As a follow up to last week’s post, a question: why start with the trilogy of systems, agents, networks? Why not start more directly – by, say, identifying Black Swan nesting grounds? It’s an important question, so I’d like to address it prior to next week’s session.
Much as it would be nice to hope that workshops on likely sources for Black Swan events would set the stage for event readiness, the evidence unfortunately does not point in that direction. For instance, many people either participated in or reviewed the outcomes of the Event 201 Pandemic Exercise – but their presence does not appear to have had any great impact on subsequent COVID-related policies. Nor can I claim any great successes with this approach: prior to 2008 and 2015, I highlighted in my talks economic history and human migrations as crucial topics for K-20 education, but saw few instances of actual curricular adoption.
So, developing a new set of habits of mind may be a more indirect approach – but it may also be a more successful one. But then, why this trilogy? The answer has both a constructive aspect – the features of Black Swans that it illuminates – but also a deconstructive one: the misconceptions about Black Swans it dispels.
One if the most common misconceptions about Black Swans is that their unpredictability results from the singularity of triggering events. In this view, Black Swans cannot be predicted because they are the result of a once-in-a-lifetime occurrence, never seen before, or to be seen again. This leads directly to a sense of helplessness and associated blamelessness: the fates are against us, and mere mortals are but the plaything of their whims. Nor only is this viewpoint incorrect and untrue – it is also dangerous and poisonous, leading to passivity and a surrender of agency.
Black Swans are generally not the result of unusual events – they are rather the outcome of couplings between distinct entities, encompassing component parts with varying degrees of autonomy, and interconnected in multiple ways. A simple (non Black Swan example) may be helpful here. The behavior of sandpiles as more sand is drizzled onto them has been extensively modeled and studied: small avalanches will be triggered at different points, with different frequencies based upon (among other factors) the stack height, dampness of sand grains, rate of sand addition, etc. Any given avalanche is a priori unpredictable, although it is not a Black Swan event (for instance, the statistical distribution of sand cascades is predictable in a way that Black Swan events are not), but start thinking about what might happen if multiple sandpiles started interacting with each other, and you’ll be closer to Black Swan insights.
Now, these insights wouldn’t be much use if they still left us in a passive position – but, as we’ll see, they can form the basis for planning and action that goes beyond mere reaction. How? Well, that’s the topic of the sessions – and of more blogs to come.
“One of the major problems encountered in time travel is not that of accidentally becoming your own father or mother. […] The major problem is quite simply one of grammar, and the main work to consult in this matter is Dr. Dan Streetmentioner’s Time Traveler’s Handbook of 1001 Tense Formations. It will tell you, for instance, how to describe something that was about to happen to you in the past before you avoided it by time-jumping forward two days in order to avoid it.”
Douglas Adams, The Hitchhiker’s Guide to the Galaxy
Many of the events that shape the world of learning and academe are not slow, gradual changes. Rather, they belong to the category of Black Swans, events that:
- cannot be predicted ahead of time;
- have an extreme impact;
- can be rationalized or understood retrospectively, but not prospectively.
The lack of predictability of Black Swans might lead someone to write them off as “just one of those things” that you “just have to bear” – and nothing could be more wrong or more destructive. It is possible to design institutions and plans for action that, without predicting the unpredictable, are either resilient in the face of Black Swans, or – even better – antifragile, a term coined by Nassim Taleb to describe entities that actively benefit from unexpected shocks.
Black Swan thinking and antifragile design require a toolkit that is very different from traditional planning approaches. In order to address this need, I will be leading a 6-month project, sponsored by the ShapingEDU group at ASU, to develop such a toolkit for K-20 institutions. It comprises three stages:
Stage 1: The End of Fairytales
- A multisession course, focusing on entities at three key levels of analysis and planning – systems, agents, and networks – required to identify the nesting grounds of Black Swans, and develop habits of mind and sets of responses to the unknown.
Stage 2: Painting Antifragile Learning (Not) by Numbers
- A design studio, reframing SAMR as a tool not just for identifying and implementing optimal uses of technology in teaching and learning, but also as a guiding scaffold underpinning learning experiences that do much more than just stand up to rapid change.
Stage 3: The Great Swan Game
- A day-long scenario game, inviting teams from a diverse range of academic institutions to leverage and apply the knowledge gained in the first two stages. Their goal: to design organizations and learning frameworks that can thrive amid flocks of particularly ill-tempered Black Swans.
It’s an honor to have been selected by ASU and ShapingEDU as an Innovator In Residence and to develop this project. I welcome everyone to the virtual pond – kits for building your own pith helmet and binoculars will be provided.
The slides for my January 9 online FTTE session are available below:
The slides for my ShapingEDU Live session are now online:
A recording of the session is also available:
Of Swans & Dragons – YouTube Recording
We are not wholly bad or good,
Who live our lives under Milk Wood
– Dylan Thomas, Under Milk Wood
I frequently get asked these days, what’s next for SAMR? Are all its details set in final form? After all, the model is about twenty years old now – and its complementary partner, the EdTech Quintet has been around for somewhat under a decade.
When I first introduced the SAMR model, many applications of IT in education were in comparatively early stages. Fast forward to today, and that toolset has matured considerably, although its components have not changed significantly. However, one area has emerged in the last few years in ways that were not visible at the birth of SAMR: AI and its applications, not just in education, but in the world as a whole.
The changes introduced by AI cannot be underestimated: the more robust estimations of its impact upon the workforce, for instance, point to a majority of all jobs undergoing significant task changes, replacements, and redesign as a result. Adoption has been rapid, not just in fields like medicine and law enforcement, but also in education. As one example, AI-driven advising and tutoring systems have become commonplace in higher education.
All of which could be viewed as a net positive, were it not for one small detail: the workings of the new AI systems tend to be opaque to the individuals charged with deciding to implement them – and even more so to those whose careers and lives will be directly affected by them. The results are not pretty: in one recent study, for instance, an AI tool widely used to help manage healthcare decisions exhibited significant racial bias, despite explicitly prejudicial decisions never having been a component of its design.
Much of the reaction in the popular press has tended to run in the direction of AI as a fundamentally inscrutable savior/demon – which is obscurantist nonsense. As serious researchers know, nothing makes AI essentially opaque – complex or challenging, yes, but not inscrutably mysterious.
Which is where SAMR reenters the picture. As educators and learners who have used the model know, when tasks shift from S to R, an interesting process takes place: the impact of technology use upon learning outcomes is enhanced, while simultaneously learners also generally gain agency as a result of the shift. And that is exactly what is called for in the context of AI: processes that increase agency relative to AI for those most likely to be affected by it.
The good news is that the core structure of SAMR works well in this new context – but it needs to be supplemented by some new tools to fulfill its role. One key component of this toolset is the introduction of aspects of AI into learning experiences in such a way that learners gain true creative skills and understanding relative to AI, and not just a superficial cocktail party familiarity with some of its features. At the recent AMEE conference in Vienna, I highlighted aspects of this approach relative to the education of future physicians.
There is another component of the toolset that is also important, and that is the introduction of thinking tools to deal with the rapid and unforeseen changes that are likely to result from the ways that AI is being introduced – what are known (in one incarnation) as black swans. These thinking tools have value beyond AI, of course – and other components of today’s world, such as climate change and social media interactions likewise call upon this toolset for effective understanding and policy definition. Supported by the ShapingEDU team at ASU, I will be presenting a series of sessions on this topic, both in the context of SAMR, but also independent of it; the first session is scheduled to take place on November 20.
As in the quote from Under Milk Wood that opened this blog post, AI – and what we do with it – is neither wholly bad or good. But to realize its better side will require a wealth of approaches that might dwarf even the richly diverse cast of characters inhabiting Dylan Thomas’ mythical Welsh town.
The slides for my PechaKucha presentation at the AMEE 2019 closing plenary are now online:
The slides for my workshop at AMEE 2019 are now online: