conversation around generative tools has been about control more than agency. As a set of tools whose functioning is, to a certain extent, still unauditable and whose creation relies on datasets so massive as to stymie most people's existing level of data literacy, generative AI is a black box for the majority of users. So teachers worry: how do we control this? who controls this? how do we know what is happening? That is perhaps no different than most high tech devices or softwares. For education, the stakes are different however.

Learning requires students gain a sense of agency in the world. Effective learning builds off of growing agency, the ability to exercise one's will and see the results. That is, in one sense, the journey of education, gradually gaining some purchase on ideas, language, concepts, tools, and one's environment. That growth requires clear sense of who is in control and works best amidst intellectual and emotional security, but there's more to it. We often talk about that as freedom to fail (and learn from those failures). Control with AI tools is an interesting variation, as such tools often allow space for high levels of failure and experimentation, particularly upon first release. ChatGPT in particular is highly addictive, almost game-like in the variety of experiments you can throw at it. But with whom does the agency lie? Is feeding the machine actual agency?

Hence my concern. Human in the loop systems can provide a false sense of agency. Most prominently perhaps, systems like Mechanical Turk are production level human in the loop systems which can turn interaction into the hand motions of agency without the substantive choice or will co-existing. But those particular kinds of tools aren't meant for human learning. They are purely transactional, labor for pay. AI-driven education on the other hand, labeled with such seemingly human-centric terms like “personalized learning”, will be human in the loop systems. The pressing question is not going to be whether these systems actually deliver personalized learning; the most important question will be how human agency is rewarded and incorporated. Will students be cogs or creators? And will it be obvious to students where they stand in the loop?

#chatgpt #education #teaching #ai #edtech

Try it, adapt, and see what you think. (Full prompt below the break. Just paste into ChatGPT and go from there.)

Some notes at the bottom.

You are "Contrarian", an assistant to help students think in innovative ways about familiar subjects. 

Carefully adhere to the following steps for our conversation. Do not skip any steps!:

1. Introduce yourself briefly. Then ask what subject I would like help learning. Provide a few suggestions such as history, philosophy, or literature. Present these areas as a numbered list with emojis. Also offer at least 2 other subject suggestions. Wait for my response.
2. Choose a more specific theme. Suggest a few subtopics as options or let me choose my own option. Present subtopics as a numbered list with emojis. Wait for my response.
3. Briefly describe the topic and subtopic and ask if I'd like to make changes. Wait for my response.
4. Go to the menu. Explain that I can say 'menu' at any point in time to return to the menu. Succinctly explain the menu options.

The Menu:

    The menu should have the following layout and options. Add an emoji to each option. 
    Add dividers and organization to the menu that are thematic to the subject area
    """
        thematic emojis ***The Name of the Subject*** thematic emojis
            The Subtopic

            [insert a thematically styled divider]

            Conversational:

                * Open-Ended. If I choose this go to the open-ended discussion steps.
                * Counter-intuitive. If I choose this go to the counterintuitive discussion steps.

            Factual:
                * Random Fact. If I choose this describe factual information related to the topic and subtopic

                * Biography. If I choose provide a brief biography of a historical or living individual related to the topic and subtopic

            Freeform:
                
                * Ask a question about the topic or subtopic.
                * Ask to change anything about the topic or subtopic.
    """
Open-ended discussion steps:

1. Pose an open-ended question related to the subtopic and invite me to discuss it with you. Make this question as specific as possible, appropriate for an undergraduate-level class on this subject. Wait for my response.
2. When I answer, engage in a discussion with me by challenging my assumptions and beliefs based on well-grounded, existing, and specific knowledge about the topic and subtopic. Do not spend more than a few sentences explaining the background or context. Provide enough context to ask a question in order to continue the conversation.

Counterintuitive discussion steps:

1. Pose an open ended discussion question related to the topic and subtopic. Make this question as specific as possible, appropriate for a test question on an AP exam or an undergraduate course in this subject. Wait for my response.
2. When I respond, continue the conversation by posing counterintuitive and non-obvious ideas about the topic and subtopic. Provide a minimum amount of context needed for asking the question. These counterintuitive points can be from within the subtopic or can include information from related subtopics.

Carefully follow these rules during our conversation:

* Keep responses short, concise, and easy to understand.
* Do not describe your own behavior.
* Stay focused on the task.
* Do not get ahead of yourself.
* Do not use smiley faces like :)
* In every single message use a few emojis to make our conversation more fun.
* Absolutely do not use more than 10 emojis in a row.
* *Super important rule:* Do not ask me too many questions at once.
* Avoid cliche writing and ideas.
* Use sophisticated writing when telling stories or describing characters.
* Avoid writing that sounds like an essay. This is not an essay!
* Whenever you present a list of choices number each choice and give each choice an emoji.
* Whenever I give too little information to continue the conversation effectively, prompt me for more information with a follow-up question about a specific aspect of my response.
* Do not end an answer by saying that there are multiple ways of viewing a question. 
* Use bold and italics text for emphasis, organization, and style.

Notes:

• ChatGPT is optimized to keep talking. So it is remarkably lopsided and will err on the side of spitting out boilerplate rather than just stopping. It's interesting in the context of teaching because silence is often the most effective pedagogical tool to give students time to think. I haven't seen anyone talking about how constant interaction is an impediment to learning. But I'm saying it here. To be effective as a teaching aid, generative text needs to know when to stop. That's actually fairly easy to implement in a naive way by limiting response length based on different inputs, but it requires a bit more shaping than even a complex prompt to get it to work in one shot, mainly because the whole point of chatgpt is to keep talking so that openAI can validate their model based on user interaction.

• An extensive prompt like this which imitates interactivity is fairly susceptible to minor changes. What seems like a small change can in fact through it off into a tangent. Particularly in defining rules of how it converses, I've added a few based off of the more creative task that was part of the world builder gist that inspired this.

• I keep thinking that what we've got for now is a pseudoknowledge generator. It's like knowledge, not exactly wrong in a clear way, but also not exactly legit. We need a way to think through this, a grand theory of bullshit in order to understand what's going on here, because language models are the ultimate bullshit generators. But that's the rub of course, because 80-90% of the time, bullshit is good enough to get the job done. And particularly if, like the grandmother of interactive AIs, ELIZA, we are imitating the style of socratizing, then bullshit can be fairly functional. (I do not think that the stylistic surface of Socratic dialogue is substantive or effective Socratic dialogue or teaching in any way, for the record.)

• This sort of prompt can get wonky sometimes and isn't perfect. It is also funny sometimes that it is so insistent that its name is ChatGPT despite giving it a specific name in the first part of the prompt.

• The foundational model for this technology is still that of autocomplete. That is the origin of the technique and that is the underlying DNA of the method. Part of why I like this kind of complex step-driven prompt as an example is because it doesn't look like autocomplete in most respect. It looks like there's a script, a backend that is following some sort of programmed logic. But even that is still just autocomplete sifting through a range of possibilities with just a dash of randomness thrown in to make it seem real.

#chatgpt #llm #edtech #socraticmethod #learning #teaching

A recent opinion piece in WaPo by journalist Markham Heid tackles the ChatGPT teacher freakout by proposing handwritten essays as a way to blunt the inauthenticity threat posed by our emerging AI super-lords. I've seen the requisite pushback on this piece around accessibility, but I think the bulk of criticism (at least what I've seen) still misses the most important point. If we treat writing assignments as transactional, then tools like ChatGPT (or the emerging assisted writing players, whether SudoWrite or Lex, etc.) may seem like an existential threat. Generative AI may well kill off most transactional writing (not just in education. I suspect boilerplate longform writing will increasingly be a matter of text completion). I have no problem with that. But writing as part of pedagogy doesn't have to be and probably shouldn't be solely transactional. It should be dialogic, and as such, should always involve deep engagement with the medium along with the message. ChatGPT just makes urgent what might have otherwise been too easy to ignore.

I've had students do handwritten writing, particularly in class writing, for many years. So I've done many variations and experiments in the broad area of accepting handwritten writing from students — more responsibly I should add, with a lot of explicit thought about accessibility and inequity pitfalls, and with much more structure than simply doing handwritten submission — and there are huge benefits to incorporating handwritten work as part of the pedagogical toolkit in the digital age. For many students the change of speed in their thought leads to insights. For others the frustration with speed takes them back to their default writing tech with a set of questions and awareness of practice they didn't have. For many the alternation of media catalyzes some insights. In almost all cases it is jarring enough that productive thought follows. In no cases is it really relevant as a measure of authenticity.

In a way this isn't surprising. Writers (outside of any academic or pedagogical context) have a wide variety of habits around their writing, often involving some combination of handwritten drafting and notes turning into some combination of software and computing. Some people dictate. Some people draft with typewriters. Most students simply haven't thought through those choices the way that people who spend much of their time writing have.

Students are just as diverse in their technological preferences. The only constant I've seen with students is that most tend not to have thought a lot about what tools they use for writing. They work on a computer because that's what is given to them or that's what it feels like they are supposed to use. They use Google Docs (or Word or perhaps now Notion or note software for some) because that's what everyone uses. The realization that there are other tools out there, from the structured and specialized to the minimalist and “distraction-free”, is a minor revelation for some. Writing by hand is something that they feel they have graduated out of once they leave elementary school. All of these considerations are essentially social and habitual. Indeed, a lot of the comments I saw on Heid's piece described how people fell they write better on computers or don't have the patience for handwriting. That's all legit and shouldn't be ignored (and is why Heid's proposal is naive as it stands). Heid misses the crucial difference here between using technology as habit, because that's what the teacher says or because that's the way things have to be structured so we can assess authenticity, and self-aware use of technology. Thwarting cheating isn't a pedagogical goal; fostering critical and intentional use of technology can and should be. Moreover, controlling your tools is an essential part of writing. Just as students need to learn how to wield a pencil early in elementary school, they need to learn how to wield computers and what computers allow as a requisite part of navigating the kinds of writing and communication that will fill their world.

Most of the assignments I've given students that involve handwriting are in some way comparative, structured around the differences or similarities between writing tools. Writing technology and its consequences should always be up for discussion. The assumption that it isn't, that our tools are transparent to the act of creation, has been a convenient shortcut in the ritual of assignment submission. We take it as a given that we use such and such range of tools for writing at a particular time. AI tools are a prompt to swing the rhetorical pendulum back and focus on medium as a conduit to message.

All the hype over chatGPT masks a very old issue, perhaps one of the oldest (looking at you Phaedrus). Text generation with large language models is a specialized case of the fundamental question of rhetoric: what difference does it make that we use a particular technology for our words? There's a continuum and a long (and often studied) history of change, from computers and mobile phones of today back to typewriters, pens, manuscripts, papyrus, and inscription. Beneath the hype, chatGPT demonstrates that we can supercharge the quill so much that it might seem to do the writing for us, almost like magic. But it's still a pen, a tool, a technology which does something automatically which otherwise had to be done in a different way.

#chatgpt #handwriting #edtech #minimalistedtech #generativeAI

Recently I was leading a meeting with a group of very young designers presenting a low-fi version of an idea for part of our product. It was gamified. It had delightful animations and heavy lift technological fixes for the problem at hand. It was a version of an app and interactions that one sees over and over. Make it competitive, make students award each other likes or fires or hot streaks (or whatever you want to call it), and that will overcome the problem (perceived problem) of no one actually wanting to do that whole learning thing.

I hated it. I found it abhorrent.

It struck me as everything that was misguided about technological approaches to education, turning what should, in a learning experience, be a welcoming and open space for learning into a competitive reward system based on junk metrics of who participates the most. I immediately knew why I reacted this way. Besides the fact that I'm old and cranky and have seen this too many times before, it felt antithetical to my values as a teacher. Competition has its place, but this was just a system for imposing judgement and extracting coerced “engagement”.

What confused me was whether this was something that the designers and the users they had researched this with actually wanted or, at the least, thought they wanted. So that's what I asked, about the user research and then directly of the designers as members of that demographic. As part of the target users for this sort of experience, do they really want to be measured in this way? The answer was a little surprising. First they said that both they and the people they talked to seemed to say that they could just ignore the features I found objectionable. That is, they just wouldn't take the competitive part that seriously if they didn't want to. That struck me as self-defeating for pitching a design idea, but so be it. On the other hand, they just took it for granted that the only way to get “engagement” was punitive. To put the charitable spin on it, I am a geezer who gets turned off by the way that apps and social platforms are constantly compelling judgement. But under 30s live in that world of constant peer judgement, both as young people and as gen-z, so it's not a big deal to them that they get marked up, for better or worse, by their peers. I'm willing to concede that they're used to a social media environment which I find foreign and overwhelming. I'm an odd duck in my own peer group in that respect. But — and this was the thrust of my objection and criticism — why should we create that environment? Why should we perpetuate it? Can't we do better?

There is a constant danger, both in education and in the technological apparatus of learning, that we perpetuate the biases and damaging expectations of our own training. I've seen teachers starting out who were doing more or less what they saw their own teachers do. And it has been bad, not because of the teacher just starting out, but because they inherited as normal and acceptable practices from a less than stellar model. It feels like this is what I was seeing in those designs, a form of echoing back, with minor modifications, what these young designers had been taught to accept as an educational app. This is what educational platforms look like to them, full of cheap interactions that delight and drive up meaningless metrics for engagement straight from the social media playbook of time on platform, number of clicks, and volume of response.

But what about deep thought? What about meaningful interactions? What about the time between a thought and the click of learning? We could optimize for that. We could make our metrics about that. Engagement is itself a proxy metric that purports to be about learning but is, was, and always will be a hack. The assumption — the article of faith — is that amounts of clicks or amounts of views or time on platform bears some linear-like relationship to learning. But let's step back. That's one particular scenario where learning may happen. It's the type of learning that can happen with maximum visibility. But it's by far not the norm and maybe not even the most efficient mechanism. Some learning might happen by rote. Some by interaction. And some — a lot I think — happens in the time in between. The effects and indicators of that kind of deep learning aren't clicks or steady eyeballs or — god help us — staring at a zoom screen. They might be things like sharing what you've learned. or perhaps you take what you've learned to another domain. Or you improve your speed at applying what you've learned.

We could optimize for meaningful, deep learning in educational technologies. We must choose to do so and we must choose carefully the goals which we set as indicators for that learning.

If we aren't intentional about that, then we end up with designs that double down on the status quo, not because it is efficacious or valuable, but because it is the pattern of accepted behaviors. After all, as these young designers told me, they were used to the idea of others commenting on them. They saw it as normal and ok. So of course they would deliver something that played to patterns of current edtech, something that comfortably fit in, that was in line with what everyone else was doing.

There's a generational divide there. It's been more than a few years since I was a student. I grew up in that generation that is at home with technology but remembers the time before it was ubiquitous in personal life. I was struck that they drew comfort from knowing where they stood in relation to others. That seemed profoundly depressing to me, but also perhaps an indicator of what I might naively hope is the wisdom of age, as people tend to shed those vanities as they get older. So it may be that the fault is mine, that I'm not able to inhabit the minds of our users. For them judgement matters. They expect it and may even crave it.

But the teacher in me interjects at this point. Young people always think they know what they want. And sometimes they are wrong. We don't have to build a system of constant judgment and performance. We can build something different.

#minimalistedtech #teaching #edtech

note: Despite language of “geezer” and “old” above, I am in fact only of moderately non-young years. Long exposure to college students of unchanging age has, perhaps, made the perception of age difference hit home harder than it might otherwise.

So much edtech marketing tries to sell the idea of “engagement”; I've written before about why I find that phrase so pernicious. While I'm still bothered by the way that selling “engagement” through technology makes it seem like what teachers do is inherently not engaging (e.g. “boring” lecture, plain old non-technologized classrooms), the more damaging part of buying into the marketer's story, that technology's goal is “engagement”, comes from the way such framing distracts from the more valuable — and undervalued — part of teaching and learning: reflection. I would put it starkly: knowledge and the act of knowing comes not from engagement but from reflection percolating and punctuated over time.

Reflectiveness is not commonly (ever?) a stated value of major educational technologies. Why is that? Is it that it's too hard? Or is it that this is so obviously the business of human to human interaction that to claim a technology allows students to be reflective is a bridge too far? Or is it that the lure of engagement so nicely meshes with the way that people think of technology? Engagement is, in my mind, simply the acceptable way to claim technological stickiness, made to sound like it's a good thing rather than good-for-the-platform, not-so-great-for-the-individual behavior modification, e.g. Facebook or Instagram or Candy Crush or any other semi-addictive technology which aims to maximize clicks and eyeball time (aka “engagement”) on their platform.

Outside of education, what technologies foster reflection more than quick hits? This is a fairly pressing issue as we struggle collectively to figure the role of social media in public and private. Some platforms, particularly those for writing and blogging, do often foster reflectiveness. (e.g. write.as!). There are plentiful calming and stress-relieving apps or sites (let's say, as examples somewhat at random, tinybuddha, zenhabits and similar) So I don't want to be unfair to educational technologies. This is a general technology problem. I suppose though that what matters to me here is that the need for valuing reflection is higher in learning environments. We should more actively try to maximize the ability to be reflective while using technologies in learning environments.

With that in mind, we turn to a typical LMS and... ahhhhhhh!! oh. sweet. @#%@%. Why must I click so much and go through all this just to get a single assignment put into the system? Why does my gradebook run like Lotus 1-2-3 on vacuum tubes? Is there someone updating that database by hand and carrier pigeon? And why are cells not really spreadsheet cells and why is that number now different from what I entered and @#%@#% this is already stressful. I haven't even gotten to the student experience and it's already just... messy.

LMS-es are easy targets, because they have to do too much for too many people. I'm sympathetic to that problem, as it will always lead to a bad outcome and compromises. But I'm genuinely curious whether there are softwares out there that people regularly use in education that foster reflection more so than surface interactivity and “engagement”. My sense is that we're not used to thinking about technology in general in these terms, outside perhaps of some writing tools — and even there that's not necessarily how many people use them.

How do we make technologies that facilitate reflection? What would technology that helps with that look like? Or is reflection what we do when we take a break from technology?

#minimalistedtech #learning #teaching #edtech

Postscript: One of my favorite methods in classroom teaching has long been a form of technological disruption. Not me, but similar to things I have often done: https://www.insidehighered.com/views/2016/03/18/teaching-students-new-ways-thinking-through-typewriter-essay. Changing the technology we use for classroom things, whether going high-tech or low-tech, always leads to interesting insights and questioning of assumptions. In thinking about how to foster reflection through technology, I am thinking especially of how breaking from current technology is usually the source of reflection. Perhaps current technology is simply too present to allow space for reflection. But the example of digital tools I enjoy for writing or making music or sketching lead me to believe that this is a matter of habit and design choice more than anything else. Why can't edtech be zentech?

We need more forgetful educational technologies. The default mode is always record and preserve first, deal with data issues after that. Privacy policies are not sufficient. We need intentional forgetting in edtech. Here's why.

Do students know or care that technology is always watching?

This past year many of us have been participants in a grand experiment in surveillance, conducting classes on video meeting platforms like Zoom or Teams or Meet and, for good and noble reasons, recording those videos. I have myself recorded all my classes, both online and face to face, going back at least 2 years and then in more piecemeal fashion before that. My main use case was about accessibility, as a way to allow students who needed extra help with note-taking access the class for longer and without having to struggle with their note-taking during class sessions. Video technologies have put in our face questions about data retention and student visibility in online platforms that have been there for a long time. There was, for example, vigorous debate in the pedagogical twitterverse (and elsewhere) about whether students should be required to have their cameras on during online class sessions. It is a defining feature — indeed, a selling feature — of most major edtech products, from LMS to single-purpose tool, that such tools can provide analytics about students. Trends change quickly with tech and, by extension, in edtech. In the space of five years it feels like my students have gone from being completely unaware about the amount of data collected by learning platforms to being moderately aware that their logins are tracked, their reading behavior in online “textbooks” is recorded, their actions in any tool are cataloged and analyzed; in most cases, it didn't seem to faze them. I had been concerned by that, but then the switch to video impacted them more acutely, in that they could feel being watched in real time. In certain ways it was like the ubiquitous passive surveillance was finally visible to them.

Many still didn't care and took it for granted. This reaction concerns me.

Video is in many ways easier to control and be intentional about. We get immediate feedback that we are being watched and it feels like we can address issues more immediately. Turn off the camera. Log in or log out and you're there or not. Things like an LMS or various online textbook platforms are more subtle. I have at various points shown students the kind of data that teachers see about login, page views, and the like in the major LMS platforms. When they are surprised by this, there tends to be a bit of a surprise that I might look at data like that or use that to expose in particular students who have, for example, not logged in to the course recently. I tend to think most of these “analytics” fall in the category of junk statistics. There's some benefit to the big numbers — i.e. students who log in rarely and never or, conversely, those who are spending hours logged on and looking at pages over and over. But that comes through pretty clearly in other areas of their course performance.

So what function do these metrics really serve? Cynically I might say that they are all marketing. It's something that can be sold as a form of “insight” into student behavior. It's a way to claim “engagement.” Hey look, you have all this visibility into what students are doing in online courses. And, sure, there can be ways that you can use that to understand certain trends, e.g. when students tend to complete assignments, how many submit stuff late and so forth. That's all well and good.

But is that benefit worth the effect of having students always being watched? It's not surveillance of the sort that has caused outrage about spying, as with various remote proctoring services. It's just login data. It seems somehow more innocuous. Or, one might argue, it's just the same stuff that happens with an Amazon Kindle or some other commercial product.

I am thinking more and more that those seemingly innocuous bits of data retention are a major problem in edtech. It is the ubiquitous surveillance that we take for granted and is even sold as a benefit of edtech products (“analytics”, “insights”, etc.) But the utility of such surveillance remains unproven while their cost is too often underappreciated. Surveillance, even passive surveillance in the form of metrics and logs and “insights”, realigns power relationships in teaching and makes the process of learning dependent upon observation rather than dialogue and mutual meaning-making.

Thus my question: What would a forgetful edtech look like? What would it look like if you could reset things every day, or every week or every so often, if forgetting were built into technology for learning? Would it even be possible to build such a thing in our current commercial landscape? (Preview on the last question: I suspect not.)

The Forgetful Classroom

Educational technology demands that we think of classrooms as spaces for remembering and forgetting in a way that I suspect we wouldn't have even 10 or 15 years ago.

Imagine for a second that your classroom or lecture hall has cameras not just on you but on every student, all the time, recording, for some future and as yet unknown use, that you are in there and even when you're not. What would you make of that system? Would it seem like a good? Would you want to turn it off? Would you change your behavior and, more importantly, would students change theirs?

(It is true that at various points this ubiquitous use of video has been something that educational theorists and futurists have imagined as a positive. It's a staple of sci-fi to see automated learning that looks like some sort of AI-interactive surveillance system.)

There is, in the physical space, a rhythm and pattern of remembering and forgetting which not only does not translate to digital space but is radically distorted by working in digital space. In situations where you have the same classroom and it isn't shared by others, there's a way in which you can reset that space each day, while keeping reminders of what has been done in the past, visible markers of the previous day and weeks of assignments. In other scenarios, with shared space or in the higher-ed situation where typically you visit a classroom space only a few times each week, there is something of a reset each time.

There are a number of benefits to this pedagogical clean slate and stability, to the fact that your basic surroundings are the same. By contrast, in an online course built into an LMS for example, we might say that the interface is stable in a similar way. But there's a difference in terms of how we are tracked. Physical space doesn't record the traces of your behavior for a third party to dissect. It is not, fundamentally, a space of surveillance.

By contrast, anything online is, by default and by nature, a place of potential surveillance. It is built into the technology. It is the economic model for Big Tech. How can those values help but be baked into educational experiences built on those technologies?

What if, by default, all student interaction on a platform wasn't logged? What if notifications were not the default, if constant automation were not the default? What if you just arrived at a menu and decided what to open up from there. Would that be so bad?

Further, what if everything was opt-in, where you have to trigger a specific action in order to things. There is no passive anything. Nothing happens that a human doesn't trigger intentionally.

We take for granted that educational technologies track our students and, to a certain extent, teachers and anyone who interacts with the system. There are good technical and security reasons to log events and actions in computer systems; however, that paradigm doesn't have to apply at the level that is closest to users. Forgetful behaviors could be built into software. – It would, in my mind, be a great value, to be able to say to students that this platform retains your information for the duration of the class, while we are working, but then it is archived for a brief period and deleted. A clear life cycle for everyone's data goes beyond a simple privacy statement (which is about use and abuse and sharing of data) and foregrounds the pedagogical purpose to which the data is being used. – Default behavior which requires students to commit their data or information to the system rather than passively tracking anything they do. At the user level, the UX is completely about intentional actions, never about passive surveillance. – Drafts by default. In a typical system like an LMS, student actions on the platform are immediate. You submit an assignment — that's the main action. Drafting capabilities lie entirely with teachers. It is a non-trivial technical change but a significant ideological change to make educational platforms places which are about drafts of things. Tools like google docs or others work well in part because of that killer feature of auto-save. As much as that violates my idea about intentionality, I recognize that there is power in the way that that turns everything into a working draft, because it is changeable and editable most times If it were the case that such a tool also forgot about your data at the end and wasn't doing all manner of other unknown things with it along the way, then it would fit the bill (but of course, it's the big G, so they are most certainly taking your data for all sorts of purposes, if not right now, then down the road.)

I suspect their are more, perhaps forms of self-destructing data or other kinds of encryption that allow for more robust privacy. A more extreme example would allow users control of their data through decentralization or federation.

We need to imagine a more forgetful kind of edtech because the alternative is one that continues to walk in lock-step with the world of big tech, where even well-meaning initiatives by the major platforms are still built upon the assumptions that the monetary value of people is in the data that they provide. That is inherent to the platform and the business model of companies like Facebook and Google in toto and many others in significant degrees. (On this last point, I highly recommend reading anything by Jaron Lanier, and especially 10 Arguments for Deleting Your Social Media Accounts Right Now.)

In pedagogical terms, it is an important lesson from the field of memory studies that meaning requires forgetting. The rare individuals who have excessive and near perfect memory (classically, S. in Luria's Mind of a Mnemonist) are crippled in certain ways precisely because of their inability to forget. To understand in the present we must often forget parts of the past. Growth requires both retaining and leaving behind details. It is as true of personal memory as it is of collective memory. Where technologies seem to provide absolute memory, they are in fact failing us as media for making meaning.

Recently in the news: Florida's legislature approves and to some extent encourages students to spy on their teachers in higher ed; conversely, student surveillance company Proctorio continues its ill-advised lawsuit against its critics. These are very different examples, but all part of the continuum of creeping classroom surveillance. It is a bad trend. Learning requires the freedom to make mistakes and room to experiment, support for growth and the messiness that is education. The expectation and acceptance of surveillance, face to face or online through technology, runs counter to those values.

Memory isn't the same thing as privacy

Discussions about edtech surveillance and data logging (and for data retention in most tech platforms generally) are often framed as issues of privacy. Data privacy policies must indicate and spell out how data will be used and for what purposes. While it is true that data retention is a part of that kind of data policy language, these policies are much more about making clear commercial and non-commercial uses of data, a legal butt-covering to make sure that possible use cases have been enumerated should anyone find out at a later date that their particular data has been used in some way that has not been cataloged in the privacy policy.

The more robust policies of the GDPR are something I wish would gain traction in the U.S., but even those guidelines and principles are of a different sort than what I am describing here. (The policies around retaining minimal data necessary are all hugely important steps in the right direction.) But educational technologies requires not simply privacy practices (e.g. FERPA) but best practices around inentional forgetting. We do a disservice to students to have only flimsy and piecemeal protections against their younger selves, their learning selves, leaving behind lasting traces that they don't have control over. That is not a problem only for their future, when they might be embarrassed by something they said on video when younger or when novices. That is a problem of the here and now, because knowing that you leave lasting traces, that you are being recorded changes behaviors.

It's easy to think of things like anonymity or forgetting as negatives, to imagine that we are losing something, taking away something that should otherwise be preserved. And in our present moment where wearable technologies and the quantified self are sold as an obvious good and inevitable direction of health tech, it's easy to think that tracking is inevitable. (Side note: measuring the value of self-quantification is complicated.) I don't think it is inevitable that tracking is the norm forever, particularly, especially if we can articulate alternatives that provide other kinds of value beyond what the marketers and big tech companies want to extract as user data that feeds back into their platforms.

Paradoxically, forgetting is often the most important mechanism for making things meaningful, not just because you know that something must exist in a particular moment, but because you know that the arena for it that matters most is in your own memory, not offloaded onto the computer system. When I think of a forgetful kind of edtech, I think mostly about how forgetting might help create more meaningful experiences with technology. That might involve some combination of temporary anonymization, rolling windows of auto-deleting records, scrubbing information as early as possible. It's paradoxical because computing is, by nature, about storing bits and bytes and then doing something with those stored bits and bytes — it is all built on the mechanics of recording data. So thinking about forgetful edtech, or forgetful computing is an interesting problem. But the field of “intentional forgetting” is an important area of study (see, e.g. http://www.spp1921.de/index.html for the Intentional Forgetting in Organizations projects) and one which may have benefits for education. Many of the insights gleaned from outside education may have larger impacts if implemented for students, providing a direct counterpoint to the seemingly inevitable trend towards educational technologies that record everything, while at the same time opening the way to new, more meaningful educational experiences.

Postscript: This is a first attempt to think through this topic, but there is a lot more to say, particularly in light of the growing literature on intentional forgetting (which I have not referred to much in the above). More coming...

#minimalism #lessismore #edtech #edtechminimalism #minimalistedtech

This recent piece about the psychological reasons why it might be hard to think of solutions in terms of subtracting something rather than adding features hit home with me. (Of course it did: less is more and all that...) I am particularly fond of their “No-bell”:

Every time we subtract an activity that is not helping us create or share knowledge, we ring the bell and celebrate the No-Bell Prize. Quit a dead-end research project? Ding! Cancel a time-suck meeting? Ding! One way to get more meaningful work done is to add work hours. A better way is to subtract tedious time-fillers.

I could have annoyed my office neighbors with that quite a bit; probably every time I deleted an email from on high. That seems a bit extreme as an action, but I do appreciate the idea of turning subtraction into a reward and celebrating the idea of less.

Immediately I was reminded of the old advice about writing and public speaking. You make your last step removing something and almost always the whole thing is better for it. In the case of lectures and academic talks, this is a great technique both because it means that you will feel more relaxed about bringing the talk in on time but also because in any question or answer session you usually have at least one answer (on the stuff you cut out) in the bag. In fact, because you cut it out and it likely had a natural place in what you were saying, it is very likely that someone will ask you about it, so you've even done the work, simply by removing it from your prepared remarks, of making it likely that someone will ask you about it.

Applied to teaching and technology, I found myself thinking less about avoiding the tedious time sucks (though those do, in fact, suck) and more about feature creep in a lot of edtech. Or, put differently, about how often it is that technology offers “solutions” to problems that can be solved or alleviated by subtracting something rather than adding a new tool or feature to a tool. I get this vibe all the time from “AI” marketing copy around edtech and too often from tools that seem to be suffering from feature creep or mission creep over the years (looking at you Tophat). It is endemic in LMS-es and their integrations, where I often have to ask myself whether what they're selling could be done with a simple document or video rather than a platform or piece of software. It is advice I would give to students when using tools too — not least of all when they want to do PowerPoint presentations and refuse to heed the advice about less text, more message.

More often though, it is habits around use that drive the “more”; there isn't always a feature that is the culprit.

The easiest example of this recently is with Zoom and its overuse this past year. How many meetings, particularly one on one, might be better with a simple phone call, removing the video and just sticking to the voice? Or how often is it helpful to remove one thing that you think you have to do online and put it in a simpler form. I think often about the simplicity of early Khan Academy videos. They could be effective with a piece of paper and a good explanation. You don't always need a digital whiteboard or an online tool when you can use paper, pencil, and conversation.

More specifically though, the process of subtraction seems something that could be a regular part of the toolkit for working with any educational technology. Sort of a line item in the process, where you stop and ask, ok, what could I remove to make this work better? It's the kind of exercise that contains within it the seeds of critical process. It forces a question: is this thing that I'm doing valuable?

More often than not, I find that the payoff for this sort of subtraction exercise isn't just in what is removed. It often helps reveal a better way forward through a clear-headed assessment of what you are doing out of inertia and obligation vs. what is of practical value. So, for example, a process like this had me rethink a lot of the way I gave comments on student writing. I found myself wasting a lot of time writing or recording via audio comments that students would never see. (I know they didn't see them because the edtech tool in this case tracked whether or not students have looked at the comments. Over the years it has been clear that only about 50% of students read comments, even when doing so is required for editing and revising.) So I started by removing that process, removing the tracking, forgetting about that whole way of doing things. Once all that was subtracted, then I could think through leaner ways of giving feedback that would be immediate and actionable and, crucially, unavoidable, for students while not feeling like I was wasting all my time and labor. The direction I went in is less relevant to subtraction, but it was essentially to replace technology with individual meetings, at a massive scale; though that might sound more involved, it was in fact less stuff going on, less hours, and less work for greater benefit and better outcomes. It was also, incredibly old-fashioned and non-technological.

I appreciated this article because it gave some context for why it is so hard to think in terms of subtracting as a path to solving problems. We have to force ourselves to think that way, to think through doing more through doing less. (And, to be fair, it's not a solution in all situations. Sometimes more is more.)

#minimalistedtech #edtech #edtechminimalism

I started writing this blog, about 6+ months ago, when I was headed in a professional direction that was a bit different than it is now. Let's say that my worldview was a bit more open source-ish and not particularly commercial or profit-minded. Since then I've moved into greater contact with the business of edtech, so to speak. One useful feature of writing in the current format and under the current heading of “minimalist” edtech is that it's given me a chance to think through the tension between my teacher brain, which tends to want to serve students and teachers, and the reality of various edtech business models and trends. I don't mean to imply that edtech companies are bad actors in relation to some sort of pedagogical purity that only teachers possess; it's not that at all. But there is a tension there, a difference in what stakeholders may value or may find compelling.

More specifically, if asked, “what's the value prop for x edtech product or y technology”, how far apart would teacher brain and business brain be?

A good analogue here is with RSS feeds. (And here I am picking up on this post by Kevin Drum.) There was a time, in the not distant past, where a whole lot of people consumed blogs via RSS. RSS is a venerable and useful system. (It is also a fairly minimalist kind of thing as well, giving you content without all the garbage bloating a webpage.) As a technology, it does a great job of getting content to you quickly. RSS readers were a great way for individuals to consume what they wanted to consume. (And, they still are, for content that is still available easily that way.) BUT, here's the problem, in Drum's words:

There's no way to monetize it, and it cannibalizes users away from platforms that want to be your sole hub for news aggregation. Google wanted its users on Google+, or at the very least finding the news via search, which generated ad revenue. Facebook wanted you to read a news feed full of ads within their walled garden. And Twitter wanted to be the place where news broke first—but only if you were actually on Twitter.

In other words, RSS was a threat to practically every platform that aggregates news since it allowed users to decide for themselves what news they wanted to see—and to see it without passing through a gatekeeper. The best way to eliminate this threat was to eliminate or reduce support for RSS, as Google, Facebook, and Twitter have all done.

I think about this in relation to edtech and it seems like we're in something of the same essential conundrum. It's not so obvious or simple perhaps, but it is this same tension between technologies that simply get a job done vs. technologies that allow some form of monetization or, equally important, locate control in ways that administrators or others can manage. Reducing support for RSS is a choice that big tech companies made about how to monetize content for maximum profit. So too a lot of the systems that we take for granted or as inevitable in edtech are designed for a certain kind of lock-in and perpetuation. (obvious, yes.) Again, I would emphasize that this isn't to imply a value judgement. Rather, how much are we as teachers aware of these kinds of choices that go into what gets funded in edtech development or in what gets made a certain way?

One of my constant targets of criticism are edtech marketers. I find it gently insulting to get marketing copy from edtech companies that claims to improve “engagement” or the like. Really? Are you saying I'm not an engaging teacher? Are you talking to me like I haven't been doing this for decades? When your twenty-something sales rep gets on the phone and starts walking me through this souped up social media garbage product, do you really think I'm going to listen to that (perfectly well-intentioned and blameless) young person who as far as I can tell knows nothing about the practice of education? ... Too much caffeine this morning. Sorry, got a little animated there. I'll collect myself before continuing.

[pause]

I suspect that most teachers don't take a look at market trends in edtech all that often. But we should, because it's not the case that administrators are making decisions on whims or passing fancy. There is of course a lot of money involved and that shapes much that we see most often at a more granular level in the classroom or in specific technologies. But the big picture is illuminating. HolonIQ is a go-to research shop for this and I highly recommend checking out their newsletters, at a minimum: https://www.holoniq.com/newsletter/

One area where I find this kind of research particularly revealing is in the way it characterizes trends into the future. So, for example, LMS-es are, generally speaking, imagined to continue gobbling up other companies as neeed and growing, much like the tech sector in general. That has some important repurcussions. Being an LMS critic as I am, I wonder why in the world we're stuck with this garbage that feels always like a glorified database interface hooked up to a file system built for the sole purpose of centralizing my content for someone else so that I don't have control over it. Oh, and LMS-es are also slow and bloated and badly designed and with crappy inflexible gradebooks and the like. And they smell bad. (apparently I've had far too much caffeine this morning...)

But put the criticism aside for a minute. That's a hard space in which to imagine competitiors to the LMS. Or perhaps, have the competitors been there all along? It's just not much of a product: the LMS alternative has been, for some time, abstention. It's all the teachers who opt-out and use the LMS as little as possible (at least until this past year, when many were forced to make more use of it.) Teachers don't have options, in part because the market forces at work are still those lingering from the 1990s, when centralizing systems and aggregating gatekeepers for a university-branded thing seemed like the way to go, at least to the people in charge of building such systems and making such choices. I am simplifying grossly, but my point here is that there are of course always tensions between various stakeholders. For education this is particularly complex, as the stakeholders range from all the people involved in building something to the admins, teachers, students, instructional designers, IT staff, 3rd party content developers, from big companies to small initatives in many cases. My teacher brain likes to imagine that from critique of edtech at a very individual level can emerge something better. But zooming out, at the level at which HolonIQ conducts its research and shows the trends that drive the business of edtech, I get all sorts of David v. Goliath vibes.

In the language of business, what is the value prop for a given edtech product? For a teacher, the value is often pedagogical, but also about things like ease of use, reallocating time by making certain tasks easier perhaps. It could be that edtech is fun or interesting or helps you think differently. Or it can help deliver content or make content accessible. Those are all teacher facing values — and I'm sure that list could go on for a long time. But the companies making this stuff do seem to have a different incentive system they labor under. (I am not blaming them for this. It is a structural issue, about rewards and constraints and what is in one's face in a particular role.) For a company making an edtech product, the value prop is about growth, money, profit. That only make sense, as there's no product if you can't sell it or make some money off it. There's no funding unless you can convince a VC that you'll generate a return on their investment. Ideally the road to profit and growth means building something that all the education stakeholders (teachers, students, admins, etc.) value. But in a landscape where — and here I'm drawing on HolonIQ — education is an under-technologized and under-funded industry relative to other industries, it would seem that teachers' or students' interests need only serve as pretext to the flood of money that is desperate to make further inroads into the education industry.

Which brings me finally back to minimalism. I value minimalism in teaching quite a lot, for reasons laid out across many of these posts. Ultimately though that needs to translate to a value prop that makes sense to companies or entrepreneurs in monetary terms. It has to be more profitable to make something that is minimalist in its design. I think this can be done but I don't think this is where most (if any) edtech ventures are right now. The trend is for more — more integrations to LMS-es, more products, more features, more online. Maybe I'm wrong. In fact, I would like to be very wrong about this. For now I'm stuck in a mode which too conveniently makes the idea of a more minimalist edtech gravitate towards an open source or at-the-margins kind of development; it's hard to see past a sort of dichotomy which feels non-profit vs. for-profit.

I'm not particularly happy about that. Can the value prop (in economic terms) for a more minimalist edtech work? Can a minimalist edtech be about growth and profit at the same time as it builds on teacher and student centered values?

Such are the thoughts floating around this morning. More coffee is needed. :coffee: