Teaching and Learning with Primary Sources in the age of Generative AI

The following is a (more or less verbatim) transcript of a keynote address I gave earlier today to the Dartmouth College Teaching with Primary Sources Symposium. My thanks to Morgan Swan and Laura Barrett of the Dartmouth College Library for hosting me and giving me the opportunity to gather some initial thoughts about this thoroughly disorienting new development in the history of information.

Thank you, Morgan, and thank you all for being here this morning. I was going to talk about our Sourcery project today, which is an application to streamline remote access to archival materials for both researchers and archivists, but at the last minute I’ve decided to bow to the inevitable and talk about ChatGPT instead.

Dartmouth College Green on a beautiful early-spring day

I can almost feel the inner groan emanating from those of you who are exhausted and perhaps dismayed by the 24/7 coverage of “Generative AI.” I’m talking about things like ChatGPT, DALL-E, MidJourney, Jasper, Stable Diffusion, and Google’s just released, Bard. Indeed, the coverage has been wall to wall, and the hype has at times been breathless, and it’s reasonable to be skeptical of “the next big thing” from Silicon Valley. After all we’ve just seen the Silicon Valley hype machine very nearly bring down the banking system. In just past year, we’ve seen the spectacular fall of the last “next big thing,” so-called “crypto,” which promised to revolutionize everything from finance to art. And we’ve just lived through a decade in which the social media giants have created a veritable dystopia of teen suicide, election interference, and resurgent white nationalism.

So, when the tech industry tells you that this whatever is “going to change everything,” it makes sense to be wary. I’m wary myself. But with a healthy dose of skepticism, and more than a little cynicism, I’m here to tell you today as a 25-year veteran of the digital humanities and a historian of science and technology, as someone who teaches the history of digital culture, that Generative AI is the biggest change in the information landscape since at least 1994 and the launch of the Netscape web browser which brought the Internet to billions. It’s surely bigger than the rise of search with Google in the early 2000s or the rise of social media in the early 2010s. And it’s moving at a speed that makes it extremely difficult to say where it’s headed. But let’s just say that if we all had an inkling that the robots were coming 100 or 50 or 25 years into the future, it’s now clear to me that they’ll be here in a matter of just a few years—if not a few months.

It’s hard to overstate just how fast this is happening. Let me give you an example. Here is the text of a talk entitled (coincidentally!) “Teaching with primary sources in the next digital age.” This text was generated by ChatGPT—or GPT-3.5—the version which was made available to the public last fall, and which really kicked off this wall-to-wall media frenzy over Generative AI.

You can see that it does a plausible job of producing a three-to-five paragraph essay on the topic of my talk today that would not be an embarrassment if it was written by your ninth-grade son or daughter. It covers a range of relevant topics, provides a cogent, if simplistic, explanation of those topics, and it does so in correct and readable English prose.

Now here’s the same talk generated by GPT-4 which came out just last week. It’s significantly more convincing than the text produced by version 3.5. It demonstrates a much greater fluency with the language of libraries and archives. It correctly identifies many if not most of the most salient issues facing teaching in archives today and provides much greater detail and nuance. It’s even a little trendy, using some of the edu-speak and library lingo that you’d hear at a conference of educators or librarians in 2023.

Now here’s the outline for a slide deck of this talk that I asked GPT-4 to compose, complete with suggestions for relevant images. Below that is the text of speaker notes for just one of the bullets in this talk that I asked the bot to write.

Now—if I had generated speaker notes for each of the bullets in this outline and asked GPT’s stablemate and image generator, DALL-E, to create accompanying images—all of which would have taken the systems about 5 minutes—and then delivered this talk more or less verbatim to this highly educated, highly accomplished, Ivy League audience, I’m guessing the reaction would have been: “OK, seems a little basic for this kind of thing” and “wow, that was talk was a big piece of milktoast.” It would have been completely uninspiring, and there would have been plenty to criticize—but neither would I have seemed completely out of place at this podium. After all, how many crappy, uninspiring, worn out PowerPoints have you sat through in your career? But the important point to stress here is that in less than six months, the technology has gone from writing at a ninth-grade level to writing at a college level and maybe even more.

Much of the discourse among journalists and in the academic blogs and social media has revolved around picking out the mistakes these technologies make. For example, my good friend at Middlebury, Jason Mittell, along with many others, has pointed out that ChatGPT tends to invent citations: references to articles attributed to authors with titles that look plausible in real journals that do not, in fact, exist. Australian literary scholar, Andrew Dean, has pointed out how ChatGPT spectacularly misunderstands some metaphors in poetry. And it’s true. Generative AIs make lots of extremely weird mistakes, and they wrap those mistakes in extremely convincing-sounding prose, which often makes them hard to catch. And as Matt Kirschenbaum has pointed out: they’re going to flood the Internet with this stuff. Undoubtedly there are issues here.

But don’t mistake the fact that ChatGPT is lousy at some things for the reality that it’ll be good enough for lots, and lots, and lots of things. And based on the current trajectory of improvement, do we really think these problems won’t be fixed?

Let me give another couple of examples. Look at this chart, which shows GPT-3.5’s performance on a range of real-world tests. Now look at this chart, which shows GPT-4’s improvement. If these robots have gone from writing decent five-paragraph high school essays to passing the Bar Exam (in the 90th percentile!!) in six months, do we really think they won’t figure out citations in the next year, or two, or five? Keep in mind that GPT-4 is a general purpose model that’s engineered to do everything pretty well. It wasn’t even engineered to take the Bar Exam. Google CEO, Sundar Pichai tells us that AI computing power is doubling every six months. If today it can kill the Bar Exam, do we really think it won’t be able to produce a plausible article for a mid-tier peer reviewed scholarly journal in a minor sub-discipline of the humanities in a year or two? Are we confident that there will be any way for us to tell that machine-written article from one written by a human?

(And just so our friends in the STEM fields don’t start feeling too smug, GPT can write code too. Not perfectly of course, but it wasn’t trained for that either. It just figured it out. Do we really think it’s that long until an AI can build yet another delivery app for yet another fast-food chain? Indeed, Ubisoft and Roblox are starting to use AI to design games. Our students’ parents are going to have to start getting their heads around the fact that “learning to code” isn’t going to be the bulletproof job-market armor they thought it was. I’m particularly worried for my digital media students who have invested blood, sweat, and tears learning the procedural ins and outs of the Adobe suite.)

There are some big philosophical issues at play here. One is around meaning. The way GPT-4 and other generative AIs produce text is by predicting the next word in a sentence statistically based on a model of drawn from an unimaginably large (and frankly unknowable) corpus of text the size of the whole Internet—a “large language model” or LLM—not by understanding the topic they’re given. In this way the prose they produce is totally devoid of meaning. Drawing on philosopher, Harry Frankfurter’s definition of “bullshit” as “speech intended to persuade without regard for truth”, Princeton computer scientists Arvind Narayanan and Sayash Kapoor suggest that these LLMs are merely “bullshit generators.” But if something meaningless is indistinguishable from something meaningful—if it holds meaning for us, but not the machine—is it really meaningless? If we can’t tell the simulation from the real, does it matter? These are crucial philosophical, even moral, questions. But I’m not a philosopher or an ethicist, and I’m not going to pretend to be able to think through them with any authority.

What I know is: here we are.

As a purely practical matter, then, we need to start preparing our students to live in a world of sometimes bogus, often very useful, generative AI. The first-year students arriving in the fall may very well graduate into a world that has no way of knowing machine-generated from human-generated work. Whatever we think about them, however we feel about them (and I feel a mixture of disorientation, disgust, and exhaustion), these technologies are going to drastically change what those Silicon Valley types might call “the value proposition” of human creativity and knowledge creation. Framing it in these terms is ugly, but that’s the reality our students will face. And there’s an urgency to it that we must face.

So, let’s get down to brass tacks. What does all this mean for what we’re here to talk about today, that is, “Teaching with Primary Sources”?

One way to start to answer this question is to take the value proposition framing seriously and ask ourselves, “What kinds of human textual production will continue to be of value in this new future and what kinds will not?” One thing I think we can say pretty much for sure is that writing based on research that can be done entirely online is in trouble. More precisely, writing about things about which there’s already a lot online is in trouble. Let’s call this “synthetic writing” for short. Writing that synthesizes existing writing is almost certainly going to be done better by robots. This means that what has passed as “journalism” for the past 20 years since Google revolutionized the ad business—those BuzzFeed style “listicles” (“The 20 best places in Dallas for tacos!”) that flood the internet and are designed for nothing more than to sell search ads against—that’s dead.

But it’s not only that. Other kinds of synthetic writing—for example, student essays that compare and contrast two texts or (more relevant to us today) place a primary source in the context drawn from secondary source reading—those are dead too. Omeka exhibits that synthesize narrative threads among a group of primary sources chosen from our digitized collections? Not yet, but soon.

And it’s not just that these kinds of assignments will be obsolete because AI will make it too easy for students to cheat. It’s what’s the point of teaching students to do something that they’ll never be asked to do again outside of school? This has always been a problem with college essays that were only ever destined for a file cabinet in the professor’s desk. But at least we could tell ourselves that we were doing something that simulated the kind of knowledge work they would so as lawyers and teachers and businesspeople out in the real world. But now?

(Incidentally, I also fear that synthetic scholarly writing is in trouble, for instance, a Marxist analysis of Don Quixote. When there’s a lot of text about Marx and a lot of text about Don Quixote out there on the Internet, chances are the AI will do a better—certainly a much faster—job of weaving the two together. Revisionist and theoretical takes on known narratives are in trouble.)

We have to start looking for the things we have to offer that are (at least for now) AI-proof, so to speak. We have to start thinking about the skills that students will need to navigate an AI world. Those are the things that will be of real value to them. So, I’m going to use the rest of my time to start exploring with you (because I certainly don’t have any hard and fast answers) some of the shifts we might want to start to make to accommodate ourselves and our students to this new world.

I’m going to quickly run through eight things.

  1. The most obvious thing we can do it to refocus on the physical. GPT and its competitors are trained on digitized sources. At least for now they can only be as smart as what’s already on the Internet. They can’t know anything about anything that’s not online. That’s going to mean that physical archives (and material culture in general) will take on a much greater prominence as the things that AI doesn’t know about and can’t say anything about. In an age of AI, there will be much greater demand for the undigitized stuff. Being able to work with undigitized materials is going to be a big “value add” for humans in the age of these LLMs. And our students do not know how to access it. Most of us were trained on card catalogs, in sorting through library stacks, of traveling to different archives and sifting through boxes of sources. Having been born into the age of Google, our students are much less good at this, and they’re going to need to get better. Moreover, they’re going to need better ways of getting at these physical sources that don’t always involve tons of travel, with all its risks to climate and contagion. Archivists, meanwhile, will need new tools to deal with the increased demand. We launched our Sourcery app, which is designed to provide better connections between researchers and archivists and to provide improved access to remote undigitized sources before these LLMs hit the papers. But tools like Sourcery are going to be increasingly important in an age when the kind of access that real humans need isn’t the digital kind, but the physical kind.
  2. Moreover, we should start rethinking our digitization programs. The copyright issues around LLMs are (let’s say) complex, but currently Open AI, Google, Microsoft, Meta, and the others are rolling right ahead, sucking up anything they can get their hands on, and processing those materials through their AIs. This includes all of the open access materials we have so earnestly spent 30 years producing for the greater good. Maybe we want to start asking ourselves whether we really want to continue providing completely open, barrier-free access to these materials. We’ve assumed that more open meant more humane. But when it’s a robot taking advantage of that openness? We need a gut check.
  3. AIs will in general just be better at the Internet than us. They’ll find, sort, sift, and synthesize things faster. They’ll conduct multi-step online operations—like booking a trip or editing a podcast—faster than us. This hits a generation that’s extremely invested in being good at the Internet, and, unfortunately, increasingly bad at working in the real world. Our current undergraduates have been deeply marked by the experience of the pandemic. I’m sure many of you have seen a drastic increase in class absences and a drastic decrease in class participation since the pandemic. We know from data that more and more of our students struggle with depression and anxiety. Students have difficulty forming friendships in the real world. There are a growing number of students who choose to take all online classes even though they’re living in the dorms. This attachment to the virtual may not serve them well in a world where the virtual is dominated by robots who are better than us at doing things in the digital world. We need to get our students re-accustomed to human-to-human connections.
  4. At the same time, we need to encourage students to know themselves better. We need to help them cultivate authentic, personal interests. This is a generation that has been trained to write to the test. But AIs will be able to write to the test much better than we can. AIs will be able to ascertain much better than we can what they (whomever they is: the school board, the college board, the boss, the search algorithm) want. But what the AI can’t really do is tell us what we want, what we like, what we’re interested in and how to get it. We need to cultivate our students’ sense of themselves and help them work with the new AIs to get it. Otherwise, the AI will just tell them what they’re interested in, in ways that are much more sophisticated and convincing than the Instagram and TikTok algorithms that are currently shoving content at them. For those of us teaching with primary sources this means exposing them to the different, the out of the ordinary, the inscrutable. It means helping them become good “pickers” – helping them select the primary sources that truly hold meaning for them. As educators of all sorts, it means building up their personalities, celebrating their uniqueness, and supporting their difference.
  5. I think we also need to return to teaching names and dates history. That’s an unfashionable statement. The conventional wisdom of at least the last 30 years is that that names, dates, and places aren’t that important to memorize because the real stuff of history are the themes and theories—and anyway, the Google can always give us the names and dates. Moreover, names and dates history is boring and with the humanities in perpetual crisis and on the chopping block in the neoliberal university, we want to do everything we can to make our disciplines more attractive. But memorized names, and dates, and places are the things that allow historians to make the creative leaps that constitute new ideas. The biggest gap I see between students of all stripes, including graduate students, and the privileged few like me who make it into university teaching positions (besides white male privilege) is a fluency with names, dates, and places. The historians that impress most are the ones who can take two apparently disconnected happenings and draw a meaningful connection between them. Most often the thing that suggests that connection to them is a connected name, date, place, source, event, or institution that they have readily at hand. Those connections are where new historical ideas are born. Not where they end, for sure, but where they are born. AI is going to be very good at synthesizing existing ideas. But it may be less good at making new ones. We need students who can birth new ideas.
  6. Related to this is the way we teach students to read. In the last 20 years, largely in response to the demands of testing, but also in response to the prioritization of “critical thinking” as a career skill, we’ve taught students not to read for immersion, for distraction, for imagination, but for analysis. Kids read tactically. They don’t just read. In many cases, this means they don’t read at all unless they have to. Yet, this is exactly how the AI reads. Tactically. Purely for analysis. Purely to answer the question. And they’ll ultimately be able to do this way better than us. But humans can read in another way. To be inspired. To be moved. We need to get back to this. The imaginative mode of reading will set us apart.
  7. More practically, we need to start working with these models to get better at asking them the right questions. If you’ve spent any time with them, you’ll know that what you put in is very important in determining what you get out. Here’s an example. In this chat, I asked GPT-3.5, “How can I teach with primary sources.” OK. Not bad. But then in another chat I asked, “Give me a step-by-step plan for using primary sources in the classroom to teach students to make use of historical evidence in their writing” and I followed it up with a few more questions: “Can you elaborate?” and “Are there other steps I should take?” and then “Can you suggest an assignment that will assess these skills?” You’ll see that it gets better and better as it goes along. I’m no expert at this. But I’m planning on becoming one because I want to be able to show our students how to use it well. Because, don’t fool yourselves, they’re going to use it.
  8. Finally, then, perhaps the most immediate thing we can do is to inculcate good practice around students’ use of AI generated content. We need to establish citation practices, and indeed the MLA has just suggested some guidance for citing generative AI content. Stanford, and other universities, are beginning to issue policies and teaching guidance. So far, these policies are pretty weak. Stanford’s policy basically boils down to, “Students: Don’t cheat. Faculty: Figure it out for yourselves.” It’s a busy time of year and all, but we need urgently to work with administration to make these things better.

I’m nearly out of time, and I really, really want to leave time for conversation, so I’ll leave it there. These are just a couple of thoughts that I’ve pulled together in my few weeks of following these developments. As I’ve said, I’m no expert in computer science, or philosophy, or business, but I think I can fairly call myself an expert in digital humanities and the history of science and technology, and I’m convinced this new world is right around the corner. I don’t have to like it. You don’t have to like it. If we want to stop it, or slow it down, we should advocate for that. But we need to understand it. We need to prepare our students for it.

At the same time, if you look at my list of things we should be doing to prepare for the AI revolution, they are, in fact, things we should have been (and in many cases have been) doing all along. Paying more attention to the undigitized materials in our collections? I’m guessing that’s something you already want to do. Helping students have meaningful, in-person, human connections? Ditto. Paying more attention to what we put online to be indexed, manipulated, sold against search advertising? Ditto. Encouraging students to have greater fluency with names, dates, and places? Helping them format more sophisticated search queries? Promoting better citation practice for born-digital materials and greater academic integrity? Ditto. Ditto. Ditto.

AI is going to change the way we do things. Make no mistake. But like all other technological revolutions, the changes it demands will just require us to be better teachers, better archivists, better humans.

Thank you.

Why STEM can’t answer today’s hard questions

I recently relistened an interview Ezra Klein did with Danielle Allen (Harvard Edmond J. Safra Center for Ethics) in 2019, in which they discuss how science, technology, and business differ fundamentally from politics because the former disciplines assume a set of values that are already ordered by priority (efficiency, profit, etc.) but politics is essentially all about the setting and the reordering of those values. That’s why engineering and STEM have a hard time “fixing” politics and a hard time “solving” more human questions (and perhaps even why STEM majors vote in much smaller numbers than humanities majors).

This is something the pandemic has thrown into sharp relief in the years since Klein and Allen’s conversation. On one level, STEM can “fix” the pandemic by giving us miracle vaccines. But that’s only if we assume a set of values that are held in common by the populace (the health of the community, safety, trust in expertise, etc.) If the values themselves are at issue, as they are surrounding COVID-19, then STEM doesn’t have much to offer, at least for those communities (red state voters, anti-vaxxers) whose values diverge from those assumed by STEM.

This suggests, as Allen argues, that we need to rebalance the school curriculum in favor of humanities education, including paying a greater attention to language (the primary toolkit of politics) and civics. It also suggests the need for more humanities within the STEM curriculum—not just the three-credit add-on ethics courses that characterize engineering programs and medical school, but a real integration of humanities topics, methods, and thinking as part of what it means to “know” about STEM.

This is, of course, something that’s especially appealing to me as a historian of science, but it’s something that should be just as appealing to engineers, who like to frame their work as “problem solving.” If STEM really wants to solve the big problems facing us today, it is going to have to start further back, to solve for more than just technical questions, but also for the values questions that increasingly precede them.

Rethinking ROI

This semester I’ll be co-chairing our President’s “Life-Transformative Education” task force, a signature initiative to rethink undergraduate education at UConn. Part of a coalition of similar efforts at other universities across the country, the basic idea of LTE is that an undergraduate education should change (or at least actively reconfirm) the worldview and life trajectory of each and every student at UConn. Our work involves a top-to-bottom rethinking of everything from student advising to internship opportunities to capstone experiences for graduating seniors.

Closely tied to our practical efforts at reforming pedagogy and student care at the University is a broader rethinking of the real value of an undergraduate education and the way that value proposition is communicated to students, parents, alums, and in the case of a state institution like UConn, to the taxpayers and legislature of the State of Connecticut. It seems to me that part of that work entails rethinking how value itself is calculated.

Somewhere along the way, universities, like so many other institutions in our culture, began to measure their impact in strictly economic terms. In making our case to stakeholders (especially purse-string holders) we talk about the economic impact of sponsored research, work force training, and other direct benefits to business. Likewise, at the level of the individual student, there is a strong tendency to assess the value of a college degree in terms of a “wage premium,” or the amount of money a college graduate can expect to make versus a non-graduate (the cover story in this week’s The Economist is case in point.) By this way of thinking, the “return on investment” or ROI of a college education is a simple matter of subtracting tuition costs from the wage premium a student can expect upon graduation.

This is a crude measure of the value of a college degree. Life-Transformative Education suggests that the real value of undergraduate education lies in its capacity to change lives, financially for sure, but in other ways too. Thus Life-Transformative Education demands an accounting beyond the wage premium to determine the true ROI of college.

Trends in macro-economics may provide a guide. Even fairly conservative economists are realizing that simple measures of overall economic growth don’t provide a very useful picture of the success of the overall economy, especially in the face of rising inequality. These economists are moving “Beyond GDP” to count things like people’s health, a clean environment, and unpaid labor like elder care and child rearing, in addition to growth, as a more accurate measure of economic health. New Zealand, for example, now uses a “happiness index,” created from a basket of metrics, to make some policy decisions instead of GDP.

So what should a “Beyond the Wage Premium” calculation of college ROI include? What else should we count? I have a few suggestions, including:

  • the likelihood of finding a job that includes health benefits
  • the likelihood of finding a job that includes parental-leave and childcare benefits
  • future ability to change jobs
  • geographic mobility
  • mental health outcomes
  • domestic abuse rates

What else? I’m sure there’s plenty (on both sides of the ledger) that I’ve left out. Please let me know in comments, and if you’re an economist and would like to work on this, let’s talk.

Digital History and the Public History Curriculum

A knowledge of digital history theories and methods is quickly becoming essential for public historians. More and more, digital history is a required part of the public history graduate curriculum. A panel at the (now-not-so-recent) meeting of the National Council on Public History featured public history students engaged in this new digitally-infused curriculum.

Organized and chaired by our very own Jeremy Boggs, the panel included one student from American University’s M.A. Concentration in Public History, Leah Suhrstedt, and two students from New York University’s M.A. in Archives and Public History program, Adina Langer and Lauren Gutterman. I was lucky to be asked at the last minute to provide a brief comment on what turned out to be three inspiring presentations. Two things stood out in each talk, both of them relatively simple but important insights for faculty and administrators organizing public history courses or programs.

First, it was clear from the students’ experiences that teaching and learning digital history involve good measures of risk and trust on the part of both faculty and students. In teaching something as new, changeable, and diverse as digital history, faculty have to give students freedom to try new things and make mistakes, to challenge traditional modes of work, and experiment with new kinds of knowledge creation and dissemination. At the same time, students have to accept this risk and trust themselves and their potential to engage new technologies and master new skills. To paraphrase Langer’s presentation, “Its about leaving the gate open, about teaching students how to teach themselves.” In fact, this should be a comfortable role for seasoned public history instructors. As Suhrstedt suggested in her presentation, as professionals working in mostly small and underfunded organizations, we have all been asked at some point in our careers to do something for which we were completely unprepared by our graduate training. Public historians are already scholars, public intellectuals, fundraisers, teachers, community activists, therapists, and on and on. Becoming a digital public historian is really just adding another hat to the rack.

Second, teaching digital history is not just about teaching students how to build websites. New modes of publishing and the technologies and programming languages required to mount history on the web are important parts of the digital history curriculum. But teaching students to be digital public historians means teaching them when and how best to use digital technologies in all aspects of public historical work. It’s about teaching new pathways for the entire public historical endeavor, including exhibiting history online, but also how to use digital media for community outreach, fundraising, project management, and even advocacy, something Suhrstedt demonstrated in presenting her work with the National Trust’s social media campaign. Tellingly, Gutterman’s presentation of her work with OutHistory.org was almost exclusively about outreach. The lesson of these projects is that the digital should be taught not just as new mode of dissemination, but a new mode of engagement.

Both of these thoughts have rattled around in my brain for some time, but it was only with the help of Langer, Gutterman, and Suhrstedt that I can put them to paper (or pixels, as is really the case). Fortunately for public history faculty faced with incorporating digital history into their public history curricula, both insights point to digital history not being all that different from traditional public history. Risk, trust, and engagement are all very familiar concepts to veteran public historians, something that should give us confidence in weaving the new digital history more tightly into our programs.

Picking on someone our own size

Friends of the blog will know that I have long been skeptical of historical video game projects. One of several critiques is that our budgets are just too small to compete in the cultural marketplace with the likes of EA and Activision. I understand that we’re not in direct and open competition with those companies for our students’ attention and that, if necessary, we have other means of compelling attention, especially in the context of the classroom. I’m also not saying anything about the pedagogical value of those games once students are made to play them, nor am I talking about casual games for Facebook and other platforms, which I’ll admit present a more level playing field for digital humanities. Caveats aside, I still see no getting around the fact that when students and others look at the video games and virtual environments we develop, they can’t help but compare the production values and game play to things they’re seeing on Xbox.

Consider these figures:

How can we possibly keep up?

Now consider that Foursquare, the wildly popular place-based social network has to date received a total of $1.35 million in venture funding. Again, Foursquare built a thriving social network, one of Silicon Valley’s hottest companies, for little more than what’s available to individual applicants through IMLS’s National Leadership Grants program. Try building a top video game for $1.35M.

That’s a number we can match, and the reason why, for my money, I’ll be sticking to the web and mobile space and giving history video games a pass.

[Thanks to Leslie Madsen-Brooks for the email that inspired this rant.]

iPads and irResponsibility

Seton Hill University in Pennsylvania has announced it will give every full-time student a new Apple iPad upon arrival in the fall. This seems remarkably irresponsible to me. In a time of scant resources, does it really make sense to commit hundreds of thousands of dollars to a device very few people have ever even touched and for which not a single device-specific educational application has been built and tested with real students? With a total enrollment of approximately 2000, and a per-iPad cost of approximately $500, Seton Hill could spend $1,000,000 on this experiment.

The iPad may very well turn out to be an excellent, maybe even game-changing, device. But let’s at least give it a test drive. If the iPad proves a flop—Steve Jobs is not without his failures; remember the Lisa, the Cube, and the Apple TV?—Seton Hill will have spent an awful lot of money simply to (and I hate to put it this way) tie itself to the Apple hype machine for a day or two.

Things of History, History of Things

I have just started listening to an new podcast from the BBC, A History of the World in 100 Objects, written and narrated by Neil MacGregor, Director of the British Museum. Aside from the obvious reductionism and the occasionally irritating interstitials (lots of ambient chanting and pan flute music), the show is excellent, taking one hundred objects from the British Museum’s collections to tell the history of the world from the point of view of its material culture. MacGregor is a natural, and his guests—fellow curators, historians, archaeologists, anthropologists, and others—are engaging story tellers.

Yet even more interesting from an educational point of view are the live “readings” of artifacts these scholars provide, demonstrating to the audience just how experts tease knowledge from primary source objects. This is much the lesson we at CHNM attempted in our Object of History collaboration with the Smithsonian’s National Museum of American History. The focus was narrower—six iconic objects in U.S. History—but the idea was the same: objects have histories and their curators very particular expertise in bringing those histories to light.

Briefly Noted: Universal Museum APIs; Raw Data Now!; Publish or Perish

Mia Ridge, Lead Web Developer at the Science Museum, London (where I’m a research fellow, incidentally) points to Museums and the machine-processable web, a new wiki “for sharing, discussing, arguing over and hopefully coming to some common agreements on APIs and data schemas for museum collections.”

Following closely on that, Tim Berners-Lee calls for “Raw Data Now!” at the TED Conference, suggesting that linked raw data may be poised to displace more finished works (journal articles, websites) as the main unit of scientific production. Interesting, provocative parallels to the digital humanities.

And not entirely unrelated, Mark Bauerlein considers the problems of “publish or perish” in Professors on the Production Line, Students on Their Own [.pdf]. Asking “Do any major works or authors lack editors, interpreters, theorists, reviewers, and teachers?” Bauerlein answers “the ‘coverage’ project is complete” and suggests departments turn back to teaching. “We need honest and open public acknowledgment that the scholarly enterprise has lost its rationale,” he concludes, “that central parts of the humanities are in real trouble, and that the surest way to restoration lies in a renewed commitment to the undergraduate student.” I can’t say I always agree with Bauerlein (see, for example, www.dumbestgeneration.com) but he’s invariably worth reading.

Tragedy at the Commons

Nat Torkington at the O’Reilly Radar blog has news this morning that George Oates, Senior Program Manager in charge of Flickr Commons and an original member of the Flickr design team, has been laid off by Flickr’s parent company Yahoo! As the person at Yahoo! responsible for bringing together the energy and cultural resources of the Smithsonian Institution, the Library of Congress, the Powerhouse Museum, the National Library of New Zealand, the Library of Virginia, the Imperial War Museum, and, most recently, the New York Public Library, Oates has quietly done as much as anyone in the past several years to increase and improve online access to cultural heritage collections around the world. It’s sad enough just at that. But Oates’ layoff also raises some larger questions. Is this just one of those things we see in a bad economy, or is it a reason why cultural organizations should roll their own rather than using commercial services for online work?

Torkington believes that the enthusiasm and community Flickr Commons has attracted will sustain the project through the economic downturn and what at best is likely to be a period of neglect by Flickr and its parent. Let’s hope so. A less rosy scenario is that Yahoo! decides that in tough economic times the goodwill and visibility generated by hosting the educational and cultural heritage materials of public institutions isn’t worth the cost of bandwidth.

This story drove home to me a contradiction in my own rhetoric that I hadn’t noticed before. On the one hand I have been a proponent of Flickr Commons, university channels on Google’s YouTube, and the like, recommending them to partners and colleagues as an easy way to reach out to new audiences, build communities around content, and basically just get your stuff up without the hassle of software and sys admin. On the other hand, I have repeatedly criticized the enthusiasm some digital humanists have shown for Second Life, in large part on the basis of the fact that Linden Lab (SL’s parent company) could at any moment go under or simply decide to take another business direction—and in doing so take with them all the hard, largely publicly-funded work museums, libraries, and digital humanists have put into the platform. Only today, when I read of George Oates’ sacking, did I realize that what’s good for the goose should be good for the gander. While the long term prospects of Yahoo! and especially Google may be brighter than those of Linden Lab, nevertheless they are still big companies whose first responsibility is to their shareholders and the bottom line, not to cultural heritage, education, or the work of digital humanities.

My guess is that Flickr Commons will be just fine, and I still believe there is a lot of good in the idea. But the news about George Oates, someone who was universally well-regarded in our business and in the web business more generally, should give all of us pause. Specifically, it should let us ask again whether the benefits in ease, reach, and community of using commercial services for presenting cultural heritage collections and educational resources really outweigh the costs in storage, systems administration, and content segregation of rolling your own.

I’d love to hear your thoughts.

Briefly Noted for December 19, 2008

Ahoy, Mateys! Mills Kelly’s fall semester course “Lying about the Past” was revealed today in The Chronicle of Higher Education. Read how Mills and his students perpetrated an internet hoax about “the last American pirate” and what they learned in the process. The Chronicle is, unfortunately, gated, but you can read more on Mills’ fantastic blog, edwired.

I’m sure many of you have encountered NITLE’s prediction markets, but a recent presentation at CNI by NITLE’s Director of Research Bryan Alexander reminded me I haven’t blogged it yet. As I told Bryan recently, the prediction markets are a great example of form (crowdsourcing educational technology intelligence) fitting function (NITLE’s mission to advise member schools on emergent practices) in the digital humanities.

Sadly, The Times of London recently reported a raid on the offices of Memorial, a human rights and educational organization that seeks to document the abuses of the Soviet Gulag prison camp system. Memorial was a key partner on CHNM’s Gulag: Many Days, Many Lives, and its generous research assistance and loan of documents, images, and other artifacts was essential to our successful completion of the project. It is very sad to see this brave and worthy organization suffering the same abuses in Putin’s Russia that it has worked so hard to expose in Stalin’s.

Last month the Smithsonian Institution’s National Museum of American History (NMAH) celebrated its grand reopening after an extended closure for major renovations. Meanwhile, in the web space, NMAH launched its History Explorer, which aggregates and categorizes online educational content from across the museum. Worth a look.