I recently posted this piece on the MV Ventures blog. It provoked a nice response, so I thought it was worth sharing (again) here on Rooted.

And What Do You Mean by Machine Learning?

We are living in times of accelerated change, the kind of era when transformation, without a doubt, needs a guide. Just think of the changes that have occurred this year with AI, ChatGPT and machine learning.

But one thing that hasn’t changed is the ingenuity, creativity, and curiosity of young learners – qualities that unmistakably show up in each and every one of them every single day. Every educator loves watching a child’s eyes get wide, eyebrows raised with an open-mouthed smile, announcing through the look of sheer joy: “Hey! I’ve got it! I think I get this!”

Human learning is a capacity unlike anything else we’ve seen in the natural or technological world, and its exceptionalism is fueled by our curiosity, a quality we don’t detect in the recent AI technologies that are accelerating so much change.

Now, machines are learning and doing so quickly, and there’s lots of buzz in the media for good reasons. After all:

• What are these machines actually learning?
• Can we explain how the machine is learning, knowing Deep Neural Networks are largely inexplicable?
• Whom or what is the machine learning from, and is that “learning material” biased?
• What are these machines learning about ME and about my private data?
• And who owns all this “learning”?

But here’s an even more basic question: What do we actually mean by “Machine Learning”? Is it the same as Human Learning? This conjures Seymour Sarason’s classic question: And What Do You Mean by Learning? (2004)

Neuroscientist Stanislas Dehaene understands human learning as being supported by four key pillars:

1. Human learning requires focused attention. In a world with so much information, human brains are good at selecting, amplifying, and processing specific things. Machines, however, “waste considerable time analyzing all possible combinations of the data provided to them, instead of sorting out the information and focusing on the relevant bits” (148).
2. Human learning requires active engagement: “We do not simply passively wait for new information to reach us–as do, most current artificial neural networks, which are simple input-output functions passively submitted to their environment. We humans are born with a passion to know, and we constantly seek novelty, actively exploring our environment to discover things we can learn” (187).  Active engagement requires humans from early ages to build cognitive models of the world, and curiosity occurs when there’s something limited or missing about our model that needs to be adjusted. “Even the most advanced computer architectures,” writes Dehaene, “fall short of any human infant’s ability to build abstract models of the world” (xxiv). It’s the difference between machines using “statistical regularities in data” and humans comprehending “high-level abstract concepts” (28-29).
3. Human Learning requires positive error feedback, “which compares our predictions with reality and corrects our models of the world” (145). In some respects, Machine Learning has made a lot of advancements with feedback algorithms. However, the feedback used for Deep Neural Networks is what John Hattie might call “correct and direct feedback,” meaning it isn’t open ended or posited as wonders or what ifs for the learner to think about and comprehend. It’s the difference between “deep learning” and “deep understanding” (Marcus and Davis 66). “Correct and direct” feedback does not require cognitive reflection or higher levels of depth of knowledge, so much as recall and reproduction.
4. Lastly, Human Learning requires consolidation, “which involves sleep as a key component” (146). Machines do not need to power down nearly as frequently for the learning to stick. However, when we sleep, Dehaene tells us, our brains are very active replaying experiences from the day all while transferring data to memory compartments of the brain, which is actually an advantage: “Sleep seems to solve a problem that all learning algorithms face: the scarcity of the data available for training. To learn, current artificial neural networks need huge data sets–but life is too short, and our brain has to make do with the limited amount of information it can gather during the day” (232). Sleep affords human learning another unique advantage: our sacred, human practice of daring to dream of other worlds, other lived experiences, and other realities, which fuels our ingenuity, creativity, and curiosity.

So, what do we mean by learning when it comes to humans or machines?

If Galileo were a machine learning system, he would have dropped objects after objects off the tower, recorded his results, and eventually detected a pattern to which a predicted output would be based, namely that all other objects are also going to drop at the same rate as the ones before. Machines, in other words, learn through inductive, statistical inferencing.

The human Galileo, on the other hand, had a hunch, an inspiration – dare we call it a dream? – based on his cognitive model of the world. Gary Marcus and Ernest Davis write, “The more you can make solid inferences without trying things out, the better. In this kind of everyday reasoning, humans are miles and miles ahead of anything we have ever seen in AI” (110). I’m sure Galileo tested his hypothesis, but that’s the point. It started with a hypothesis or a model of the world. And that’s the differentiator: we know that there is a world around us. We have intuition about organic and inorganic things, and we learn by adjusting and expanding our rich cognitive models as we encounter the infinite, novel aspects of our ever-changing environment.

It’s the difference between seeing data versus seeing concepts. It’s the difference between inductive methods of learning (machines) and what Erik Larson calls abductive methods for learning: “Whereas induction treats observation as facts (data) that can be analyzed, abduction views an observed fact as a sign that points to a feature of the world” (163). It’s why “the easy things are hard” as Marvin Minsky once said about AI’s greater challenges. We have image recognition technology that through millions of data samples can recognize what a dog is, whereas my daughter encountered a handful of dogs in her early years and was able to recognize all sorts of novel cases thereafter.

Her ingenuity, creativity, and curiosity fires on all cylinders because she knows she is in a world and she wants to soak it up. She wants to know about dogs, not because the pattern of pixels keeps recurring, but because she is sharing an experience with others.

So, what do we mean by machine learning? Well, it’s obviously something very different from what humans have been doing for eons. Knowing what machine learning is not, it makes one thing clear: we cannot kill curiosity in our children. It’s what sets them apart from the robots; it’s the reason we got into this profession in the first place, and it’s what inspires them to dream.

As AI expert Melanie Mitchell once wrote, “An integral part of understanding a situation is being able to use your mental models to imagine different possible futures” (238) In terms of Norman Webb’s depth of knowledge framework: are we getting kids to adjust and expand their mental models (human learning) by daring them to ask What if? Or should we really feel threatened by the rise of AI because most of what we’re doing is feeding lots of data to passive human learners?

Sources:

Dehaene, Stanislas. How We Learn: Why Brains Learn Better Than Any Machine… For Now. Penguin Books, 2021.
Hattie, John. Visible Learning. Routledge, 2008.
Larson, Erik J. The Myth of Artificial Intelligence: Why Computers Can’t Think the Way We Do.
Belknap Press, 2022.
Marcus, Gary and Ernest Davis. Rebooting AI: Building Artificial Intelligence We Can Trust. Vintage, 2020.
Mitchell, Melanie. Artificial Intelligence: A Guide for Thinking Humans. Pelican Books, 2020.
Sarason, Seymour. And What Do You Mean by Learning? Heinemann, 2004.

Recently, I attended the National Association of Independent Schools Annual Conference in St. Louis. I was excited to go because (1) it was my first time to present at NAIS, something I had been wanting to do for a long time, and (2) I was eager to hear from others, in person, about how they as school leaders, instructors, and industry consultants were making sense of and responding to the recent, ubiquitous success of 2nd Wave, generative AI technologies. Full disclosure: I too was presenting on that same subject, along with Dr. Brett Jacobsen and Josh Clark, in a session titled, “Are You AI Ready? A People-Centered Systems Thinking Approach.”

In almost every session and conversation I participated in, the focus quickly (and understandably) became one around questions of AI *policy*, a word which Google defines as “a course or principle of action adopted or proposed by a government, party, business, or individual.” In other words, policy, as defined here, serves as a mechanism for making the right decision in a near or distant future predicament. However, something as complex and unpredictable as AI begs us to ask: What if there are no clear, right answers? What if AI is so fluid in its current emergence that no policy can possibly predict any and all plausible and possible scenarios? In those cases, our policies, like early AI technologies, may turn out to be brittle tools, and the old adage that “policies are meant to be broken” once again proves probable, if not inevitable.

With that said, what would be a better approach to this conversation? Or put differently, what’s an alternative mental model we might use, other than a policy-minded approach, to expand our perspective on how we might act on and respond to opportunities and challenges presented by AI, both in the immediate and more distant future?

At MV Ventures, we introduced the 5 P’s framework for understanding the probable, plausible, and possible implications of AI on our institution’s people, protection, practices, and programming to shape an alternative mental model for grounding our position, not policy. How we understand these implications does less to carve out specific, prescriptive policies for AI’s use (or prohibition thereof) and more to help us determine a robust, strategic positioning, one that’s ready for all possible opportunities and challenges.

Demands for policy are born out of a school of thought (or mental model) that operates from a certain assumption about how businesses and organizations should develop strategy when thinking about future challenges and predicaments. Kees van der Heijden in his book, Scenarios: The Art of Strategic Conversation, writes, “Over the years, three schools of thought have arisen to interpret the way managers and entrepreneurs think about their daily business [and strategy]. These can be characterized as rationalist, evolutionary, and processual” (23). The call for policy in the face of AI’s unpredictable power and impact is the kind of call that is born out of a rationalist school of thought or mental model.

The rationalist manager or planner treats “strategy as a process of searching for the maximum utility among a number of options,” which is predicated on assumptions like: the future is predictable to some degree; there is a single, best answer in most situations; or people will act rationally in future situations (23-24). In order for us to search, with confidence, for maximum utility when faced with future challenges, we have to have a pretty good notion of what the future will look like and be able to predict, with accuracy, various actors’ actions and reactions. If we can assume as much, then forecasting the likely future for purposes of developing the best policy makes sense as an approach, but keep in mind that “the assumption underlying forecasting is that some people can be more expert than others in predicting what will happen, and the best we can do is ask them for their considered opinion of what might be in store” (27).

If AI policy is our demand, then we risk demanding it on these terms with the kind of assumptions we just mentioned, which leads me to ask: Who are those experts? Where are these expert systems (to put it in AI terminology) or Delphic oracles that can help us identify our optimum policy for AI use and adoption in our schools? Who has the forecast we need for this?

Even if we find some oracle-like experts, here’s the risk to that approach:

1. Complexity eats expertise for lunch every single day.
2. The future cannot be predicted because the future does not exist. (Jim Dator’s 1st law of futures work)
3. We shape our tools and thereafter our tools shape us. (Jim Dator’s 3rd law of futures work)

AI is complex, so proposition #1 is relevant, and we don’t know the future, so forecasting from a rationalist perspective will always be a gamble. And sure, we shape our tools and models for crafting strategy, but they determine in return what we see and amplify versus what we overlook or ignore.

It’s important to keep in mind, however, that “we have to forecast. We couldn’t drive a car [at night or in a storm] with the lights switched off altogether. The important thing is to realize the limits of our view. Making predictions beyond our capability to forecast lies at the bottom of the crises of perception” (90). Forecasting works well when dealing with known risks in the immediate future, and therefore we should engage in that kind of work. But when faced with “structural change” or “structural uncertainty,” forecasting (and the “right answer policies” that follow it) could lead to a “crisis of perception” or the “inability to see an emergent novel reality by being locked inside obsolete assumptions” (31).

I think it’s safe to say that AI is exactly the kind of “emergent novel reality” that brings new structural uncertainties to our schools and beyond, structural uncertainties that no oracle or expert system could predict with complete accuracy. That’s why the topic demands a systems-thinking approach (like the 5 P’s framework), or a new mental model that stretches us to perceive more than what forecasting and policy-making can afford us.

What we need is a processual, scenarios-based approach that expands our foresight, as opposed to narrowing our forecast. “Scenarios are not seen as quasi-forecasts,” writes van der Heijden, “but as perception devices… Scenarios are a set of reasonably plausible, but structurally different futures. These are conceived through a process of causal, rather than probabilistic thinking… Scenarios are used as a means of thinking through strategy against a number of structurally quite different, but plausible future models of the world” (29). Because scenarios are multiple, equally plausible, and neither good nor bad, they help us develop a mental model and mode of “scenaric perception” such that we can “react flexibly to structural change” in a variety of contexts or lived realities (31).

Unlike forecasting, scenarios-planning is not prescriptive, nor does it reduce or eliminate irreducible uncertainties. Also, it welcomes diverse views, all for the purposes of determining a current position that maintains “the scenaric stance,” or “a state of constructively maintaining multiple states of possibility in mind at the same time – considering, processing, evaluating, and being ready” (Smith 4). This is why Scott Smith says that foresight work or what he calls “futuring” is “ about understanding the landscape of potential futures in such a way as to guide better decision making in the present” – a decision making that comes less from policy and more from teams who understand the present in terms of multiple, equally plausible futures (Smith 104). And the decisions that are eventually made are driven not by the idea that we can link the best policy to the most probable future but by the desire to position one’s organization in a way that, no matter which plausible future unfolds, the stakeholders are ready to play a part in strategically shaping the unpredictable future that ends up being the case with the hopes of influencing that future toward preferable outcomes.

Kees van der Heijden emphasizes over and over that, “While forecasts are decision making devices, scenarios are policy development tools” (57).  But the more I think about what we mean by policy, the more I want to suggest, humbly of course, we alter his point to say that “scenarios are tools that help us develop a more strategic, agile position.” What we need with AI is not policy, but a robust, agile, values-informed position that can respond to any of the myriad futures that await us near and far. Otherwise, we may find ourselves in the midst of a crisis of perception as the world of AI technology only continues to accelerate.

As stated, rationalist forecasting does have its place, especially if we add the dimension of time. There are things right in front of us, that are probable, that need our care and attention, that therefore warrant some rationalist forecasting – like the use of Deepfakes among peer groups in school. Then there are things that are a little more beyond the trees, like the development of real-time, human-level language translators, where it warrants our thinking through multiple, plausible scenarios to improve our practice and position in World Language instruction right now.

A simple example from the field

When teachers approached me in January 2023 about developing an AI classroom policy, I refused to take a rationalist, one-policy-catch-all approach, and instead tried to create a tool for developing a position that assisted multiple scenarios and multiple users, which led to the following framework:

As teams we workshopped the tool, developing do’s and don’ts nudges for three possible scenarios: (1) early adopters who were excited to accelerate the experimental use of AI in their classes; (2) skeptics who rightfully worried about an accelerated adoption of AI technologies; and (3) teachers who suspected that AI was used in a way that compromised the integrity of the inquiry process.  None of these scenarios needed a singular, if-then policy per se; what they needed were tools and maneuvers that honored our schools’ values and mission as a place of “inquiry, innovation, and impact” while supporting a teacher’s classroom system in the context of multiple, plausible situations. The only place where traditional policy lives in the showcased tool is in the bottom right quadrant (I know something happened, now what?), and what we discovered is that the policy already existed: the age-old (not emergent) issue of academic honesty. We didn't need new policy, but we desperately needed to think through plausible scenarios to better define our values-informed position in the now.

Sources:

1. Badminton, Nikolas. Facing our Futures: How Foresight, Futures Design, and Strategy Creates Prosperity and Growth. Bloomsbury, 2023.
2. Smith, Scott. How to Future: Leading and Sense-Making in an Age of Hyperchange. Kogan Page, 2020.
3. van der Heijden, Kees. Scenarios: The Art of Strategic Conversation. Wiley, 1996.

Why are so many kids, despite our best efforts, not all right when it comes to behavioral health and wellness?

Is our interdependent connection to nature an example of what we fail to account for when attempting to nurture a learner's wellness, identity development, and grounded sense of purpose?

And what if Artificial Intelligence could play a role in all this? What if the recent popularity and success of AI could help us knock loose a new way of thinking "which seeks to override our human tendency to separate ourselves from the natural world"?

Transforming Schools While Breathing Under Water: Aquaman Learns About Physics

Let's start with a thought experiment.

Picture the iconic comic book hero, Aquaman, but this Aquaman has never been to the earth’s surface; he doesn’t even know that he is underwater.

One day, however, our aquatic, humanoid friend finds a book in a sunken ship at the deepest depths of our ocean’s floor: let’s pretend that the book explains the physics of how gravity works (gravity under normal conditions on the earth’s surface, of course), and using that textbook, Aquaman begins to learn about the predictable motions of objects due to gravitational force.

However, nothing seems to add up when he tests the theory or law in his normal, environmental circumstances, because our Aquaman fails to realize or see that he is immersed in and connected to an aquatic environment that is an integral factor for how gravity actually works in his underwater, lived experience.

Sometimes I feel like this when seeking, collectively and collaboratively, to improve so much about the school experience.

Sometimes it feels like things just don't add up the way we want them to when we attempt to apply theories, best practices, or purported “laws of educational physics” to how we run schools and help kids.  Take behavioral health and wellness, for instance: our unprecedented crisis of rising rates of depression and anxiety among kids in today's educational institutions (Abrams 2023). Take the fact that so many kids report a sense of purposelessness in our schools and our beyond (Lavasseur 2021). Could the theories and best practices we use to address such a crisis be failing here, not because the theories are wrong, but because we are employing them in a context where we’re ignoring an integral factor that makes this all make sense? Is there some kind of water-like substance in the ether of schooling that we're not accounting for as we float theories, solutions, and intervention plans that just don't seem to be delivering like promised? What is that substance or factor?

In Aquaman’s case, it was the all-encompassing presence of literal water as a physical-material factor impacting the physics of his daily practices. It's no surprise he's frustrated, or resigned to the idea that his newfound physics textbook is total baloney. He doesn't see nor understand the complete reality of his water-immersed environment and his interconnected place in it, which begs the question...

Nature and Cities: Plato's Phaedrus and the Cosmopolitan Animal

In Plato’s Phaedrus, (a text I’ve written about here, here, and here) Socrates proclaims quite confidently,  “... I’m a lover of learning, and trees and open country won’t teach me anything, whereas men in the town do” (250D, emphasis is mine). Stated after his interlocutor, Phaedrus, urges Socrates to accompany him for a discussion outside the city walls of Athens, Socrates offers this interesting juxtaposition or binary – an opposition of cities versus nature – as if cities exist completely separate from nature, which of course further suggests that humans, in some way, are set apart from nature as well, particularly when it comes to learning and intelligence.

I think of Melanie Challenger’s book, How To Be Animal: A New History of What It Means To Be Human, where she announces, “The world is now dominated by an animal that doesn’t think it’s an animal. And the future is being imagined by an animal that doesn’t want to be an animal. This matters” (1). It matters because the human animals we’re talking about “are agents of evolution with far greater powers than sexual selection and selective breeding,” because the human advantage, as Socrates implies, really is our capacity for learning quickly and more deeply. It’s why Stanislas Dehaene describes us as Homo docens, “the species that teaches itself,” writing, “Natural selection, Darwin’s remarkably efficient algorithm, can certainly succeed in adapting each organism to its ecological niche, but it does so at an appallingly slow rate… The ability to learn, on the other hand, acts much faster – it can change behavior within the span of a few minutes, which is the very quintessence of learning: being able to adapt to unpredictable conditions as quickly as possible” (xix).  

We have the capacity to adapt and even change the course of evolution, not just for us, but for our entire interconnected environment in the natural world, an amazing but also frightening capacity, especially if we are in denial about our own animal identity. As we've entered the anthropocene era, we pretend like we’re not even swimming in the same waters, like human culture and the natural world are islands apart. A human-centric view, for sure, but it's not a learner-centered one, and I think the kids are catching on to this.

Challenger's provocation is a response to our Greek philosophical legacy, a legacy that learners are no longer buying. They're reading social media headlines, following influencers like Greta Thunberg, and starting to take seriously our place on the planet, and if we as school leaders don't take seriously our interdependent role of influence in the “thicket of life” we call nature, the younger generations will detect the gaslighting (whether it's intentional or not), exposing them to greater likelihood of alienation and disconnection.

Like Challenger said, our place in nature matters, and I believe it matters as well when talking about depression and anxiety rates among the youth, when thinking about their sense of belonging and connectedness. A connectedness that is always already entangled with our natural world. Yet, as the planet reveals its pain to us, society spends more time celebrating the fantasies of billionaires who dream of colonizing Mars, when we should be talking about how to build, together, in our entanglement, regenerative communities of sustainability and belonging.

On Turning Outward

As "moderns" we've done a decent job of turning inward (and we should) when addressing health and wellness, but we more often forget the need of also turning outward by valuing, nurturing, and taking seriously our integral, interdependent connection to the natural world that, contra Socrates, we are inseparably apart of, which beckons us to ask:

If so, we need “a [new] way of thinking,” writes James Bridle, “which seeks to override our human tendency to separate ourselves from the natural world… Conventional terms such as ‘the environment’ and even ‘nature’ itself (particularly when opposed to ‘culture’), compound the erroneous idea that there is a neat divide in the world between us and them, between humans and non-humans, between our lives and the teeming, multitudinous living being of the planet” (17). How might we learn to think and see our connectedness anew, to leverage that connectedness to learn more deeply about ourselves and live healthier lives, and to understand that our connectedness is the ground upon which we design a better (more-than-human) world?

And what if Artificial Intelligence could play a role in all this? (Yes, you read that right.) What if the recent popularity and success of AI could help us knock loose this new way of thinking "which seeks to override our human tendency to separate ourselves from the natural world"?

Seeing the Magical and the Spiritual: A New Kind of "Multiple Intelligences"

Lots of recent literature on generative AI tends to fret and wonder about humanity's significance in the wake of technology's most recent demonstrations of brilliance. If we're no longer the smartest agents, where does it leave us as a species?

But what if we're missing out on another opportune perspective, namely, one that sees AI as a means for reconnecting with the natural world, as an entry point to belonging to something greater and more-than-human?

To understand what I mean, consider the case for making learning and inquiry in schools a more spiritual endeavor – a spirituality, however, that is not anthropocentric, but magical in nature. Just bear with me.

Many indigenous spiritual practitioners, according to David Abram, often claim a kind of magic – a capacity best defined as “the ability or power to alter one’s consciousness at will” (9). He writes, “The traditional magician cultivates an ability to shift out of his or her common state of consciousness precisely in order to make contact with the other organic forms of sensitivity and awareness with which human existence is entwined… It is this, we might say, that defines a shaman: the ability to readily slip out of the perceptual boundaries that demarcate his or her particular culture in order to make contact with, and learn from, the other powers in the land… Magic, then, in its perhaps most primordial sense, is the experience of existing in a world made up of multiple intelligences” (9, emphasis mine). In other words, intelligence is not just to be found in cities like Athens (sorry, Socrates); we humans are a part of something greater, something more cosmic and interconnected, a network of collective intelligence, meaning our intelligence does not separate us so much as connect us to the sensuous, living world around us. This is what I mean by rethinking our response to intelligent machines in the context of our connection to nature's "intelligences."

To illustrate, David Abram shares the story of living with an indigenous magic practitioner in Bali, residing on a family compound consisting of several small buildings for sleeping and cooking. Every morning, when staying at the compound,  the Balian’s wife brought a bowl of fresh fruit to Abram while also balancing a tray with little mounds of rice; when she returned to get the fruit bowl, Abram noticed her rice tray would be empty. When asked what the rice was for, the woman responded, “They were offerings for the household spirits… gifts for the spirits of the family compound.” Abram observed that every morning, after delivering his fruit, she carefully set the rice “offerings” at the corners of each of the buildings, but he also confirmed that each rice offering was nowhere to be found upon inspection later that afternoon.

Where did the rice go?

The reason the rice disappeared, it turned out, was due to ants, several lines of them taking the different rice offerings and returning to their colonies away from the compound, a discovery that first provoked a sense of dismissiveness in Abram, until a humbler, more open-minded thought occurred to him: “What if the ants were the very ‘household spirits’ to whom the offerings were being made?” (12).

He ends the story with the following insight:

To Abram's point, intelligence is multiple, and despite what human exceptionalists will tell you, we are not the only intelligent agents, and nor should we want to be. This kind of spiritual awareness and inquiry is less about otherworldly, anthropomorphic visions of heavenly kingdoms and more about an ecological attunement and attentiveness to the interconnected world, an awareness we rarely cultivate nor tend to in our modern cities and schools, including Athens.

The Case for Spiritual Intelligence (whether "Authentic" or "Artificial"):

“For Plato, as for Socrates,” writes Abram, “the psyche is now that aspect of oneself that is refined and strengthened by turning away from the ordinary sensory world in order to contemplate the intelligible ideas, the pure and eternal forms that, alone, truly exist” (112-113). It’s why Socrates wants to remain walled up in the fortified security of human exceptionalism or, as we know it, the Athenian city-state. However, for Abram, “It is not by sending his awareness out beyond the natural world that the shaman makes contact with the purveyors of life and health, nor by journeying into his personal psyche; rather, it is by propelling his awareness laterally, outward into the depths of a landscape at once both sensuous and psychological, the living dream that we share with the soaring hawk, the spider, and the stone silently sprouting lichens on its course surface” (10). Being spiritual, in this context, is about conscious connection – about connecting to the multiple, natural intelligences and beings of the more-than-human world-network so we can understand more deeply how we might live, co-exist, and thrive in symbiotic relationships of regenerative interdependence.

And ants are no exception; they too demonstrate all sorts of forms of intelligence, as do forests, octupi, fungi, and even slime molds, if we are open and curious enough to really see it. And what's interesting, in my view, is how AI could help shift our perception accordingly, in just the right way, so we see all this interconnectedness with new clarity and precision.

Alan Turing famously asked, “May not machines carry out something which ought to be described as thinking but which is very different from what a man does?” And why stop with machines? In other words, why should we assume that thinking and intelligence are exclusively human characteristics? What if intelligence was a kind of behavior that connects us to something greater, something more cosmic, something more-than-human?

Shane Legg and Marcus Hunter define intelligence as that which “measures an agent’s ability to achieve goals in a wide range of environments” (Yonck 15). Stephen Hawking once described it as “the ability to adapt to change” (Yonck 16), and AI researchers, Gary Marcus and Ernest Davis, claim, “Intelligence is the ability to deploy novel means to attain a goal” (Marcus and Davis 30), much like computer scientist, Stuart Russell, who states, “an entity is intelligent to the extent that what it does is likely to achieve what it wants, given what it has perceived” (Russell 14). Hector Levesque writes, “...we want to say that people are behaving intelligently when they are making effective use of what they know to get what they want” (Levesque 40). And lastly, Alexander Wissner-Gross says, “Intelligence acts so as to maximize future freedom of action” (Yonck 18).

Definitions like these seem to corroborate Turing's point that there are multiple ways, beyond human biology, for intelligence (in Turing’s case, thinking) to manifest, prompting thinkers like Richard Yonck to state that “All intelligences needn’t mimic human intelligence to be considered of a high or perhaps even ‘higher’ order” (9). In fact, “...most aspects of intelligence may in fact exist on a spectrum” (15), leading Yonck to ponder the idea that intelligence may be “a manifestation of a much larger and universal process, one that is initially dependent on probability but, given the resultant emergent properties, becomes more and more capable of self-directed volition over time… As we progress from chemistry to prebiotic self-replication to single-cell life all the way to Homo sapiens, can we view each as its own emergent variation of adaptive collective intelligence relative to the precursors it evolved from?” (45). Abram seems to think the answer's yes, as he describes spiritual awareness as the ability to appreciate the interconnections between all cosmic forms of intelligence, and to leverage collectively intelligent networks for purposes of designing better, more sustainable forms of living: for instance, the Balian offerings of rice serve as a regenerative alternative to more ecologically destructive solutions like spraying pesticide all over one's natural environment to deter the ant intrusion. The Balian practitioner found a mutually beneficial solution that also honors the mutual intelligence of each agent.

For whatever reason, our Western culture, in my view, is experiencing a kind of spiritual numbness; some might even blame the effects of technology as playing a part. It's as if we're swimming in the waters of natural interconnectedness but pretending like we're an island unto ourselves, and ironically, technology (in form of AI) is besieging this mythological island of human exceptionalism, thereby threatening our status as the one and only cognitively intelligent species. And meanwhile, our planet is crying out in pain, while humans, including our young learners, feel all the more alone, even though intelligence may be the very thing that can renew our connectedness to something greater.

Intelligence swims all throughout nature, much like Aquaman in the ocean, yet nature might be the thing we keep ignoring, even though the science instructs otherwise. One study "suggests that interventions increasing both contact with, and connection, to nature, are likely to be needed in order to achieve synergistic improvements to human and planetary health" (Martin, et al., 2020). Another study suggests that "nature is a common source of meaning in people’s lives and [that] connecting with nature helps to provide meaning by addressing our need to find coherence, significance, and purpose" (Passmore and Krause,
2023). Some studies go so far as to claim "evidence for associations between nature exposure and improved cognitive function, brain activity, blood pressure, mental health, physical activity, and sleep" (Jiminez, et al., 2021).

Like Abram suggests, perhaps it's time for us to turn outward, and to see anew how we belong and connect to a natural, collective intelligence, and AI could help us with this outward-turning. As James Bridle writes, “But what if the meaning of AI is not to be found in the way it competes with, supersedes or supplants us? What if... its purpose is to open our eyes and minds to the reality of intelligence as something doable in all kinds of fantastic ways, many of them beyond our rational thinking?” (82-83). What if AI could help us see intelligence (and even the act of thinking) in ways we couldn't before, in ways that connect us spiritually and relationally to our ecological networks? And what if we saw all this as an integral part of caring for the behavioral health and wellness of our young learners?

AI, Biomimicry, and Expanding Mental Models

In their book, Ways of Being: Animals, Plants, Machines, The Search for Planetary Intelligence, Bridle recalls walking in a forest, one connected and nourished by the mycorrhizal networks of fungi that made the forest, less a collection of individual trees, and more of an interconnected, intelligent organism that communicates, feels, and reacts in ways we may have failed to see initially. Bridle expresses their appreciation for nature, and its mycorrhizal support system, conjuring the metaphorical power of "networks" to make sense of the dynamic, organic complexity: "My sudden awareness… [of] the redwood forest…[as] a vibrant, active network beneath my feet, through which vast quantities of information as well as nutrients were passing, was not entirely new to me. It was the same sensation I experienced when beginning to understand, and bring into view, the infrastructure of the internet: the vast, planet-spanning network of cables, wires, machines, and electromagnetic signals which sustains and regulates humanity today. These microprocessors and data [centers], undersea cables and wireless transmissions are our own mycorrhizal network, interpenetrating everyday life…” (79-80). Bridle's "sudden awareness" – his sudden shift in seeing and sense-making – was sparked by emergent technology: It's an example of “technological models," in this case, the world wide web, "enabling us to better understand natural processes which don’t initially seem to be accessible to our reasoning” (238).  

Bridle concludes with a provocation that speaks to the importance of making our human thinking tangible: "Systems of intelligence, computational ability – mycorrhizal networks, slime moulds, and ant colonies to name a few – have always existed in the natural world, but we had to recreate them in our labs and workshops before we were capable of recognizing them elsewhere” (194, emphasis is mine). Bridle's point is that once we made tangible our technological concepts of the world wide web and, later, neural networks, it knocked loose our petrified perceptions of the natural world, seeing forests anew as networks of complexity and interconnection, for instance. This could explain society's delayed acceptance of Suzanne Simard's work, as represented in her recent classic, Finding the Mother Tree: Discovering the Wisdom of Forests (2021). For years, the scientific community dismissed her work on the communicative capacities and intelligence of forest systems, but once we started manufacturing analogous models in the computer lab, it's like our perceptual awareness (when turned outward) changed dramatically, as if the act of making adjusted or expanded the mental models through which we perceive and make sense of the world.

And this is the difference, by the way, between machine learning and human learning. While machines are busy collecting data, we have the advantage of fitting data into cognitive models of the world. Learning for our species is less about accumulating facts and more about adjusting mental models to make deeper sense of the world around us. “Just as the advent of networking technologies from the 1960s onwards allowed us to perceive life in new ways, and to open ourselves to new relationships and new modes of being," writes Bridle, "perhaps the advent of intelligent technologies will allow us to perceive the rest of the thinking, acting, and being world in ways that are more interesting, more just, and more broadly mutually beneficial" (83).

On Slime Intelligence

What really excites me about the intersection of technology, nature, and human perception is the reciprocity of it all. Just as technology can transform how we see, make sense of, and relate to nature, the ecological systems of intelligence (out in the world) can also shape and transform how we understand modern technology and its use cases. Bridle shares how in recent years city planners, engineers, and scientists studied slime molds, specifically the species Physarum polycephalum, in order to recreate one of the modern world's more complicated transportation systems: "When researchers placed oat flakes... in the pattern of the cities surrounding Tokyo,... the slime mould quickly reproduced their efforts... Within a few hours it started to hone its web of threads into a highly efficient network for distributing nutrients between distant ‘stations,’ with stronger, more resilient trunk routes connecting central hubs. This wasn’t a simple, join-the-dots exercise either, but a realistic map... requiring the mould to make the same kind of trade-offs that engineers have to implement" (192). Just as technology shapes our perception of nature, the collective intelligence of our natural systems have the potential and power to teach us so much about our models for technology as well. And we should listen.

What this tells us is that biomimicry and AI are two opportunities to reconnect student inquiry to an interconnected system where everyone matters: Where we matter; where technology matters; and where slime molds matter too. With this said, how might acknowledging our entanglement with nature help make "the physics of school" make sense? More specifically, what can nature teach us, not just about transportation systems, but about behavioral health and wellness and about our place of belonging in the cosmos?

David Abram concludes his environmentalist call to awareness, The Spell of the Sensuous: Perception and Language in the More-Than-Human World (2017), lamenting our "progressive forgetting of the air" as a human species, which "has been accompanied by a concomitant internalization of human awareness… an isolated intelligence located ‘inside’ the material body [which] can only be understood in relation to the forgetting of the air, to the forgetting of this sensuous but unseen medium that continually flows in and out of the breathing body…" (257). This "unseen medium" is like the unacknowledged water in Aquaman's world – a world where the physics just doesn't seem to be working the way it was promised to play out.

As we respond to the rise of AI, especially in the context of rising rates of depression and anxiety, how do we not see it as the last nail in the coffin of human supremacy but as a reminder or invitation to reconnect, on our common ground of intelligence, with nature? As David Abram puts it, it's about reclaiming our connection with "earthly nature as a densely interconnected organic network–a ‘biospheric web’ wherein each entity draws its specific character from its relations, direct and indirect, to all the others... as an intertwined, and actively intertwining, lattice of mutually dependent phenomena, both sensorial and sentient, of which our own sensing bodies are a part” (Abram 85).

We are a part of something, and if AI can teach us anything, it's not that we matter less; it's that we matter all the more because the world relies on us, on our ingenuity, and on our empathic capacity to connect with the more-than-human system of life, beauty, and becoming.

What if we more intentionally invited students to join the sensuous, living network of belonging and collective intelligence? What if learner-centered pedagogy meant finding their place in this network and taking responsibility for it?

Sources:

1. Abrams, Zara. "Kids' Mental Health Is in Crisis. Here's What Psychologists Are Doing to Help."American Psychological Association's 2023 Trends Report, 1 January 2023. https://www.apa.org/monitor/2023/01/trends-improving-youth-mental-health.
2. Levasseur, Aran. "The Biggest Problem For Kids Today Isn't Stress, It's Lack of Purpose." Ed Post, 28 October 2019. https://www.edpost.com/stories/the-biggest-problem-for-kids-today-isnt-stress-its-lack-of-purpose.
3. Plato. Phaedrus from The Collected Dialogues of Plato. Trans. R. Hackworth (1952). Eds. Edith Hamilton and Huntington Cairns. Princeton University Press, 2009.
4. Challenger, Melanie. How To Be Animal: A New History of What It Means to Be Human. Penguin Books, 2021.
5. Dehaene, Stanislas. How We Learn: Why Brains Learn Better Than Any Machine... For Now. Penguin Books, 2020.
6. Bridle, James. Ways of Being: Animals, Plants, Machines: The Search for Planetary Intelligence. Farrar, Straus and Giroux, 2022.
7. Abram, David. The Spell of the Sensuous: Perception and Language in the More-Than-Human World. Vintage Books, 2017.
8. Turing, Alan M. "Computing Machinery and Intelligence." Mind, 49: 433-460, 1950.
9. Yonck, Richard. Future Minds: The Rise of Intelligence from the Big Bang to the End of the Universe. Arcade, 2020.
10. Marcus, Gary and Ernest Davis. Rebooting AI: Building Artificial Intelligence We Can Trust. Vintage, 2020.
11. Russell, Stuart. Human Compatible: Artificial Intelligence and the Problem of Control. Penguin Books, 2020.
12. Levesque, Hector. Common Sense, The Turing Test, and the Quest for Real AI. The MIT Press, 2017.
13. Martin, Leanne, Mathew P. White, Anne Hunt, Miles Richardson, Sabine Pahl, and Jim Burt. "Nature contact, nature connectedness and associations with health, wellbeing and pro-environmental behaviours." Journal of Environmental Psychology, 68, 101389, 2020. https://pearl.plymouth.ac.uk/
    bitstream/handle/10026.1/15691/Martin, White, Hunt, Richardson, Pahl & Burt(2020).pdf;jsessionid=EE4E6D6B734958A2E495AEA6728099AE?sequence=1.
14. Passmore, Holli-Anne and Ashley N. Krause. "The Beyond-Human Natural World: Providing Meaning and Making Meaning." Int. J. Environ. Res. Public Health, 20(12), 6170, 2023. https://www.mdpi.com/1660-4601/20/12/6170?type=check_update&version=1.
15. Jimenez, Marcia P., Nicole V. DeVille, Elise G. Elliott, Jessica E. Schiff, Grete E. Wilt, Jaime E. Hart, and Peter James. "Associations between Nature Exposure and Health: A Review of the Evidence." Int. J. Environ. Res. Public Health, 18(9): 4790, 2021. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8125471/.
16. Simard, Suzanne. Finding the Mother Tree: Discovering the Wisdom of the Forest. Vintage, 2022.
    

How do we leverage the human advantage to make human thinking visible in the age of cognitive technologies?

The End of High School English? Or, Photography's Not Dead!

The question of assessment practices in the context of artificial intelligence is on a lot of educators’ minds. Humanities and English teachers, for instance, have expressed their wonders and worries about generative Large Language Models, like ChatGPT, and the media frenzy that followed OpenAI's release of GPT-3 hasn't helped as well, with some pondering whether this signals the end of the high school English class or the sudden death of the classic college essay.

Questions worth exploring, for sure, but perhaps in less dramatic terms.

What I'd like to explore here feels less dramatic, I hope, but urgent nonetheless:

How do we distinguish human cognition from the work of machines? How do we make human thinking visible and therefore something we can assess with confidence?

A secondary question, connected to this, that worries many teachers is why would students choose to outsource their work to automated machines?

First off, the reality is that teachers and students have been playing “the imitation game” well before AI became publicly accessible like it is now. Am I assessing this student’s cognitive work, or did they read this online? Are students simply aping back what they heard the teacher say? Is that ok?

Ultimately, we have to define more precisely what kind of cognition we want to assess. Put another way, what's the competency we wish to measure?

If it’s recall, perhaps imitating the teacher is perfectly suited to the assignment. I discussed this in an earlier post, reminding readers that writing is a technology, not a competency. And yes, writing has, to some extent, been cognified, much like the digital camera from years previous. As Kevin Kelly writes, “...photography [well before writing] has been cognified. Contemporary phone cameras eliminated the layers of heavy glass by adding algorithms, computation, and intelligence to do the work that physical lenses once did. They use the intangible smartness to substitute for a physical shutter” (2016, 34). Despite this, there are still photography classes to be taught in the age of AI, meaning human creativity and ingenuity are still valuable and necessary parts of the artistic process. Technology did not signal "the death of photography" in schools.

The question, then, is not whether writing is dead (it's not!) but for what reason are we doing it and at what level of complexity (in terms of the greater context). After all, complex communication will be an important human competency in the age of intelligent machines.

Vigorous Complexity over Rigorous Difficulty

A tool that can help clarify matters related to measuring human cognition is Norman Webb’s 4-Level Depth of Knowledge framework, level one being recall and reproduction; level two, skills and application (comparing, summarizing, estimating, etc.); and levels three and four being reserved for more complex strategic and extended thinking, respectively.

In our current moment of technological progress, AI programs have superhuman ability when it comes to level one, and they prove to be pretty impressive in most cases when replicating the skills represented by Webb's level two. For AI programmers, “the difficult part of knowledge is not stating a fact, but representing that fact is a useful way” (Hawkins 2022, 120).

It’s the difference between knowing “what” and knowing “what if,” which scales perfectly with Webb’s four level framework. “What if” is a cognitive maneuver that demands us to extend our thinking in ways that robots cannot do very well because they operate by statistics, not by well-informed, imaginative speculation grounded in a model of a world that's informed by lived experience. It may seem like generative AI models (like ChatGPT) have made it more difficult to see and determine whose cognition we are assessing, but from another perspective, the dust has settled all the more definitively in the era of AI because one thing is clear: certain DOK levels are more suited to authentic, human intelligence versus the ones that are easily performed by artificially intelligent machines.  It is not about what you know; it is about what you do with what you know, and humans can do things that machines cannot. It's also not so much about rigor and difficulty, as it is about the vigor demanded of humans when faced with the complexity of a challenge or task (Webb 1, 2021).

The Human Advantage is about Complexity

In my last post, I referenced the Human Advantage - namely the fact that humans have common sense, background knowledge, and lived experience. We're embodied and able to recognize visual situations, and we reflect metacognitively while also knowing how to interact with others in ambiguous situations. As humans, we know there is a world, and we defer to it while also constantly adjusting our mental models to map its infinite complexity.

Knowing we have this advantage, it is necessary to keep in mind that machines demonstrate "artificial" intelligence, abeit superhuman at times and able perform some of the most difficult tasks (like beating Garry Kasparov at chess), but intelligence that pertains to specific, narrow tasks, whereas what makes human intelligence "authentic" is that we can learn anything. In other words, we know how to navigate complexity.

And there's a lot that AI is missing. In How We Learn: Why Brains Learn Better Than Any Machines... For Now (2020), Neuroscientist Stanislas Dehaene lists some of these shortcomings (for now), some of which I provide here:

1. Learning abstract concepts: “[machines] actually tend to learn superficial statistical regularities in data, rather than high-level abstract concepts… human learning is not just the setting of a pattern-recognition filter, but the forming of an abstract model of the world” (28-29).
2. Data-efficient learning: “machines are data-hungry, but humans are data efficient. Learning, in our species, makes the most from the least amount of data” (30).
3. One-trial learning, or the ability to “integrate new information within an existing network of knowledge” (31).
4. Systematicity and the language of thought, or “the ability to discover the general laws that lie behind specific cases” (31).
5. Composition: "[In current neural networks,] the knowledge that they have learned remains confined in hidden, inaccessible connections, thus making it very difficult to reuse in other, more complex tasks. The ability to compose previously learned skills, that is, to recombine them in order to solve new problems, is beyond these models… In the human brain, on the other hand, learning almost always means rendering knowledge explicit, so that it can be reused, recombined, and explained to others” (33).
6. Inferring the Grammar of a Domain: “Finding the appropriate law or logical rule that accounts for all available data is the ultimate means to massively accelerate learning–and the human brain is exceedingly good at this game” (35).

What all this means is that human thinking can be made explicitly visible if we invite students to do work complex enough to be worthy of our unique, organic cognitive capacities. Based on this research, I recommend the following indicators as a way to verify and evaluate an assessment or performance task's level of complexity:

It's important to note that I am not suggesting that all of these boxes must be checked, but if an assessment incorporates at least 2 or 3 of these, then perhaps we're dealing with a complex task that requires the human touch.

To Automate or not to Automate, That is the Question:

When asking the question, why would a student choose to outsource their work to machines?, the complexity of the assignment is only one aspect of the matter.

Consider the following four necessary components for nurturing healthy engagement in learners:

1. A safe sense of belonging
2. A feeling of competence
3. A clear sense of meaning
4. A feeling of purpose

One reason a student may outsource their work is because they don't believe they can do it (competence) or they sense that others don't believe in them (belonging). I've written about the connection between competence and wellness in other contexts. Another reason, however, is that the task or assignment might lack meaning or purpose from the perspective of the learner: they don't see "the why" or the reason for doing the work and therefore have no ownership of the endeavor. That's why I recommend considering these elements when reviewing the design of an assessment, assignment, and/or project:

Notice there's overlap from the former list, and like the previous suggestions, the idea is not to check every box here, but to consider whether the learning opportunity involves 2 or 3 of these elements. If so, most likely there's a clear invitation for student ownership, for meaningful and purposeful pursuit of excellence, which will look different for every child.

The point here is not to fool-proof assessments such that artificial intelligence has no part to play. The point here is to remind us of what good assessments and good performance tasks have always looked like. In fact, in no way would I want to suggest that technology has no part to play, but how might we be intentional about its role? When working with students on action plans for completing a task or project, consider returning to the SAMR model in the following way:

When asking this question, make sure to personalize it. To know how AI can augment and modify an individual's performance, we must know the individual. An "achiever," who seeks to maximize results while optimizing efficiency, may work well with machines in very different ways than an "explorer" who seeks to search, experiment, and discover at a very different pace.

Also, remember to ask the question, what competency do we seek to nurture and measure? (I wrote about this previously in the context of writing) The answer to this question grants us guidance on knowing when to seek the assistance of AI and when not to, which requires the teacher's judgment and professional discretion.

And lastly, let kids formulate the driving questions whenever at all possible. Intelligent machines can provide brilliant answers to challenging questions, answers that sometimes surprise us in thrilling ways. What AI cannot do well, however, is ask truly novel questions that inspire us to explore the unknown. There is probably an AI system that could answer any question about the known cosmos, for instance, but no intelligent system would ever have posed the following imaginative question, a question I once heard Christian Long pose when keynoting a conference years ago: What if we built the Hubble Telescope and simply pointed it into darkness? What might happen? Humans have the curiosity and audacity to ask new, and often rediculous, questions that inspire us to create and construct knowledge and meaning; AI merely reflects back to us both the ingenuity of our inquiries as well as the biases through which we've asked them.

Resources:

1. Developing Assessment Complexity: A Tool (created by Jared Colley)
2. A Guide to Performance-Based Assessment Development (adapted by Jared Colley from Karin Hess, Rose Colby, and Daniel Joseph’s Deeper Competency-Based Learning: Making Equitable, Student-Centered, Sustainable Shifts, Corwin, 2020)
3. Artificial Intelligence + Academic Integrity - a Resource for Teachers (created by Jared Colley)

Sources:

1. Daniel Herman. “The End of High School English.” The Atlantic. The Atlantic Monthly Group, 9 December 2022. Accessed 13 February 2023. https://www.theatlantic.com/technology/archive/2022/12/openai-chatgpt-writing-high-school-english-essay/672412/.
2. Stephen Marche. “The College Essay Is Dead.” The Atlantic. The Atlantic Monthly Group, 6 December 2022. Accessed 13 February 2023. https://www.theatlantic.com/technology/archive/2022/12/chatgpt-ai-writing-college-student-essays/672371/.
3. Gerald Aungst. “Using Webb’s Depth of Knowledge to Increase Rigor.” Edutopia.org. George Lucas Educational Foundation, 4 September 2014. Accessed 2 March 2023. https://www.edutopia.org/blog/webbs-depth-knowledge-increase-rigor-gerald-aungst.
4. Kevin Kelly. The Inevitable: Understanding the Twelve Technological Forces That Will Shape Our Future. Viking Press, 2016.
5. Jeff Hawkins. A Thousand Brains: A New Theory of Intelligence. Basic Books, 2022.
6. Norman Webb. DOK Primer. WebbAlign, 2021. Accessed 30 October 2023. https://www.webbalign.org/dok-primer.
7. Stanislas Dehaene. How We Learn: Why Brains Learn Better Than Any Machine... For Now. Penguin Books, 2020.
8. Ruben R. Puentedura. “Transformation, Technology, and Education.” Strengthening Your District Through Technology Workshop, 18 August 2006. Accessed 14 March 2023. http://hippasus.com/resources/tte/.
9. Parts of this post were adapted from the following: Jared Colley. A People-Centered Organization Living in an AI World, Transformation R&D Report, Vol. 1. Ed. Dr. Brett Jacobsen, MV Ventures, Summer 2023. [Purchase and download a copy here]
   
   

Instead of asking, what can machines do better than humans? And what can humans do better than machines? What if we asked, what do humans need to learn and do to thrive in an age of intelligent technology?

What Remains Human in the Age of Intelligent Machines? Authentic vs. Artificial Intelligence

In some recent posts, I referenced Plato's Phaedrus in the context of AI to remind readers of two concerns he (and his protagonist, Socrates) had about the new form of technology known as writing:

1. Writing would "implant forgetfulness" because humans would "cease to exercise memory." Technology, in other words, would make us less reliant on exercising and honing our cognitive capacities. (more on that here)
2. And, "once a thing is put into writing, the composition, whatever it may be, [could drift] all over the place," and what if the information that's adrift is bad (or misunderstood) information? (more on that here)

Familiar concerns, if you've been listening to current conversations about AI in the age of (dis)information.

"Plato was teaching," writes David Abram, "precisely at the moment when the new technology of reading and writing was shedding its specialized 'craft' status and finally spreading, by means of the Greek curriculum, into the the culture at large" (108). Plato, more so than anyone else, recognized the profound effects that this new technology would have on human culture, on how we live, work, and learn, as well as on on our collective understanding of what it means to be human.

And Plato was right on some level: Writing changed us. Or better yet, it revealed or brought to the surface aspects of our humanity we may have not been attuned to otherwise, and AI will do the same in this century.

Teachers today, like Plato, are witnessing the spread of this new technology, both in our modern curricula and in our daily experiences, and already it is profoundly changing things. The teachers I talk to feel a similar discomfort when witnessing the uncanny powers of generative artificial intelligence, a technology that is no longer a "craft" reserved for engineers in computer science labs: we all can access and harness its power.

Much of the discussion around artificial intelligence, like writing, is just as much about redefining what it means to be human and identifying that remainder that sets us apart from these new technologies. Plato wondered what it would mean to be human if memory and information are extracted, outsourced, and disseminated by technologies that no longer rely on embodied, human cognition as the means for doing so, and similarly, teachers (and other professionals, for that matter) are wondering what remains for them - as well as what will remain for their students in an age of increasingly intelligent machines.

I'd like to make the case that there's good news ahead.

The Human Advantage

It's important to emphasize that “the question of whether AI will replace workers assumes that AI and humans have the same qualities and abilities–but, in reality, they don’t. AI-based machines are fast, more accurate, and consistently rational, but they aren’t intuitive, emotional, or culturally [responsive]” (de Cramer and Kasparov 97-98, 2021). Replacement is not the right relational concept, in other words; after all, writing did not replace our memories. Oral histories, for instance, are still one of the greatest resources for research historians.

The reality is that while machines learn to do certain tasks whose output is “more precise and faster,” human skills like “creativity, care, intuition, adaptability, and innovation are increasingly imperative to human success” (Sanders and Wood 129, 2021). What all this means is, not only is there a human remainder, there's a clear human advantage:

Rest assured, there are lots of tasks to be done by humans in the age of intelligent machines–tasks that require one to handle complex communication, to apply information strategically, to generate ideation, to read information discernibly, and to engage in complicated sensori-motor work (Brynjolfsson & McAfee 201-202, 2014). With this in mind, we should invest in this human advantage by focusing less on substituting human cognition and more on augmenting it, which can happen if we learn how to "work well with machines" - something I think Plato had doubts about.

Working Well With Machines... and People... and Nature: The Necessity of Collective Intelligence

The ability to work well with machines will be an integral competency in the age of AI, what one might “call fusion skills–those that enable [people] to work effectively at the human-machine interface” (Wilson and Daughtery 92, 2021). It is an ability to know when and how to combine the “technical acuity” of machines with human strategic thinking (Brynjolfsson & McAfee 189-190, 2014), but it is also the ability to exercise a knowledgeable understanding of what it means to be "AI literate" (something I addressed in my previous post). I think this was the real source of Plato's concern: instead of augmenting human performance and experience, writing would make us vulnerable to forgetfulness, misinformation, and misunderstanding.

But was he right? What if the capacity of these machines could potentially augment or even modify the human advantage? What if we focused less on substitution of jobs and people and directed our attention to augmenting and modifying tasks and processes? (And yes, I am reappropriating the SAMR framework here...)

From this perspective, the outcomes we achieve on our own will pale in comparison to outcomes we can achieve through augmented and modified means made possible by partnerships of collective and collaborative intelligence, partnerships that may be with machines, other humans, but also with the systems of the natural world. Like one author predicts, “In the near future there may be classes of problems so deep that they require hundreds of different species of minds to solve” (Kelly 47, 2016). When discussing the collective intelligence of human groups, one educational expert writes, “It has become clear that a group’s ‘intelligence’ does not come solely from the intelligence of its members. Indeed, one advantage of a team is that gaps in each team member’s knowledge and skill can be covered by other team members who happen to be more competent in peers’ gap areas” (Lesgold 53-54, 2019). In an era when complex problems emerge rapidly, no single expert will be able to find the solution, but experts who work well with machines and other people could potentially replace experts who avoid doing so.

Perhaps this is what sets our moment apart from Plato's experience - namely, this time we don't have a choice. For instance, one philosopher of education claims that already the complexity of the world's problems has far exceeded the capabilities of individual humans, meaning that our planet’s educational crisis is actually a crisis of competency (Stein 18, 2019). This demands us to come together collaboratively–machines, people, and things–to discover and realize some viable form of collective, planetary intelligence (Bridle, 2022).

But what are the integral competencies that will empower both educators and students to thrive in the age of intelligent machines? How do we maximize the impact of our human advantage?

Integral Competencies in the Age of Collective Intelligence

In the book Running With Robots: The American High School’s Third Century, the authors ask us to think about the shift from content fluency to content literacies, claiming “What students need from school today is not so much content fluency but, rather, omnidisciplinary content literacy, coupled with process fluency” (Toppo and Tracy 15, 2021). In a pre-AI era that prioritized specific content fluencies, workers and learners alike “needed to have deep knowledge of a narrow area. Today, deep analytical content can come from AI…[whereas humans] need to be able to synthesize information, which means collaborating across functions and working in cross-functional teams” (Sanders and Wood 130, 2021). That requires a certain level of omnidisciplinary literacy. We need to prepare learners not only to have a depth of knowledge but a range of skills and literacies that enable them to develop the following core competencies necessary for the age of collective intelligence:

1. Ability to Learn Quickly: Much like we learned when examining the move from 1st to 2nd Wave AI (from a reliance on human programming to an emphasis on machine learning) individuals who only have expertise in a very narrow area will be, at some point, under threat of replacement and obsolescence. The age of AI will not be kind to “brittle” experts and will demand, from all of us, the regenerative capacity to reinvent oneself. I've emphasized how automation will more likely take over tasks and processes, and not necessarily replace jobs and people, but there will be disruption, and many workers will be displaced and required to learn new skills in new areas. This means it’s absolutely necessary to be able “to quickly retool for new roles if current roles disappear” (Lesgold 41, 2019).
2. Ability to Facilitate Collaborative Activity: In the age of collective intelligence, the ability to connect with others, facilitate collaborative activity, and navigate group dynamics will be highly valued. When complexity emerges quickly, “the substitute for having an expert… will likely be a team of people with varying expertises who work together to solve problems that require merging their different competencies… The new model will be just-in-time assembly of a team who, working together, can address emergent problems” (Lesgold 18, 2019). This means that schools must do more than just assign “group work”: students need to build the capacity to do complex, multi-staged project-based work with teams of diverse people with varied levels of knowledge and ability.
3. Ability to Manage Complex Communication: In order to facilitate collaborative work, we need learners to master the art of complex communication in diverse environments where there are multiple perspectives with various norms and different lived experiences. This means students need significant practice communicating their ideas to people who think differently and to engage with others who possess different knowledge and different cultural identities. Computer programs do not always “read” the nonverbal cues; they don’t really “understand” jokes, sarcasm, and syntactically incorrect statements that a majority of humans can easily decipher. Good communicators and listeners will be necessary in the future.
4. Ability to Exercise Social-Emotional Awareness: Both successful collaboration and complex communication require exceptional social-emotional skills as well. “[S]everal things [have] to happen if collaborations… are to work effectively. These include ensuring that all group members have a chance to have their ideas heard, assuring that each member’s contribution is truly understood by the entire team, building on each other’s contributions, considering alternative viewpoints,... and signaling respect and value of each other” (Lesgold 20, 2019). Machines can automate analytical tasks much more rapidly and effectively but they lack the ability to replicate and perform certain social-emotional competencies (Lesgold 44, 2019). There will be a demand for workers who others enjoy being around and would want to work with, especially in the age of increased automation.
5. Ability to Evaluate and Strategically Use Information: In a world where information flows at breakneck speeds from unknown sources, much is asked of us as the executive decision-makers. It’s not enough to just evaluate the information; we need to know what to do with it, especially in the context of accelerated automation. Not only do we evaluate available information, we also use it to understand a situation, to determine whether we need more (or different) information, and to decide what to do next. These are strategic roles still to be played by humans in the age of intelligent technology (Lesgold 51, 2019). Students, for instance, will need to evaluate different sources and types of information, especially ones with conflicting perspectives. By doing so, we build resilience, capacity, and confidence in learners who understand how to manage polarities and conflict and still make actionable decisions. In this context, it's also important to emphasize again the vital importance of developing a robust form of information and AI literacy, which equally relates to the next competency as well.
6. Ability to Exercise Confidence and Sound Judgment: Closely connected to the ability to evaluate and use information is the willingness to exercise judgment and to do so confidently. This also connects to persistence because “The tasks left for humans in the age of smart machines generally are challenging. If we know clearly what to do in a situation, a smart machine can do it for us. So, productive human life in the future often will require confidence that persisting in an effort is worthwhile even if it does not immediately produce the desired outcome” (Lesgold 56, 2019). Confident judgment empowers us to courageously and willingly try to solve a problem; it demands from us grit and a growth mindset. Confident judgment also means remembering who is in the executive role and who is serving in an advisory capacity. Automation does not mean we surrender control to reckoning machines, but it takes confidence and sound judgment to know when to trust a machine versus when to say what we know to be right and true, regardless of the computational outputs (Smith xv-xix, 2019).
7. Ability to Work Well with Machines: We’ve discussed at length the importance of this vital competency, one that Plato expressed skepticism over. I believe technological literacy and competence is possible, especially in the context of these other integral competencies, each of which complements and supports the others.
8. Staying Physically and Mentally Fit: Lastly, in a technocentric world, the human body is often an afterthought. The irony, however, is that limitations in the development of artificially intelligent technology may be due to the fact that these intelligent machines lack a functioning, autonomous body. As one AI expert put it, “Thinking about the complexity and scale of the problem further, a seemingly inescapable conclusion for me is that we may also need embodiment” if we want AI to develop true intelligence (Karpathy, 2012). The body is inextricably linked to intelligence and to our minds, and this kind of total intelligence means we need to think about the total health of the human individual as well, especially in the age of intelligent machines (Potter 6, 2023). As one author claims, what is needed to be successful in this age is not new knowledge, per se, but extra brain power and cognitive bandwidth,  and that kind of “complex thinking requires the body to be delivering more energy and oxygen to the brain, and that happens more successfully in healthy people” (Lesgold 59, 2019) Exercise, in other words, boosts brain power, which the research confirms: “Studies have shown that exercise produces chemicals that make it easier for new brain cells to communicate and that is one of the few things that can stimulate new brain cell growth in humans too, particularly in areas of the cortex vital for learning, memory and mood” (de Lange, 2023).

I do not disagree with Plato that powerful technologies can produce adverse effects. Social media is a good example of where we got it really, really wrong, especially in terms of the technology's effect on developing learners. My friend and frequent collaborator, Nate Green, wrote about this in Slate.

To get it right, we don't need to ask what can humans do better? versus what can machines do better? Instead we should be asking: what do humans need to learn and do in order to thrive in an age of artificial intelligence? Part of the answer is knowing how to work well with machines (we can do it, Plato!), which involves developing AI literacy, but to develop that literacy, we have look at all the integral competencies discussed above.

In my next post, I plan to turn my attention to the question of assessment in the Age of AI to answer the question: How do we leverage the human advantage to make human thinking visible in the age of cognitive technologies?

Sources:

1. A large portion of this post was adapted from the following: Jared Colley. A People-Centered Organization Living in an AI World, Transformation R&D Report, Vol. 1. Ed. Dr. Brett Jacobsen, MV Ventures, Summer 2023. [Purchase and download a copy here]
2. Plato. Phaedrus from The Collected Dialogues of Plato. Trans. R. Hackworth (1952). Eds. Edith Hamilton and Huntington Cairns. Princeton University Press, 2009.
3. David Abram. The Spell of the Sensuous: Perception and Language in the More-Than-Human World. Vintage Books, 2017.
4. David De Cramer and Garry Kasparov. “AI Should Augment Human Intelligence, Not Replace It.” Harvard Business Review. Harvard Business Review Press, Winter 2021.
5. Nada R. Sanders and John D. Wood. “The Secret to AI Is People.” Harvard Business Review. Harvard Business Review Press, Winter 2019.
6. Melanie Mitchell. Artificial Intelligence: A Guide for Thinking Humans. Picador, 2019.
7. A. Karpathy. “The State of Computer Vision and AI: We Are Really, Really Far Away.” Andrej Karpathy blog, 22 October 2012, karpathy.github.io/2012/10/
   22/state-of-computer-vision.
8. Brian Cantwell Smith. The Promise of Artificial Intelligence: Reckoning and Judgment. The MIT Press, 2019.
9. Erik Brynjolfsson and Andrew McAfee. The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Co., 2014.
10. H. James Wilson and Paul R. Daughtery. “Collaborative Intelligence: Humans and AI Are Joining Forces.” Harvard Business Review. Harvard Business Review Press, Winter 2021.
11. Ruben R. Puentedura. “Transformation, Technology, and Education.” Strengthening Your District Through Technology Workshop, 18 August 2006. Accessed 14 March 2023. http://hippasus.com/resources/tte/.
12. Kevin Kelly. The Inevitable: Understanding the Twelve Technological Forces That Will Shape Our Future. Viking Press, 2016.
13. Alan M. Lesgold. Learning for the Age of Artificial Intelligence: Eight Education Competences. Routledge Press, 2019.
14. Zachary Stein. Education in a Time Between Worlds: Essays on the Future of Schools, Technology, and Society. Bright Alliance, 2019.
15. James Bridle. Ways of Being: Animals, Plants, Machines: The Search for Planetary Intelligence. Farrar, Straus and Giroux, 2022.
16. Greg Toppo and Jim Tracy. Running with Robots: The American High School’s Third Century. The MIT Press, 2021.
17. Catherine de Lange. "Mental Muscle," New Scientist, 3-9 June 2023.
18. Ben Potter. Total Health: The Wholeness of People in an Age of Technology, Transformatino R&D Report, Vol. 2. Ed. Jared Colley, MV Ventures, Fall 2023. [Purchase and download a copy here]
19. Nate Green. "Schools Really Messed Up With Social Media. Now, We Have a Second Chance," Slate, 26 June 2023. Accessed 25 October 2023. https://slate.com/human-interest/2023/06/mental-health-facebook-social-media-teenagers.html

How might we teach, model, and develop AI literacy in all learners, so that they do not mistake machinic outputs for wonders? How might we leverage generative AI's wondrous powers to augment student learning and performance? Are these two questions in tension with each other?

A Call for AI Literacy

"A New York federal judge on Thursday sanctioned lawyers who submitted a legal brief written by the artificial intelligence tool ChatGPT, which included citations of non-existent court opinions and fake quotes." So begins a CNBC article that was published in June of 2023. I remember hearing about the story when listening to NPR in my car last summer.

It reminded me of an On The Media episode, "It's a Machine's World," that aired back in January not long after OpenAI made its latest version of ChatGPT freely available to the public. Brooke Gladstone and her guest emphasize the point that programs like ChatGPT are “people pleasing applications,” meaning it is “good for people to keep in mind that these models are, above all, designed to sound plausible.” Large Language Models, like ChatGPT, are less concerned with learning the "truth" and therefore conveying it, and are more focused on delivering what the program predicts the user wants. This brings up deep issues of trustworthiness, especially in the context of schools, thereby reminding us of the importance of human judgment as well as the urgency to develop in students and faculty alike a robust form of AI literacy.

Otherwise, we risk mistaking mere signs (or outputs) for actual wonders, which can have real societal consequences. Just ask those lawyers from NY.

Plato Warned Us

Plato warned us about this. More specifically, he warned us of the power of technology and its ability to spread misinformation and misunderstanding. This was another one of his (or Socrates') arguments for why writing, as a technology, could be harmful (go here to read about Plato's other argument):

Anyone who leaves behind him a written manual, and likewise, anyone who takes it over from him, on the supposition that such writing will provide something reliable and permanent, must be exceedingly simple-minded...

Ouch! We definitely have been living through a moment of history where we - the exceedingly simple-minded - have been misled and manipulated by the excess of misinformation that makes up the majority eco-system of our social media platforms and websites. In other words, Plato warned us about Elon.

He continues his argument stating:

And once a thing is put into writing, the composition, whatever it may be, drifts all over the place, getting into the hands not only of those who understand it, but equally those who have no business with it. (521)

As an avid reader and lover of books, it's hard to concede that Plato was right about something here. Signs are powerful. Written words do things to the world and to those of us who live here. I think of pernicious texts like Protocols of the Elders of Zion and the destruction that that "fake text" caused. Plato had reason to express concern.

Trustworthiness, Explainability, Bias & Fair Use

Channeling Plato, we must acknowledge that AI is "getting into [all of our] hands," and it is powerful and often wondrous. Pushing back on Plato, it's also an exciting and positive game changer, no doubt, and it will transform schools, augment human performance, and change the way we teach and assess our students. And all of these are good things.

But how many of us are "those who understand it," to quote Plato? How many of us have the AI literacy necessary to avoid mistakes like those committed by the lawyers in NY?

Beyond the issue of trustworthiness, there are other challenges that demand us to develop AI literacy.

Take explainability, for instance. There is a trade off between high-performing, unpredictable Deep Neural Networks and the ability to explain their complex statistical calculations and outputs. By relying on the probability of what will likely take place, they are rarely, but inevitably, inaccurate (trustworthiness) and thereby pose an “agency risk” because their inexplicable behavior “make it even harder to attribute responsibility to a particular agent” in cases where they are mistaken (Babic, Chen, Evgeniou, & Fayard, 2021). This is one of the greatest dilemmas in the debates about responsible AI because “the abilities that [machine learning] systems are being pressed to explain may be powerful exactly because they do not arise from the use of the very concepts in terms of which their users now want their actions accounted” (Smith 66, 2019). As one expert put it, “computer scientists don’t actually know what’s going on under the hood of their systems,” and that poses challenges for us as responsible educators (Hosanager 105, 2019).

Perhaps the most alarming issue related to artificial intelligence is the replication and amplification of bias through powerful programs whose coding and data were created, collected, and curated by flawed human beings. This is not a technical problem; it’s a human one. We cannot “assume that data itself is neutral and objective… [W]hat is vital to remember is that there’s no such thing as ‘raw data.’ Whatever data we measure and retain with our sensors, as with our bodily senses, is invariably a selection from the far broader array available to us” (Greenfield 210, 2018). This is true for coding as well, and this point matters for people-centered organizations like schools. How much biased coding (writing) and data are fueling our generative AI systems and thereby amplifying our biases such that, in Plato's words, it "drifts all over the place"? This is the newest challenge of the (dis)information age, and how are we preparing students for it?

There's also the question of "fair use" versus copyrighted material that generative AI uses to bolster its wondrous power. Take image generators like DALL-E, Midjourney, and Stable Diffusion, for instance; Kate Koidan in her article on Medium explains how DALL-E claims to have been trained on licensed content as well as on publicly available material. Midjourney and Stable Diffusion, however, claim they simply performed "a big scrape of the internet" without seeking permission of the artists and creators of that original content. It's no surprise that such irresponsible use of data has now led to lawsuits. So, how are we preparing students to be informed, ethical decision makers in this kind of environment?

The Future is Here

The future is here. And it involves a new kind of technological platform, what I'm calling "Artificial Intelligence Management Systems." In addition to a traditional LMS, schools will need to consider whether they need to additionally adopt an AIMS, an exciting example being the startup, FlintK12. If you haven't checked out their platform, you should.

But don't forget that before we aim to adopt the right platform for managing student use of AI, we have to develop in collaboration with our students an AI literacy, an understanding of AI, that informs how we respond to the signs and outputs of these wondrous machines. Otherwise we will be doing a lot of unlearning in the near future, and maybe even confirming Plato's own bias, that we should "have no business with it" in first place.

Let's make sure that does not happen.

Fortunately, I do believe we can do both - namely, teach AI literacy and leverage the technology's power to augment human (and student) performance. I'm curious how others are doing it though, because this is uncharted, wondrous territory, and there are no obvious signs to lead the way.

Sources:

1. Brooke Gladstone. “It’s a Machine’s World.” On The Media Podcast. WNYC Studios, National Public Radio, 13 January 2023. Accessed 24 January 2023. https://www.wnycstudios.org/podcasts/otm/episodes/on-the-media-
   its-a-machines-world.
2. Plato. Phaedrus from The Collected Dialogues of Plato. Trans. R. Hackworth (1952). Eds. Edith Hamilton and Huntington Cairns. Princeton University Press, 2009.
3. Boris Babic, Daniel L. Chen, Theodoros Evgeniou, and Anne-Laure Fayard. “A Better Way to Onboard AI: Understand It As a Tool to Assist Rather Than Replace People.” Harvard Business Review. Harvard Business Review Press, Winter 2021.
4. Brian Cantwell Smith. The Power of Artificial Intelligence: Reckoning and Judgment. The MIT Press, 2019.
5. Kartik Hosanager. A Human’s Guide to Machine Intelligence: How Algorithms Are Shaping Our Lives and How We Can Stay in Control. Penguin Books, 2019.
6. Adam Greenfield. Radical Technologies: The Design of Everyday Life. Verso Press, 2018.
7. Kate Koidan. "Legal & ethical aspects of using DALL-E, Midjourney, & Stable Diffusion." Medium, 29 March 2023.

Are we succeeding when it comes to convincing kids of the value of human-generated writing? What happens when we reframe the conversation by acknowledging that writing is technology?

Techne, Episteme, & Plato:

As a former English teacher, this post feels a little bit like a betrayal. Like I've turned my back on my people and the noble struggle they continue to endure: namely, the art and science of teaching students how to write. Arguably one of humanity's most sacred and unique skills as a species.

In reality this piece is not an betrayal; it's an attempt to reframe the conversation in the current context of AI and Large Language Models (like ChatGPT), and to do so by revisiting one of writing's first critics: the Greek philosopher, Plato, who understood the practice on different terms than we do today.

For him the act of writing was still a novel creation, manufactured by humans and therefore something that probably felt a bit new... and unnatural. For us, however, writing has always been a part of our lived experience, a human capacity that has always already been part of our human form of life.

Like some of our students (but for different reasons, perhaps), Plato, by way of his teacher and literary protagonist Socrates, makes his ambivalence about writing well known, especially when comparing it to the skill of oratory, aided by a highly-functional, working memory.

We know this because Plato wrote down his criticisms in the Phaedrus, one of his many written recordings of Socrates' famous dialogues. He records Socrates' words as follows:

For Plato, writing was a form of technology, and one that threatened basic, human cognitive capacities like our ability to think, remember, and know the true nature of things. He offered other arguments in support of this as well (that I'll address in a later post), but the point I want to explore here are the parallels between Plato's worries about the impact of technology and our current conversation about the impact of AI.

Our history with technology over the millennia is a story of using our understanding or knowledge (what the Greek's called episteme) to identify, define, extract, and transfer the powers, capacities, skills, and artful techniques of man and nature (what Greeks called techne) in order to optimize and multiply those capacities in machinic devices and mechanisms manufactured by humans. In other words, unlike any other species, we use our knowledge (episteme) to build technologies (techne) in order to offload capacities that otherwise are limited by the abilities of our human body. And in this case, writing helps us offload the demand to commit things to memory. It expands our ability to keep a record of things, but reduces what sticks in our minds, therefore limiting our knowledge according to Plato.

This history and entanglement with technology applies to the modern workplace as well, namely a “history of people outsourcing their labor to machines - beginning with rote, physical work (like weaving) and now involving some complex cognitive work” (de Cramer and Kasparov, p.97). Think of how we describe automobiles in terms of horsepower because the machine replicated and increased the speed and strength of one of nature’s most impressive specimens. The power demonstrated by the machine in no way threatened humanity’s place in nature; if anything it confirmed our ingenuity. But AI is different, just as writing was for Plato, because these technologies attempt to migrate powers of the human mind to machines with technical and computational capacity that far exceeds ours.

Technology, from this perspective, threatens not just our technical capabilities as a human species, but our seat of supremacy when it comes to knowledge and cognition as well. In other words, episteme is losing currency in the post-knowledge economy of the information age, and if machines are performing tasks, calculations, and scores of music equally as well (and even better) than humans, and what you know no longer ensures you a valued role in our ever-changing society, how should we face this predicament from a people-centered perspective? Was Plato on to something?

Stone Tools, Chainsaws, and Why We Still Write:

Enter the year, 2022. It's November, and for the first time for many of us, we witnessed the power of Large Language Models like ChatGPT, powered by deeply layered, complex neural networks, whose workings remain a mystery to us as human observers. Like Plato, many of us felt the discomfort of the uncanny, the sense something unnatural was unfolding before us.

And with the start of a new school year, faculty are facing an unprecedented challenge in the era of Large Language Models: How do we convince students that the juice is worth the squeeze when it comes to engaging in the arduous (and at times slow) process of writing? A provocation I propose is, What if calling writing a competency complicates rather than clarifies our cause? With this in mind, what could we learn from Plato?

I had the opportunity to hear Eric Hudson speak at Woodward Academy last Tuesday here in Atlanta. His talk, "The Power and Potential of Generative AI in Schools," was fantastic, and at one point, he showcased this quote from Ethan Mollick's piece, "The Homework Apocalypse," on one of his slides:

In the age of instant generative AI, students need to know the why when we ask them to engage in difficult processes like writing. Taking seriously Plato's reminder that writing is a form of technology, the fact that students are having a hard time understanding the why becomes all the more clear: it's like asking them to use stone tools in a workshop full of freely available, state-of-the-art power towels. I'm not using this metaphor to denigrate writing, but to understand more deeply the perspective of students who "want to accomplish more than they did before."

We need to reframe the messaging because of course human-generated writing is an invaluable technical capacity that every learner needs to master, but what if we stopped calling it a competency and saw it instead as an appropriate technological medium for demonstrating certain human competencies? For instance, critical thinking is a competency; so is communicating effectively or even storytelling, perhaps. I think of self-knowledge and self-awareness as a competency too, and writing is one of the most beautiful and sacred ways to make these competencies visible, one where human artistry never ceases to surprise and inspire us. In this sense, I'm not convinced that Plato's prediction about forgetfulness proves to be that important or concerning.

Competencies and Technology in a Purposeful Context:

On that Tuesday at Woodward, I really started thinking about this idea, thanks to what Eric shared. After his session, Sarah Hanawald spoke as well and shared a graphic to help us frame a better conversation around when is it appropriate to seek assistance or perhaps even automate parts of the writing process using AI, versus when is it necessary to limit the task to human efforts alone. To spark the conversation, she handed out the following graphic from Ditch That Textbook:

When we start thinking about the why behind each writing occasion or assignment by identifying the competency at play, the two dimensional graphic starts to look a little more three dimensional. For instance, if it's about communicating information efficiently, there may be a lot of appropriate use cases for automating parts of the process. If it's about engaging in critical, reflective thinking, perhaps relying less on AI is necessary, at least at first.

If we want to succeed in getting kids to see writing less as a stone tool and more as an invaluable technique for expressing one's competencies and cognitive capacities, then we have to do more than say that writing is important in and of itself (even if we know that to be true due to our greater lived experience). Because if it's just writing for the sake of writing, they will work smart when they might need to be working hard, and that's a problematic outcome that can best be summed up by finishing Plato's written statement cited at the beginning of this article:

AI will tell our students lots of stuff (whether what it tells us is true is for my next post), but it will not teach them like a human mentor can and should. This means students need to write because it's also an opportunity for us as teachers to connect with them in those invaluable teachable moments. It's a way to cultivate wisdom and build their cognitive capacity. We just need to be more explicit about why we're practicing this timeless technique as well as how it connects to their lived experience. But keep in mind, AI is forever part of their lived experience moving forward, so we must coach them on how to use AI responsibly and in ways that augment instead of substitute for their performance as writers.

Sources:

1. Plato. Phaedrus from The Collected Dialogues of Plato. Trans. R. Hackworth (1952). Eds. Edith Hamilton and Huntington Cairns. Princeton University Press, 2009.
2. F. I. G. Rawlins. “Episteme and Techne.” Philosophy and Phenomenological Research, Vol. 10, No. 3 (Mar. 1950), pp. 389-397.
3. David de Cremer and Garry Kasparov. “AI Should Augment Human Intelligence, Not Replace It.” Harvard Business Review. Harvard Business Review Press, Winter 2021.

Have you ever been given a deadline for a task for which you were not qualified to complete? Perhaps you thought you were capable, at first, only to discover later that you were not equipped to succeed or see it through. Or you just knew it was lousy work but it was the best you could muster.

What did that feel like, knowing a deadline was right around a corner? A surge of anxiety pours over, perhaps, followed by unexpected pangs of panic and paralysis? Eventually, the panic gives way to a sense of helplessness, leading to an eventual feeling of complete powerlessness. A sense that all agency and efficacy have been stripped away. It’s one of the worst feelings ever. And it rarely rears its head without its accomplice, namely desperation.

And we wonder why students cheat (but more on that later).

Much has been written about the student mental health crisis we’re experiencing at this post-pandemic moment. Much has been written about the importance of meaning, purpose, and belonging when addressing this crisis, but not as much has been written about a fourth factor, namely the role of competence, especially in the context of time-driven approaches to schooling which involve deadlines, ends of terms, and a varied distribution of levels of student achievement. Competence is an integral part of wellness, but there’s tension when time remains the driver for moving students along.

Two important truths are at play here:

1. Different learners master the same skills in varied amounts of time, meaning time varies, from learner to learner, if we want mastery to be the constant.
2. Meeting deadlines is an important work habit that contributes to our success in life, and sometimes in life, the amount of time we’re given for a task is a non-negotiable constant.
   

With these truths in mind, the question becomes which of the following real-world examples gives us a better understanding of how to handle deadlines as well as how not to when it comes to the calculation of grades and the measurement of learning:

Example One: I have two daughters, one is 5 years old and the other is 7. They couldn’t be more different when it comes to their personality, interests, as well as how and when they learn certain things. My first daughter learned to walk at a very different moment in her development when compared to my youngest daughter. Of course, there was no deadline for that milestone, and even though they learned at different times, they are both very proficient walkers, movers, and shakers. Mastery was the constant , but the timing varied considerably in those early years.

Example Two: I entered the workforce shortly after graduate school, starting my career as a young English and history instructor in Brooklyn, NY. After writing a letter of interest, gathering recommendation letters from respected professionals, and engaging in a rigorous day-long interview process, my would-be supervisor hired me with the expectation and assessment that I had a proficient understanding of my content area as well as how to engage students in that material. Shortly after, I got my first deadline: I had to prep my courses by building units, gathering materials, and designing assessments, which needed to be ready to go come August 1st of that summer. It was a lot of work, and although I lacked the experience I have now, I was competent enough to complete the task. I’ve continued to have deadlines as my responsibilities have expanded over the years, but that increase in demand was preceded by a growth in competence.

The point here is that my “growth” as a professional had no deadline. It could have taken me 5 years, 10 years, or 20 years to get to where I am. That, of course, depends on so many factors – factors that are not the focus of this post. However, when I became more competent, I moved on to new responsibilities in my career, which is exactly the point with a competency-focused approach to education, which makes me wonder about how we handle deadlines when it's really about "growth."

In Example One, the “primary user” if you will is a learner who lacks competence at what they’ve been asked to master, and the bottom line, in this situation, is prioritizing learning and growth. In Example Two, the “primary user” is a competent employee whose skill proficiency has already been assessed to some extent, and the bottom line, in this scenario, is the mission of the company, which in most cases involves maximizing profits and strengthening one’s presence in a marketplace. So which of these analogies serves as a more accurate comparison when considering the healthiest way to address work habits in a K12 environment?

If grades are meant to measure and document the growth of learning, to chart a student’s journey towards mastery, should we penalize missed deadlines by weaponizing our instrument for measuring learning? Are grade penalties the way to handle this? Is that reflective of real life when we consider the user is a novice and the bottom line is about growth?

My first daughter walked at 11 months, but my youngest daughter didn’t master it until she was 12 months old. Should I have stopped my 2nd daughter at 11 months, or made it harder for her to get there because of a lack of timeliness? Of course not, that would be absurd.

The real point I’d like to get at is this: when we advocate for addressing missed deadlines through means other than grade deductions, many skeptics argue that we are not preparing students for the real world – a world where there’s consequences for missing your deadlines. My provocation is that giving kids deadlines when they are not proficient practitioners is not an accurate simulation of actual real world deadlines. I would go so far as to say that, in really rigid environments, it could have the opposite effect of driving kids to adopt unhealthy coping strategies for dodging responsibility in the future because too often in their past they were given deadlines in developmental moments when they were not equipped to competently complete the task. This gets back to the mental health crisis mentioned earlier, and the question of preparation versus impairment.

Let me be clear: I’m not advocating for no deadlines. I’m not suggesting we don’t hold students capable through intervention, coaching, and relational partnerships. We just can’t do that by weaponizing grades and by pretending that every student learns to walk at the same time. And when they cheat, go back to the first two paragraphs of this post, and read slowly before reacting. Kids want to do the right thing; they want to learn, but desperate circumstances unfortunately provoke desperate actions, or in the case of missing deadlines, disengagement when the crisis of competency becomes too overwhelming.

Co-authored by Jared Colley + ChatGPT

Anthony Brandt and David Eagleman in their book, Runaway Species: How Human Creativity Remakes the World, offer a three part framework for understanding how novel things are created. Using the three cognitive maneuvers – Breaking, Blending, and Bending – humans continue to produce creative artifacts, solutions, and products, not out of nothing (like an all-powerful deity), but by reconfiguring found materials, tools, and objects into new arrangements and values.

What happens when a machine breaks, blends, and bends an age-old academic performance task, like composing essays? What happens when intelligent machines, OpenAI’s ChatGPT, start to deterritorialize the landscape of writing into one where humans are writing-with-machines? As we confront this conundrum, consider Kevin Kelly’s framing of our relationship with machines in the age of AI: “Everyone will have access to a personal robot, but simply owning one will not guarantee success. Rather, success will go to those who best optimize the process of working with bots and machines" (The Inevitable 58–59). Later he writes, “This is not a race against machines. If we race against them, we lose. This is a race with the machines. You’ll be paid in the future based on how well you work with robots” (60). I want to emphasize this claim: One of the valued human competencies in the age of machines will come down to “how well we work with machines.”

There are already examples of concerted efforts to “work against” ChatGPT, and many have written about ChatGPT from a rightfully concerned perspective – such as how it will break the college essay or the high school English class – the kind of novel outcomes where breaking could undoubtedly have negative consequences for society as a whole.

And I do share these concerns: I think of the anecdote from Melanie Mitchell’s Artificial Intelligence: A Guide for Thinking Humans when she and the famous philosopher Richard Hofstadter met with Google’s AI research team. Hofstadter, to the surprise of Google’s AI team, expressed his sense of terror when seeing what Google was trying to accomplish and the speed at which they were trying to get there. “Hofstadter’s terror… was not about AI becoming too smart, too invasive, too malicious, or even too useful. Instead, he was terrified that intelligence, creativity, emotions, and maybe even consciousness itself would itself be too easy to produce–that what he valued most in humanity would end up being nothing more than a ‘bag of tricks,’ that a superficial set of brute-force algorithms could explain the human spirit” (11). I agree. To echo a point made by friend and colleague, Jeannette Lee-Parikh, the value of teaching writing is not, from a competency-based perspective, only about communicating and conveying information effectively. The deeper value has to do with how it slows us down in a fast-brain world to do slow-brain, deep reflective thinking. I think Hofstadter worries about certain technological shortcuts short-circuiting profoundly important human activities that are integral to healthy development of the self. Totally agree.

But just because there is a danger that machines could “work for us” in a way that’s damaging to the human spirit does not mean we must necessarily “work against” it as educators. I'd like to explore, with co-authorial assistance of ChatGPT, ways we might “work with” AI tools to break, blend, and bend conventional human activities, skills, and tasks in ways that are positively valuable and exciting. That's why I asked for ChatGPT's assistance:

BREAKING
Yes, ChatGPT may have broken some elements of the traditional essay-writing process that's geniune cause for concern, but it’s also a tool that can help us break writer’s and researcher’s block by aiding in the brainstorming process, by helping a student get started with organizing ideas and producing a basic body for revision, re-write, and improvement. In other words, ChatGPT potentially can serve as a catalyst to creative thinking.

BLENDING
When examining ChatGPT, one could make the case that one novel feature of the tool is how powerfully it blends a personalized assistant (like Alexa) with a wealth of synthesized information (like Wikipedia), with the added ability to continue to learn. Again, what a powerful tool for brainstorming, planning, and learning! A student the other day was working on a project where they are designing a pop-up restaurant for students and faculty at school. The student was chatting with the OpenAI system to gather ideas for their restaurant concept and design. In other words, ChatGPT potentially can serve as a catalyst to strategic thinking.

BENDING
ChatGPT claims that AI could help “improve the overall quality and coherence of [students’] writing” and “that it could enhance their writing style.” Perhaps one way to do this is to bend the concept of authorship itself.

Descartes once famously made the argument that a single architect designing a building would produce a better product because the totality of the design would be more coherent, whereas a scenario where multiple architects worked on a single design would be one where the product is less uniform and therefore less favorable. I think almost every design firm in the 21st century would disagree with this argument — at least in its simplest form. We know that collaborative design teams produce superior results, and the same could be said about writing. Perhaps we’re moving even further away from Descartes’ dream of intellectual and creative individualism; perhaps writing-with-machines is a natural next step in this collaborative unfolding.

What if students were asked to bring a piece of AI-generated writing to class with the task of making choices on how to improve the written artifact with the expectation that they explain the reasoning of their choices? In other words, ChatGPT potentially can serve as a catalyst to critical, evaluative thinking.

As a former English teacher, towards the end of my tenure I became more and more uncomfortable with the age-old model of reading a common text, engaging in discussions, direct instruction, and short writing, and finally writing a cumulative essay for a final score. Too often, I lacked confidence when evaluating the level of cognition expressed by the student. Was it simply recall – namely aping back all the things said in class - and if writing were just about communicating information, perhaps that’s ok? However, I wanted writing to serve as the performance task that sparks, inspires, and develops deeper levels of cognitive expression. Working with machines and having kids evaluate and make choices to improve the quality of a draft while also explaining their reasoning could be a way of assessing this more precisely.

If I am correct that “how well we work with machines” will serve as a crucial competency for life success in the age of machines, then we risk missing a huge opportunity by being the schools that banned calculators instead of integrating them into authentic learning environments. This is our calculator moment in Humanities, and how we frame this and respond to it will have decades-long ramifications.

Jared's Sources:

• Brandt, Anthony and David Eagleman. Runaway Species: How Human Creativity Remakes the World. Catapult, 2018.
• Descartes, Rene. Discourse on Method and Meditations on First Philosophy. Hackett Publishing Company, 1999.
• Kelly, Kevin. The Inevitable: Understanding the Twelve Technological Forces That Will Shape Our Future. Penguin Books, 2017.
• Lee-Parikh, Jeannette. "Why A 3300 BC Invention Still Matters in the 4th Industrial Revolution: Writing As A Complex & Foundational Competency." OESIS Boston Conference, 22 October 2021.
• Mitchell, Melanie. Artificial Intelligence: A Guide for Thinking Humans. Picador, 2019.
  

I started to write the newsletter about grades and grading. It was the end of the semester, and I thought it might be a time to simply offer up some encouragement about how to free yourself from the burden of producing grades for your classes.

Then, May 24th happened.

I really don't have words to describe how I felt on that day. A couple of weeks later, and we're now getting congressional testimony from survivors and pediatricians and others in the Uvalde community.

It's all too much, to be honest.

As the United States navigates another spate of mass shootings, as we once again mourn the lack of safety in our schools, as we hear the calls for gun reform and the accompanying cries of "don't politicize the second amendment" interjected between "thoughts and prayers," I can't bring myself to publish that piece on grades and grading.

It'll just have to wait for another time.

This Month on ROOTED

Back in May, Jared wrote an excellent, though-provoking piece on mindsets:

Deep Belief, Shallow Mindsets, and Surface Behavior – An Instructional Coach Ponders the Usefulness of Talking about Mindsets

It’s afternoon, the end of the school day, and faculty are getting ready for a weekly workshop while students scurry out the building. The session that day focused on un-grading—specifically as it relates to formative assessments for learning. The behavior or practice being discussed was whether or…

ROOTEDJared Colley

From Around the Web

I'm going to suspend our typical "around the web" segment for this issue so that we can focus on what to do for the people of Uvalde. Here are some helpful resources:

• GoFundMe has a hub set up for Uvalde. This is one of the more heartbreaking things to see.
• LULAC has also created a fund.

In addition, you might consider listening to this episode of This American Life:

The Parents Step In - This American Life

The government isn’t doing much to prevent school shootings. So parents are jumping in — and getting results.

This American Life

The episode looks at parents impacted by mass shootings — Aurora and Sandy Hook — and gives insight into the fallout from these shootings. Yes, there's heartache and grief and all of the things you'd expect. But the consequences are so much greater and so much more frustrating than you might imagine.

About

ROOTED Monthly is a fresh collection of posts, links, and ideas about teaching. Please feel free to forward this newsletter or ask friends to subscribe. We'd love to reach a larger audience, especially if it will be helpful to any educators out there. Lastly, we would love to feature your voice on ROOTED. If you have an idea for an article, please go to our submissions page and get in touch with us.

It’s afternoon, the end of the school day, and faculty are getting ready for a weekly workshop while students scurry out the building. The session that day focused on un-grading—specifically as it relates to formative assessments for learning. The behavior or practice being discussed was whether or not to grade formative assessments and count them towards a student’s score or final grade in a class. Everyone seemed to be on board with the idea that, like in sports, formative assessments are an opportunity for practice, meaning there’s a reason the scoreboard isn’t turned on at that moment. With lots of heads nodding, it all seems to make sense until one courageous, skeptical participant speaks up: I don’t believe that students will take the work seriously if it’s not for a grade. There’s a reason we’ve never done it this way: It will never work. I can’t motivate a student without grades.

Of course, I had a lot of questions for the vocal participant, but more on that later.

As an Instructional Coach, how should one make sense of the interaction—a moment where it feels like someone just threw cold water on a warm idea that was gaining steam? The temptation in a moment like this is to chalk up the person’s resistance to the fact that they don’t have the right mindset: You need to try to adopt more of a Growth Mindset! Right now, you’re too fixated on why something won’t work and why you can’t do it.

But what is a mindset? Or is that even the right question to ask? Perhaps it’s not a “thing” at all. Perhaps we need to examine not what it is, but how we use the word because the language we employ when discussing growth, coaching others, and evaluating behaviors is so important.

Instructional Coaches often speak about a “Fixed Mindset” versus a “Growth Mindset”—the difference between those who believe certain behaviors can lead to improved performance, capacity, and intellect and those who do not. The intent when talking mindsets is good: we’re basically trying to encourage someone, to tell them you can do this; your actions can motivate kids through other means! You can improve your practice in this way! But I worry about the impact sometimes. I wonder if labeling it so simply (namely this mindset versus that mindset) might mask the challenging realities we face when serving on collective teams that are focused on improvement.

It’s important for me to say that my wonder is less about Carol Dweck’s thoroughly researched concept^[1] and more about how we use the term too simplistically to explain away our difficulties in coaching someone towards growth and improved practice. I worry that our language limits our view to seeing only internal causes and explanations (more on this later).

Coaching colleagues is a complex and challenging endeavor, but one way to make it feel like a simpler scenario is to label the person or the challenge: You’re either fixed or growth-oriented. But binary labels cannot capture complexity, meaning we risk effacing an individual’s sense of agency, identity, and dignity when we characterize “a person’s performance (or behavior) to be internal (caused by the person) and to be stable (consistent across situations)” — which is also an accurate description of what social psychologists call “attribution bias.”^[2] It is our tendency to blame the person (internal attribution) instead of investigating any external conditions that might reveal a person’s performance or behavior not to be consistent across situations (meaning the attribution is not stable). We do this on our commutes to work when someone cuts us off and we conclude, Well that person is a jerk! Stop and ask: Could there be other factors? Was the driver rushing their child to the emergency room, for instance? In other words, perhaps there is something specific to the context that caused the behavior (Mark D. Cannon and Robert Witherspoon, 2005).

In other words, it’s unhelpful (I think) to attribute these labels to the whole person: Again, what about the situational context or external factors at play? Can one have a fixed mindset in one environment, but behave differently in a situation where certain conditions have changed? Could there be a scenario where some external factor prevents me from believing my actions will have an impact? What about predicaments where one doesn’t think they have the option to act in ways they believe will have an impact on growth and improvement?

I suggest we interrogate this further by looking through the lens of the Three Levels of Culture:^[3]

1. Deep Culture (Roots) = Our Beliefs
2. Shallow Culture (Trunk) = Our Dispositions, Ways of thinking, Norms
3. Surface Culture (the Foliage/Leaves/Flowers | What’s really visible) = Our observable behaviors

Following this model, our mindset—whether fixed or growth-oriented—is something (according to how we use the word) that lives and evolves in the shallow region of our cultural self—just below the surface. Technically, it’s not completely observable. With this framework in mind, consider the concept of self-efficacy, which according to Albert Bandura “refers to BELIEFS in one’s capabilities to organize and execute the courses of action (i.e. BEHAVIORS) required to produce given attainments” (18).^[4] Also, think about the definition of collective teacher efficacy: “the collective self-perception (BELIEF) that teachers in a given school make an educational difference (their BEHAVIOR has impact) to their students over and above the educational impact of their homes and communities” (14).^[5] Something to keep in mind for later: disagreements about deeper cultural beliefs have a high emotional impact on those who disagree on such matters, whereas differences in surface behavior have a much lower emotional impact. It’s easier to discuss disagreements over grading policy than debating differences of deep belief about the purpose of education, for instance, which also means it’s safer to encourage someone to try changing a certain behavior than attempting to challenge that person’s beliefs.

Thinking about all this, what’s going on when a colleague seems to exhibit a “fixed mindset” in a moment when the community is pushing for collective, efficacious transformation in practice? Take changing how we grade so students learn more deeply, for example. In the scenario at the faculty workshop, perhaps that person lacks a sense of self-efficacy. Perhaps there’s a disconnect between deep belief and surface behavior (whether that behavior is a natural occurrence or coerced)? Perhaps we’re looking at the wrong level of culture (in this case mindsets, i.e. shallow culture) when trying to locate the thing that’s obstructing transformative opportunities from happening. Perhaps the language game of mindsets is misleading in that it simplifies something that’s harder to name with clean labels—namely our fluid, ever-evolving ways of being which are always context-specific.

My provocation: We cannot physically observe mindsets, yet that’s how simple labels (like “fixed” or “agile”) function: labels proclaim to see that which cannot be seen in its full complexity - namely, the mind of another (or an Other).

Instead, we should observe and take in, without judgment, a person’s behavior and stated claims (surface culture), and in the spirit of curiosity, seek understanding about that person’s beliefs. Instead of trying to FIX a person’s mindset or before correcting certain behavior, how might we uncover the disconnect between a person’s professed or withheld Beliefs (deep culture) and the collectively desired Behaviors of the team (surface culture)? Is there a misalignment of belief between the person and the team? Is there a misunderstanding in terms of which behaviors match and reinforce one’s professed beliefs? Are a person’s behaviors and professed beliefs merely performative because they feel there’s no other option than faithless compliance?

In terms of a disconnect between belief and behavior, we’re really dealing with the problem of self-efficacy – something that can only be realized when our behaviors are truly reinforcing and in exponential cooperation with our beliefs (and vice versa).

“The key,” writes Zaretta Hammond, “lies in focusing on deep culture. Rather than focus on the visible ‘fruits’ of culture… we have to focus on the roots of culture: worldview, core beliefs, and group values” (24-25). But how are we going to honor that process of uncovering a person’s deep beliefs? Because it’s not a matter of identifying a person’s beliefs to point out that they’re wrong (that would have a high emotional impact; besides, it’s no one’s business to change another’s beliefs. That journey is personal); instead, it’s a matter of responsive coaching that seeks to understand the disconnect between a person’s beliefs and an institution’s or team’s agreed-upon behaviors and action steps. What are ways to build bridges between beliefs and behavior that honors everyone’s identity and ways of making sense of the world?

If we’re looking for shallow explanations for the challenges we face as Instructional Coaches then stick to the language of mindsets-as-labels (which was not Dweck’s original intent). However, if we want to inspire deep transformation through the work we do with others then let’s find ways to bridge gaps between beliefs and behaviors such that everyone genuinely feels a deep sense of self-efficacy and belonging. That means we’re not in the business of changing someone’s culturally situated beliefs; instead, we’re in the business of finding common understanding and mutually identified connections to bridge gaps that establish collective efficacy.

To illustrate, let’s return to our opening example. A team, department, or PLC has made the behavioral commitment not to grade formative assessments: Only summative performances will count towards one’s academic score (we’re talking about surface culture behavior). However, a skeptical teammate pushes back because they don’t believe that students will take the work seriously if it’s not being scored with a recordable number/letter grade. The much-needed skeptic in the room doesn’t believe in the efficacy of the team’s agreed-upon practice (a matter of deeper beliefs).

Why the resistance? Perhaps, in prior experiences, that person succeeded in an educational environment that puts all the emphasis on grades? Perhaps that contributed to a personal feeling of significance and success in one’s previous cultural experience (a feeling they wish for their students as well)? Perhaps there are other factors in place that contribute to their skepticism, such as an administration that is predictably unsupportive when students do go off the rails and stop submitting evidence of their formative learning experiences.

Instead of debating the validity of one’s belief versus another’s (which could produce a high emotional impact), I suggest we learn more about that person’s beliefs; try to discover what shared beliefs you have in common. Perhaps both of you believe that learning only improves when students feel free to practice something multiple times—that failing forward is a productive and necessary part of everyone’s life experience (a shared deep belief). Perhaps in partnership, this is the bridge that needs to be built to connect a person’s beliefs to the behaviors a team has agreed upon as a collective commitment. Now, we’ve established grounds for cultivating self-efficacy: Based on our shared belief about failing forward, let’s explore this practice of not grading formatives. Let’s ground our reasons for trying this on the foundation of this shared belief and see how it goes?^[6] And now, we’re on the path to leveraging the most powerful influencer for maximizing student learning—namely collective teacher efficacy (in this case, as it relates to how we collectively grade and assess student work).^[7]

With this approach, everyone’s beliefs remain respected, and the bridges between belief and behavior are built to honor the fact that there are a variety of routes for forging these connections between depth and surface. Will the skeptical teacher be fully convinced of the efficacy of ungrading formative assessments? Probably not completely; but hopefully they’re a little more open to testing a new practice (a demand that has a much lower emotional impact) in a way that’s grounded in something they already believe and value (which avoids harmful high emotional impact).

As my mentor, Bo Adams, once stated: “Sometimes, in order to change one’s beliefs, we first have to change our behavior.” Maybe the team’s collective impact on improving student learning actually will transform that person’s belief about what motivates students to get the work done. But that’s neither here nor there because, as stated earlier, it’s nobody’s business to do so. It is our business, however, to be curious about each other, to seek understanding, to be vigilantly attuned to how we personalize the bridge-building between beliefs and behavior, and to do so for purposes of impacting student learning.

To sum it up, how might we bridge the gap or help discover the connection between belief and behavior for those we support, coach, and work with, such that self-efficacy (and ideally collective efficacy) can become an actualized practice that maximizes impact? It starts with establishing trust by genuinely seeking understanding in culturally responsive and identity-responsive ways. As Peter Dewitt suggests in Collective Leader Efficacy, “instead of seeing a colleague as resistant, try to understand that your colleague may lack self-efficacy in that area” (41). Let’s personalize our conversations around growth, not by relying on binary constructs (like fixed vs. growth mindsets), but by seeking understanding about why someone might feel a lack of efficacy when it comes to a specific area of improvement or practice.

As a final side note, I think what Dweck means by a Growth Mindset is simply someone who is experiencing high levels of self-efficacy. With that said, I hope the final takeaway from this reflection is that there is more to interrogate: There are always external factors preventing efficacy in the workplace, as well as sociopolitical realities that, if not considered carefully, risk missing opportunities to spark growth due to our own blinders caused by attribution bias. The good news is we have influence over external factors and systems, but we have to be aware of them, which means moving away from the language of labels and the trappings of attribution bias.

Labels beget biases, whereas looking at each person as uniquely complex with deep cultural roots forces us to do the harder work. It forces us to ask deeper questions about our roles as instructional coaches.

Ah, the spring air is warming, leaves are popping, flowers budding. It must be time to think about standardized testing season! As a teacher who must dip his toe into the Advanced Placement curriculum, I know exactly how fractious and fraught conversations about standardized testing can be.

Somehow, as I started to gear up for my own "AP Bootcamp" — a period of time where I whip my little AP English Language & Composition students into shape — John Warner's post on Campbell's Law came up on my reading list:

Campbell’s Law: Something Every Educator Should Know

As soon as I learned Campbell’s Law, how I conceived my teaching changed.

Educational EndeavorsJohn Warner

If you aren't familiar with Warner, well, I wish you were! I highlighted a different post by Warner back in the February newsletter. While that post was about Wordle and how it exemplifies good pedagogy, this post on Campbell's Law gets to the heart of what so many educators rebel against: the idea that standardized tests don't really measure the things we want them to measure.

Human beings are amazing at learning skills in specific contexts. Sometimes, those skills bleed over to other tasks. For example, when I teach you how to write a persuasive essay, I'm hoping that the skills you learn there will translate into other areas of critical thought. Often, however, that bleed is minimal. In the case of a standardized test like the SAT, the skill that most students — at least the ones privileged to have the time and money to spend on test preparation — learn is what I would call "The SAT Test-Taking Skill."

To what degree do these skills transfer? ::Shrug::

This is why we must continue to call for authentic assessments. When I teach students to write persuasive essays, for example, I often have them start by writing an argument they care about. One prompt goes like this:

Write an email to the Dean of Students in which you argue for a change to a school policy. For example, you might want some piece of the dress code amended. Use Cicero's six-part oration to outline your ideas, but you don't need to stick to that in your writing.

When I'm at a school where the Dean of Students is game for this sort of thing, I have students actually send the email or do some kind of presentation to make their case. This is an authentic assessment because students are building arguments for something they care about and they have an actual audience for their writing. They target the argument for that person in that office.

If you're subscribing to this newsletter, then you're likely familiar with the idea of "authentic assessment," and you're likely on board with their value over standardized tests. But, just in case, here are the criteria (as summarized by Indiana University) Grant Wiggins lined out for authentic assessments back in 1998's Educative Assessment.

The question, really, is how to gear our assessments so that they accomplish both. How do we create authentic assessments that also set students up for success on these tests?

Your ideas are welcome...

This Month on ROOTED

Avoid Busy-ness

Activities that are meaningful don’t keep us busy. They require time. Deep teaching asks us to value quality over quantity, to understand that less is more.

ROOTEDStephen Hebert

From Around the Web

Ed School panel examines U.S. teacher exodus

Experts at HGSE webinar say districts, schools could offer educators more support to slow departure of teachers.

Harvard GazetteLiz Mineo Harvard Staff Writer DateMarch 15, 2022March 16, 2022

A Cry from the Heart of all Independent Schools & An Elite School Meltdown | Joel Backon - Intrepid ED News

The heartwrenching story at Choate, unfolding at this very moment, is more than an elite school meltdown. It is an emotional cry from the heart of students,

Intrepid ED NewsJoel Backon

Some school systems pause diversity programs amid pushback

Conservative takeovers of local school boards have already altered lessons on race and social injustice in many classrooms .

Associated PressCAROLYN THOMPSON and HEATHER HOLLINGSWORTH

As Book Bans Spread, Suburban Moms Who Oppose Them Are Fighting Back

These mothers have deployed a series of unique strategies to convince board members to keep books about racism and homophobia.

Education WeekEesha Pendharkar

Is silence a good thing?

My students fear it, and I love it. My conversation with Arianna Vailas helped me to see that silence and community go hand in hand.

Talking with TeachersKatherine Burd

About

ROOTED Monthly is a fresh collection of posts, links, and ideas about teaching. Please feel free to forward this newsletter or ask friends to subscribe. We'd love to reach a larger audience, especially if it will be helpful to any educators out there. Lastly, we would love to feature your voice on ROOTED. If you have an idea for an article, please go to our submissions page and get in touch with us.

Sometimes, my desk fills up with clutter: stacks of paper, typically 30–40 sheets deep, that have been arranged on top of one another in a criss-cross fashion so that they remain distinct. This represents the “work” of my students: a given assignment for a given class period, piled up and waiting to be pushed through whatever algorithm I create in order to “evaluate” it before pushing it back out to them the next time I see them (or, as is often the case with me, when I happen to remember to clean my desk…I’m terrible about returning this stuff).

The use of scare quotes in the above paragraph for words like “work” and “evaluate” is absolutely intentional. I seriously question whether or not what the students have done on these stacks of paper is “work.” Moreover, because it may not be work, then it probably doesn’t have any real value, so how can I “evaluate” it? (Unless, of course, I want to put a zero on it and then deal with the student angst that ensues!)

What have I really done?

Well, I kept us all busy. Keeping students busy means keeping me busy. Busy-ness feels like productivity, but it’s really not. Busy-ness fools me into thinking that things are happening, that I’m getting things done, that I’m changing the world!

I’m not.

Keeping students busy is not valuable. Filling up their time with activities does not provide them the opportunity to dig into a good, meaningful activity, and to tackle it in a thoughtful way. Instead, the busy student works to get things done and puts very little thought into what she is doing or why she is doing it. The work of the day becomes a checklist, a bunch of to-do’s that become to-did’s added to a long list of to-already-done’s from all of the student’s other classes and activities.

(You can go back and read that paragraph again, but replace the word “student” with “teacher.”)

Done day-after-day, year-after-year, keeping students busy has two major negative impacts:

1. The busy student becomes a follower of recipes. Rather than thinking deeply about how to solve a problem, how to make it through the maze, the busy student looks for the recipe that is appropriate for the occasion. When there’s not a recipe to follow, when creativity is required, look out! Things will get hairy!
2. The busy teacher—and I’m speaking about myself here—becomes harried. The number of activities and assignments piles up, and it becomes difficult to keep track of who’s doing what and where they are in the system. The stack grows and grows, and before too long, a giant desk-cleaning is needed in order to right the ship!

If you feel like your class is becoming a checklist, you might want to think about how you’ve organized things. Any project, any assignment, of course, has its steps, but these steps shouldn’t be focused on the speed with which they are completed, nor should they always be prescribed or even mandated.

Activities that are meaningful don’t keep us busy. They require time, attention, and thought. Deep teaching requires us to value quality over quantity, to think of the school year in terms of less is more.

Activities that are meaningful produce value for the persons involved. In addition to a less is more approach, we might also consider the idea that anything worth doing is worth doing well. If it’s worth doing, then it will have value. Sometimes that value is very apparent—building something in a shop class, for example—but at other times, the value is a bit more nebulous: what did I get out of explicating that poem?

Every morning, I journal. It takes a long time: typically 40–60 minutes for me to get my three or more handwritten pages in. (I’ve set three as a minimum thanks to Julia Cameron’s practice of “Morning Pages.”) This is a meaningful activity; it takes me a while. But if something is worth doing, then it’s worth taking the time to do it, right?

My journaling has a positive impact on my life; it forces me to put the present day into perspective, to process the day before, to come up with writing ideas or teaching ideas for the days to come. I love it, but I also find it difficult and time-consuming sometimes. Many mornings, for example, I want to hit SNOOZE and get another hour of sleep. But, since I know that writing out my morning pages leads to a better day, I do it. I’ve been doing it for three years now, and I can’t think of a time when I finished those pages and said, “Well, that was a waste!”

This journaling process is slow.
It’s intentional.
It’s reflective.
It’s the opposite of busy.

This is what any good class assignment should be moving toward. When designing an assignment, ask yourself these questions:

• Does the assignment promote a focus on a process that includes reflection? → Have I given my students enough time to do it and to think about what they’ve done? ←
• Is the assignment geared toward having an impact on the way the student understands her world?→ Have I given my students the opportunity to understand something new and to express the (potential) impact of that understanding? ←
• Am I giving this assignment because I want to feel effective?→ Am I assigning this for their betterment, or is it really about my need to feel like I’m doing something? ←

That last question is key. We don’t create curriculum for ourselves. We really can’t. We mustn’t. Stop, please! I do not want to give assignments so that I feel good or so that I can check a box.¹ We don’t want to put the student through a course of study that is geared toward making us feel good.

Sometimes, we get antsy when students are sitting in our classroom and staring into space. We feel guilty, like they aren’t getting any learning done. But this is not true. Sometimes, good thinking requires us to stare into space! Sometimes preparing for good thinking requires us to process whatever happened before—that D- on the AP Bio project, perhaps—by staring into space for a bit.

I see this in class discussions all the time. Someone asks a deep question, a question that really probes, that plumbs the depths of the human soul! The room goes silent. The hair stands up on the back of my neck. The silence grows louder. Things get awkward. The quiet draws on. A sense of dread starts to fill my chest. Will no one be brave and bold and speak to this question? After what feels like a long pause (but is probably only 30 seconds), I jump in to save the conversation.²

What a waste! I just ruined it! I killed their opportunity to think so that I’d feel more comfortable because I sometimes mistake “vocal” for “productive” or “verbal” for “thinking.”

After class discussions, I usually reserve 5–10 minutes for the class to unpack the conversation: discussion about discussion. It’s super meta, but it’s also super important. We have to think about what we’ve just done, what we’ve just experienced. We have to reflect:

• How did we do?
• Where did the conversation go off the rails?
• Where was it awesome?

Invariably, if an awkward silence like the one described above has happened, students will say, “We weren’t talking the whole time.” They perceive this as a negative. But when we unpack that, when I ask them what they were doing during that silence, someone will chime in and say that they were thinking about the question.

Thinking about the question is a big win, y’all! Celebrate it!

If you’re having a shallow discussion, if you’re giving a shallow assignment, then you’ll know it because the students will have all the answers. They’ll be able to apply the recipe to the task at hand, check it off their lists, and then move on to the next one.

If you’re giving shallow assignments, then you’ll know it because your desk will fill up with small scraps of paper, little things to mark that don’t take much time but come in large numbers.

I’ve let this happen to me from time to time throughout my career. It’s pernicious. Pushing those papers back and forth across my desk makes me feel productive, but when I stop to ask myself whether or not I’m actually making an impact on my students’ hearts and minds, the answer is grim. That busy-ness that made me feel good and productive in the moment, typically makes me feel ineffective upon further reflection.

Be effective.
Make an impact.
Do something meaningful.
Avoid busy-ness.

Notes

¹ Some of us, I know are hemmed in a bit. We work in contexts that require us to check some boxes, to have some grade in the book each week or every other day, for example. In talking with colleagues at such schools, I have honestly never heard one of them say that they liked this system. I’ve never heard one of them say that having to put something in the grade book every week feels meaningful. I'd love other perspectives on this.

² I am indebted to the good people at the Exeter Humanities Institute (EHI) and Alexis Wiggins for always pushing me to let the students do the work in class discussions. For those who do a lot of class discussion, the baptism-by-fire available (at a rather steep professional development cost, I know) at EHI is second-to-none. The Harkness Method requires real patience and deep trust. But it’s just the sort of quality-over-quantity approach that I love. Alexis Wiggins has taken Harkness and morphed it into what she calls “Spiderweb Discussions.” Check out her book The Best Class You Never Taught.

This post originally appeared on sbhebert.com as "Avoid Busy-ness" (2020).

Lately, I've been thinking about the difference between culpability and responsibility. Culpability, of course, has to do with blame. When we cause a problem, we are at fault. We are guilty. We are culpable.

Often, we use responsibility in this same way. We might say, "I'm responsible for my mistakes," meaning that I am to blame for them.

But I don't think this is what responsibility is really about.

The root word for "responsibility" is response. We are responsible for something when we are called to respond to it. Sometimes, we are both culpable and responsible: it's my fault and now I must respond to it. But this isn't always the case.

As a teacher, for example, I sometimes have students come into my classroom who aren't prepared (yet!) for the work of the class. In some cases, they've been at a different institution with different expectations. In other cases, they've been passed along even though they didn't really meet the expectations of previous courses. When they walk into my classroom, I am not culpable for this lack of preparation, but I am now responsible for it. My job is to help the student learn and grow: I'm answerable for their success in my class. I'm now responsible for that student's growth.

This is where the recent conversation about Critical Race Theory, I think, has gone off the rails a bit. Many of the arguments I've been reading have talked about how past oppression — e.g., centuries of enslavement — is not my fault (speaking as a white person here); therefore, I'm not responsible for it.

This argument, however, confuses culpability and responsibility. I am not culpable for the development of the systems that led to enslavement, Jim Crow, redlining, underfunded schools, etc. I didn't do that. I'm only forty years old; I wasn't alive in the heyday of those policies.

But I am living in a world built by those policies; therefore, I am now responsible for them.

Most of the links in this month's "From Around the Web" deal with this issue. How are we responding to our history? What do we do? Where do we go from here?

This Month on ROOTED

Grading for Learning in Rochester and Teaching without Homework

Schools in Rochester are grading for learning, and I’m teaching without homework. Read on!

ROOTEDStephen Hebert

From Around the Web

Loretta J. Ross: What if we called people in, rather than calling them out?

How can we have more productive conversations with people we vehemently disagree with? Civil rights activist Loretta J. Ross gives us the tools to call people in—instead of calling them out.

NPRManoush Zomorodi

College students discuss need to ensure diversity on campus

Reaction follows Supreme Court decision to rule on University’s policy of considering race as one factor among many in admissions.

Harvard GazetteNikki Rojas Harvard Staff Writer DateJanuary 27, 2022January 27, 2022

Lack of diversity in AI development causes serious real-life harm for people of color

Kelsey Snell asks Black Women in A.I. founder Angle Bush about the consequences of the lack of diversity in artificial intelligence development.

NPRKelsey Snell

What’s the point of working toward problems that lack solutions?

Conversations with colleagues and young(er) teachers cause me to wonder what’s required to make positive change in response to answerless questions.

Talking with TeachersKatherine Burd

New Twist in Pandemic’s Impact on Schools: Substitutes in Camouflage

Deployed to classrooms in New Mexico to help with crippling staff shortages, National Guard troops are employing their informal motto, “Semper Gumby” — Always Flexible.

NYTimesBy

Teachers Are Quitting, and Companies Are Hot to Hire Them

Businesses that are eager to fill jobs are offering former educators better pay and more autonomy.

The Wall Street JournalKathryn Dill

About

ROOTED Monthly is a fresh collection of posts, links, and ideas about teaching. Please feel free to forward this newsletter or ask friends to subscribe. We'd love to reach a larger audience, especially if it will be helpful to any educators out there. Lastly, we would love to feature your voice on ROOTED. If you have an idea for an article, please go to our submissions page and get in touch with us.

A recent piece in the Rochester Post Bulletin ran under this headline: "RPS's new grading system, Grading for Learning, gets poor marks with many teachers." You can read the full piece here:

RPS’s new grading system, Grading for Learning, gets poor marks with many teachers

Teachers say they have lost autonomy in the classroom and are dealing with tremendous workloads.

Rochester Post BulletinMatthew Stolle

As I read, I was struck by the inadequacy of the headline. Yes, some teachers are struggling with the new "Grading for Learning" system, but the reporter, Matthew Stolle, goes to great pains to include voices of opposition, teachers who think the new system is good (or, at least, good if modified and managed better).

In a recent piece for this site, I argued for some similar provisions to Grading for Learning. My basic premise was simple: students should be able to continue trying until they've reached satisfactory results. It would be a shame, for example, if we told budding surgeons: "Nope, that's now how you do the procedure. You'll never be a surgeon." We don't do that, of course. The surgeon keeps practicing until she has the moves just right.

Don’t Like the Result? Do It Again.

I’ve tried just about every grading system under the sun: traditional systems, standards-based, no grades, etc. But starting with these three key principles, I really think I’m hitting my stride.

ROOTEDStephen Hebert

Likewise, I argue, students should be able to revise their essays, retake tests, and practice, practice, practice until they get it right.

The Rochester Public Schools have embraced this kind of ethic. You can read about the four "big ideas" at length on the page below, but I'll summarize them too:

Grading for Learning - Rochester Public Schools - Rochester, MN

Grading for Learning - Rochester Public Schools is a high performing public school district located in the middle of South-Eastern Minnesota and spans 218 square miles.

logo

Grading for Learning's Big Ideas

1. Homework, quizzes, and other daily tasks are formative practice and should not negatively impact a summative academic grade.​​
2. Reassessment is allowed on all summative assessments.​​
3. Nonacademic factors are not counted ​​in the summative academic grade.
4. Only evidence of student proficiency toward learning targets or summative assessments is used to reach a summative academic grade.

If you read between the lines here, you can see what's going on. Rochester Public Schools are trying to make grades meaningful again, a reflection of a student's proficiency rather than other factors.

This is an age-old debate, isn't it? A decade-and-a-half ago, the AP English Language and Composition exam featured this prompt for its argumentative essay:

When I was a kid, growing up in the 1980s and 1990s, it was common to receive extra credit or "points" for a variety of non-academic activities. I recall, for example, that I earned extra credit in my English classes because I was cast in one of the school's plays. Students not cast could receive extra credit for attending the play. No one really stopped to say, "Well, what if you aren't able to attend the play? How is that fair?"

I see Rochester's "Grading for Learning" paradigm as an attempt to wrestle grades to the ground, to make them meaningful so that they aren't simply indicators of privilege or unhealthy grindstone mentalities. Instead, the argument seems to be, grades should be indicators of academic proficiency. An "A" should mean that a student has met (nearly) all the objectives of the course, not that the student donated money to a charity or attended a play or whatever.

Where some will likely take issue with "Grading for Learning" is in its attitude toward quizzes and homework. Rochester Public Schools reframes these activities as "practice." Therefore, they are formative assessments intended to help students along the way. The grade, however, is not about how many practice sessions you held or how many little projects you completed. Instead, the grade will be based on the summative assessments: what can you do when the pressure's on?

This, of course, has its own set of problems. What do we do, for example, with students who have test anxiety?

But, it seems to be a step in the right direction.

As one teacher in the Post Bulletin article points out, the trouble that many teachers are having with the system is not really about the grading system itself: it's about the roll-out and the training.

If you are going to completely shift your paradigm of assessment, then you can't keep doing things the same old way. The homework and the quizzes need to be meaningful and targeted. Otherwise, don't assign them.

Stephen Shakes His Fist at Homework (Again)

This attitude — anti-homework — may seem controversial to some, but I assure you, students can, indeed, learn without homework. At present, I teach a homework-free class, and students are learning quite a bit. How do I know? Well, I pre-assess their knowledge and skills, then we go through a series of learning experiences, and then we assess again. The assessments show growth. Bingo!

While students are welcome to work on their assignments outside of class time, the course is designed such that the most meaningful, most important experiences, happen inside the classroom. After all, I see these students for 80 minutes every other day: roughly 60 hours of class time during the semester. I tell students at the beginning of the semester that if they give me their best 80 minutes every other day, then we won't have homework. They like the bargain, and they maximize class time.

So, how do I teach a homework-free class? Well, let me give you some thoughts.

Structure and Routine

Recently, an administrator observed one of my classes and called it "chaotic." This might sound like a strike against me, but I'm wearing it as a badge of honor. My class time does often look chaotic, but there's a structure and a routine that governs almost everything we do. (I say "almost" because I know, too, the value of breaking that routine.)

Here's the typical structure of an 80-minute class:

1. Gratitude. We spend 5–10 minutes going around the table and sharing what we're grateful for that day (or what we're looking forward to). This is an integral part of the class. When we skip it (rarely), students clamor for it.
2. Reflective Journaling. Students then take about 10 minutes to respond to a journal prompt. The prompt is intended to re-activate what we did last class while also looking forward to what we're going to be doing that day.
3. Common Experience OR Individual/Group Work. After the journal, then we move into either a common experience or individual/group work. This is where things look chaotic. I'll unpack these a little more below.

I don't do exit tickets or have any set ending for class. We work and chat and have a good time until the period is over. I usually wrap things up by telling them what they can expect next class and what they can be thinking about between now and then.

It's a loose routine, but it keeps us going.

Balancing Common Experiences and Individual/Group Work

The heart of our class meeting is either a common experience or individual/group work.

Common experiences include things like class discussions, all-class activities, viewing of documentaries, etc. These are pretty traditional, to be honest. so there's not much to say there.

Individual/group work is where chaos ensues. For each unit of the course, students can choose from a dozen or more projects and activities to explore. They can work on them individually or in groups. I don't care. As they work, they ask for help from each other, they ask for help from me. They also talk about what's for lunch later that day, or they might entreat their classmates to come to tonight's varsity soccer game. I don't really care. They chat, they have a good time, they also turn in some great work.

I do my best to balance these two modalities, but I err on the side of individual/group work as our most frequent mode because it offers students a great deal of choice in how they spend their time. My typical balance is one common experience each week.

I don't think anything I'm doing here is radical. The only thing that's radical about it, from my view, is an enormous respect for the students' time and intelligence.

I don't want my class to dominate their lives. I want them to enjoy the learning challenges I present to them each day. That's me respecting their time.

I want my grades to reflect my students' knowledge and skills. That's me respecting their intelligence. You don't need bonus points or extra credit for charity or mandated homework and such. Just come to class, try some new things, and see what knowledge and skills you pick up along the way.

This is where I love what these Rochester schools are trying to do: create a grade that has meaning. Now, let's mentor teachers into it so that they'll make it work in their contexts.