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What do we lose when we smooth out the natural wrinkles in our faces and our writing?
Introduction
Pentominoes are shapes made from 5 squares joined edge-to-edge. There are 12 of them:
Next, let's define what an enclosed area is with these shapes. The pentominoes must create a fence where they touch edge-to-edge with no overlaps. Note that corners touching is not closed because it is not edge to edge:
Activity
Build a fence with all 12 pentominoes, edge-to-edge, no overlaps. If you have manipulatives, Polypad or toys that can make them you can use those, or use the tool below (full page version).
Now that you've made an area here are some challenges:
- Maximize the amount of area
- Maximize the number of separate areas you can get
- Make the area inside a rectangle
- Make the shape on the outside a rectangle
- Make the shape on the inside and outside a rectangle
A quick note: These challenges have a rich history. Pentomino fences have been explored in Martin Gardner's 1960s columns, and by Solomon Golomb, Sivy Farhi, Michael Keller, Rodolfo Kurchan, and many others. Any variation I add has almost certainly been thought about before.
Educator Resources
Spoiler alert — go play before proceeding (this means you too).
Activity Structure
This is a 30–60 minute activity. If you haven't derived the pentominoes prior to this you might want to ask how many shapes are possible first to find the 12 shapes. Here is a toy to explore.
Make an initial garden (5-10 minutes)
This can be done with manipulatives, Polypad, or with the tool earlier in the post. The advantage of using something manual is getting to think about how the area is measured first.
Using the number bars in Polypad is its own activity. Using this for the first area or on paper has its benefits to count.
In Polypad, using arrow keys nudges pieces to align with the grid.
Iterate on the gardens - maxing (10-15 minutes)
Take the first garden and then tweak it with different ideas to see if more area can be created. This is where having a tool to count for you might help with having immediate feedback on small changes.
Small changes to try:
- Flip one piece
- Swap a few pieces
- Swap some corners
More to play with (5 min - the rest of your life)
What other ways could you optimize this fence? What other challenges are there?
- Max the number of enclosed regions
- Make the area inside a rectangle
- Make the shape on the outside a rectangle
- Make the shape on the inside and outside a rectangle
- Maximum number of enclosed 1x1 regions
- Maximum of two regions that are completely separate
- Make a game of it
Discussion Questions
- If all 60 squares came unglued, what's the largest garden you could build?
- Which pentomino is the best fencer? Which is the worst?
- When two pentominoes touch along an edge, how much fence is lost?
- Does a rounder garden beat a longer one?
- Did you plan the shape first, or place pieces and see what happened?
- What strategy would you share with a friend for building a fence?
- What's the best garden you can grow without the X?
- Can you grow two separate gardens of the same size? Three? Four?
- What are interesting questions you can pose?
Resources, Extensions, and What Ifs
- CIMT - Enclosure Problem
- Hexomino problems
- Katamino board game
- 3d print pentominoes, make them from Artec or other toys, print them
- There are thousands of extensions with pentominoes
- Knotted Doughnuts and Other Mathematical Entertainments by Martin Gardner
Vocabulary
- Pentomino — A shape made from 5 unit squares joined edge-to-edge. There are 12 of them.
- Polyomino — The general family: 1 square (monomino), 2 (domino), 3 (tromino), 4 (tetromino), 5 (pentomino), and so on.
- Unit square — A single square of side 1.
- Edge-to-edge — Two pieces touching along a full shared edge, not just at a corner.
- Fence — All 12 pentominoes arranged together to surround a region.
- Garden / interior — The empty squares fully enclosed by the fence.
- Enclosed — Trapped inside the fence with no path of empty squares leading to the outside.
- Leak — A gap where empty squares from the interior connect to the outside.
- Area — The number of unit squares in a region.
- Perimeter — The total length of the boundary of a shape.
- Isoperimetric — About the relationship between perimeter and area. For a fixed perimeter, rounder shapes enclose more area.
- Connected — All pieces touch (directly or through other pieces) so the fence is one whole.
- Conjecture — A mathematical statement believed to be true but not yet proven.
- Upper bound — A provable ceiling on how big something can be.
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EdTech has a recurring habit of announcing the next transformative breakthrough with enormous fanfare and little introspection. A new technology. A new model. A new price point that promises to reshape higher education, reduce costs, and finally achieve scale.
Often, these innovations are not entirely new. They are familiar ideas repackaged with updated language and some contemporary technology. But they tend to rest on a persistent set of misunderstandings — about how students actually engage, how learning unfolds, and how difficult scale really is.
The recent sequence of events involving Sal Khan offers a particularly instructive example. First came the quiet recalibration around Khanmigo, the AI tutoring tool that was initially framed as transformative but appears to have seen limited sustained use. Almost immediately afterward came the announcement of a new joint venture with ETS and TED to launch a low-cost online degree. The new venture was touted as.
[snip] a new higher education collaboration designed for an AI‑driven era. [snip][which] aims to prepare learners for the next generation of jobs while cultivating the uniquely human skills required to thrive in work, life, and society amid rapid technological change.
Individually, neither development is remarkable. But together, they illustrate something more revealing: the rapid pivot from one ambitious claim to another, without fully reckoning with the structural constraints that limited the first.
The 5% problem
The first signal of this latest cycle was the Chalkbeat article revisiting the performance of Khanmigo, Khan Academy’s AI tutoring tool. In it, Sal Khan appeared to temper some of his earlier claims about how transformative AI tutoring would be. The article noted that student uptake had been limited and that sustained usage was concentrated among a relatively small subset of students.
The piece quickly spawned a familiar secondary wave of commentary. But the most interesting detail in the article was a familiar one. Only a small fraction of students regularly used Khanmigo; roughly 5 percent in some implementations.
Khan and other company representatives expressed frustration at students’ lack of engagement. The students weren’t using it “correctly.” They weren’t self-initiating.
This is where the pattern reasserts itself.
When only 5 percent of students engage with a voluntary educational tool, that is not primarily a student problem. It is a design and integration problem. The hardest challenge in EdTech is not building a capable tool. It is embedding that tool into systems that support motivation, accountability, and sustained use — especially for students who are juggling many demands and uneven academic preparation.
Teaching the already motivated 5 percent is not transformation. It is amplification. Which makes the rapid pivot — from limited engagement with an AI tutor to the announcement of a fully AI-mediated, $10,000 degree — especially striking.
From tutor to degree
Within a week of tempering expectations about Khanmigo, Sal Khan pivoted to something new, this time in higher education. He announced a collaboration with testing giant ETS and TED (of TED Talks fame) to create an accredited university offering low-cost degrees.
ETS, Khan Academy and TED will announce a joint plan to launch the Khan TED Institute, a new higher education collaboration designed for an AI‑driven era. The Khan TED Institute aims to prepare learners for the next generation of jobs while cultivating the uniquely human skills required to thrive in work, life, and society amid rapid technological change.
The rhetoric is expansive. The ambition is unmistakable.
Another founder, Amit Sevak, who leads ETS, acknowledged that they are still working out many of the details, but that the new institution could someday enroll “tens of thousands” of students, rivaling flagship state universities. Sevak said he’s “100%” anticipating that its instructors will be humans, most likely a large network of adjuncts.
The details are still emerging. But the scale aspirations are clear: tens of thousands of students, rivaling flagship state universities.
The curriculum is still under development, but Khan said it will be guided by corporate partners that include Google, Microsoft, Accenture, Bain, McKinsey, and Replit. [snip]
None of the employers have committed to hiring graduates of the new program [snip]
Blue-chip corporate names lend credibility, and many of these same companies are already involved in creating and offering certificates and content on other platforms as well as their own.
None of this is unreasonable. But none of it is new. And none of it resolves the core challenges that have faced other “revolutionary” online low-cost degrees: sustained engagement, student persistence, and the economics of large-scale asynchronous delivery.
If voluntary AI tutoring struggled to engage more than a small fraction of students, what exactly changes when the same logic is applied to a fully asynchronous degree? To understand why this matters, it helps to remember what happened the last time we declared scale solved.
Reinventing what already exists
The Khan TED Institute is presented as a reimagining of higher education for the AI age. Strip away the rhetoric, however, and the underlying structure is familiar: asynchronous lessons, simulations, peer dialogue, remote faculty guidance.
Fully online higher education is not an untested frontier. Nearly 30 percent of U.S. higher education enrollments now include online coursework. Entire institutions operate at scale in fully asynchronous formats.
And yet the proposed design is framed as if it marks a departure:
Instead of professors lecturing from the front of an auditorium, the faculty will create virtual lessons and assignments that students can complete independently. The exact format and pacing of courses is undecided, but Khan said students will practice skills in group projects, asynchronous simulations and live “dialogue sessions” where they will receive peer feedback and support virtually.
There is nothing radical here. Virtual lessons, asynchronous assignments, group projects, simulations, peer dialogue sessions— essentially discussion boards or tutorials —have been core elements of online pedagogy for two decades. They are not breakthroughs. They are baseline design choices.
This pattern is familiar. A decade and a half ago, MOOCs promised to democratize higher education with nearly identical rhetoric: lower cost, global reach, scalable delivery, brand-name partners.
The MOOC model evolved into online degrees, many priced below traditional programs and delivered through platforms like Coursera — the most serious and well-resourced attempt to operationalize this approach at scale. Coursera combines brand-name university partners, a massive global learner base, and what Phil Hill has called an enrollment “flywheel.” And yet, even with those advantages, the degree business has proven harder than early projections suggested, as recent earnings reports make clear.
Recent data show that degree enrollments have plateaued and revenue per degree has declined. Coursera has increasingly shifted emphasis toward shorter credentials and enterprise offerings rather than continued degree expansion. This is the best-resourced version of the model. If scale were easy, it would be visible here.
The lesson is not that online degrees cannot succeed. It is that scale requires sustained marketing investment, institutional credibility, student support infrastructure, and retention strategies that go well beyond content delivery.
The proposed Khan TED Institute would enter a more crowded and mature market than Coursera did in 2017. It would do so without an existing institutional brand, and with an undergraduate target population that is typically more brand-sensitive and retention-challenged than graduate learners. A $10,000 price point is rhetorically powerful. But price alone does not solve acquisition costs, persistence challenges, or the economics of sustained student support.
What makes this different from earlier Khan Academy expansions is that the degree market is not a philanthropic distribution problem. It is a competitive acquisition problem. In the past, Khan Academy and Khanmigo benefited from large institutional adoptions, foundation support, and government partnerships. They did not have to fight for individual tuition-paying undergraduates in a saturated, brand-sensitive market with high marketing costs and low retention margins.
Competing in the undergraduate degree market requires sustained marketing investment, enrollment operations, student support infrastructure, and regulatory compliance — not simply compelling rhetoric and strong corporate brand associations.
Scale is not frictionless
The proposed curriculum for the new Khan TED Institute is fairly predictable and not unreasonable, if a bit STEM and jargon heavy.
*Core knowledge in mathematics, statistics, economics, computer science, science, history, and writing.
*Applied AI skills, including AI‑assisted app development, financial modeling, building AI agents, and team‑based deployment projects.
*Communication and leadership, developed through structured collaboration, peer tutoring, dialogue sessions, and public speaking"
On paper, this looks coherent. It blends some liberal arts with technical fluency, and applied collaboration. But what is striking is how frictionless the model sounds.
Content can be delivered asynchronously. Skills can be practiced in simulations. Dialogue sessions can be scheduled. But learning — especially for under-prepared or time-constrained students — is not a smooth pipeline from exposure to mastery. Learning requires feedback loops, accountability, structured practice, and often sustained human intervention. AI can assist with parts of this. It does not replace the systems and practices that make engagement durable.
If a voluntary tutoring tool struggled to move beyond a small, self-motivated minority, it is reasonable to ask what mechanisms will ensure persistence in a largely asynchronous degree, especially when the underlying logic still seems to be that access to a tool or content is enough and that students themselves bear responsibility for engagement.
This fact comes out in the recent Chalkbeat article from Sal Khan.
Khan gives this analogy: Imagine he walked into a class, sat in the back of the room, and waited for students to seek out help. “Some will; most won’t,” he said. That’s been the experience with AI tutoring, he said. It doesn’t necessarily make students motivated to learn or fill in gaps in knowledge needed to ask questions.
And from Kristen DiCerbo the Chief Learning Officer.
Kristen DiCerbo, the organization’s chief learning officer, said AI can only respond to students based on what they ask. And it turns out, she said, “Students aren’t great at asking questions well.”
And yet the need for deliberate and designed engagement is even more important in an online degree than in a tutoring app. The students most drawn to low-cost, asynchronous degrees are often balancing work, care-giving, and uneven academic preparation. Designing for their success requires more than access to content and AI tools. It requires structured support systems, good pedagogy and instructional design, integration into institutional processes, and sustained human accountability. And nothing in the current framing suggests that these structural engagement challenges are being treated as primary design requirements rather than downstream engineering problems.
The interval is shrinking
The debate over Khanmigo also prompted a sharp observation from one of the most perceptive critics of technology-mediated learning at scale. Before the announcement of the Khan TED Institute, Justin Reich proposed what he called the “time-to-TED-talk-renunciation” metric — the interval between a bold claim about technological transformation and the subsequent retreat to a more modest position.
In 2011, Khan argued "Let's Use Video to Reinvent Education." In 2019, he gave an interview with District Administration magazine where he suggested that actually we shouldn't reinvent learning, but students in math class should do online practice problems one day a week [snip] In 2023, Khan argued that "AI Could Save Education," and in 2026 Matt Barnum in Chalkbeat basically got him to quote the thesis of Failure to Disrupt: "“AI is going to help, but I think our biggest lever is really investing in the human systems.” [snip]
It begs the question, given that the time-to-TED-talk-renunciation is shrinking, at what point should we predict that Sal Khan gives a TED talk where he SIMULTANEOUSLY advances and then renunciates some techno-utopian idea
Reich’s framing is humorous. But it captures something real. Each new technological promise arrives with expansive rhetoric. Then implementation collides with student behavior, institutional inertia, and economic reality. The recalibration follows.
Reich even sketched a trend line.
The interval appears to be shrinking.
And then, within days of Reich posting that chart, Sal Khan announced the Khan TED Institute — from the stage of a TED talk.
The humor works because the pattern is familiar. But the stakes are not trivial. Each cycle absorbs institutional attention, philanthropic dollars, public imagination — and some students who will enroll in the new experiment and struggle.
The deeper issue is not ambition. It is repetition. We keep repackaging familiar models without grappling with the structural constraints that limited them in the first place. In this case, that constraint is engagement — how to design for sustained participation among students who are balancing work, caregiving, uneven preparation, and financial risk.
The interval between declaration and re-calibration may be shrinking. The underlying mistakes remain unchanged — and they will produce the same outcomes unless the design logic changes.
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From disappearing news articles to lost films, music, and websites, a new book from the Internet Archive reveals how our shared digital record is eroding, and what it will take to preserve it.
What does it mean to live in an era where culture can simply… disappear?
Vanishing Culture: A Report on Our Fragile Cultural Record—a new book from the Internet Archive—brings together essays, research, and case studies that document a growing crisis: the erosion of access to the knowledge, media, and history that shape our collective memory. From journalism and government information to music, film, and the web itself, the shift from ownership to access—and from physical to digital—has made culture more vulnerable than many realize.
This isn’t just about nostalgia. It’s about accountability, scholarship, and the public’s right to access information. When news articles are altered or removed, when public information is taken offline, or when creative works are locked behind shifting licenses, the historical record becomes incomplete. What disappears is not just content, but context.
DOWNLOAD & READ Vanishing Culture for free at the Internet Archive. PURCHASE A PRINT COPY from Better World Books, or your local bookstore.
Recent efforts by some publishers to block web archiving services like the Wayback Machine underscore how fragile access to digital history has become. When large portions of the web are intentionally excluded from preservation, gaps in our shared record are structural, not accidental.
At the same time, libraries, archivists, and preservationists are working to push back against this loss. The Internet Archive and its partners continue to build a digital library for the web: capturing, preserving, and providing access to materials that might otherwise vanish.
Vanishing Culture is both a warning and a call to action. It invites readers to reconsider what it means to preserve culture in a digital age, and to recognize that without intentional effort, much of what we create today may not be available tomorrow.
Read the book, explore the essays, and join us in the work of preserving our digital past before more of it disappears.
Hello!
Today’s email is a little longer than usual—though I like it and it has pictures. Feel free to just stay for the sketch as always.
Jono
The McNamara Fallacy is a belief in easy-to-measure quantitative metrics at the expense of ignoring hard-to-measure qualitative factors.
Robert McNamara was president of Ford Motor Company and later the Secretary of Defense for the USA during much of the war in Vietnam. He was highly intelligent and excelled at dealing with data and using it to inform strategy.
Coined by social scientist Daniel Yankelovich, the McNamara Fallacy, also called the Quantitative Fallacy, involves:
Prioritising easier-to-measure quantitative metrics and
Disregarding hard-to-measure qualitative metrics as unimportant
The fallacy can progress as follows:
Measure what can be measured
Disregard what we can’t measure
Assume what cannot be measured is not important
Conclude that what can’t be measured doesn’t exist
In the words of Yankelovich:
“The fallacy is: If you’re confronted by a complex problem that is full of intangibles, you decide to measure only those aspects of the problem that lend themselves to easy quantification, either because you find other aspects difficult to measure or because you assume that they can’t be very important or don’t even exist.” …
“It is a short, fatal step from the statement, ‘There are many intangibles and imponderables that we can’t put on our computers,’ to the statement, ‘Let’s measure what we can and forget about the intangibles.’”
Yankelovich cites working with Ford during McNamara’s time and sharing research that included both quantitative and qualitative factors. As the research was assessed, the qualitative data on the meanings people gave to small cars were discarded, and the less significant quantitative and demographic data were retained.
Examples of the McNamara Fallacy
I first learned about the fallacy from a reader’s article about one of the easiest to measure aspects of a bike: its weight. All things being equal, we’d probably prefer a lighter bike, but other aspects like maintenance, reliability, and handling are important yet harder to measure, report on and compare. As a result, weight often comes to the fore at the expense of the others.
Other examples might include:
Perhaps your hiring time is down, but how is the fit of the people you’re bringing in?
Maybe more people are visiting your website, but they aren’t the right people for your service.
We can calculate a country’s GDP, but GDP doesn’t account for human labour without a monetary transaction—like a home-cooked meal—and vital work done by nature, like filtering water, sequestering carbon, or lifting spirits.
If food in a can gives you all the nutrients you need, what are you missing by skipping family meals?
A commonly cited example of the McNamara Fallacy is the US military’s approach to measuring progress in the Vietnam War.
The McNamara Fallacy and the Vietnam War
As the US Secretary of Defense from 1961–1968, McNamara employed similar techniques to those he had used successfully in business contexts to assess the progress of the war in Vietnam.
If wars were won by inflicting damage on the enemy, then metrics measuring the damage inflicted should be decent proxies. In particular, assessing body count evolved to be the primary measure of progress.
Reliance on purely quantitative metrics had significant shortcomings. In this context, enemy body count was more easily quantified than their morale, political support, or resolve to defend. Because of the interest in this measure, many officers believed that it was also often inflated (see Goodhart’s Law and Campbell’s Law below).
Measuring the tons of explosives dropped is easier than measuring the reduction in capabilities they caused. Knowing the number of troops you have is easier to measure than the abilities of those troops.
According to the numbers, the U.S. was winning the war, yet it failed to overcome the resistance of North Vietnam.
Related Ideas to The McNamara Fallacy
An over-reliance on quantitative metrics quickly leads to several other related problems to deal with:
Goodhart’s Law: When a measure becomes a target, it ceases to be a good measure.
For example, assessing the progress of war on the numbers of enemy dead may lead to increased killing to inflate numbers. Or pinning a bonus on review ratings may lead to fake reviews.
Campbell’s Law: The more a metric counts for real decisions, the greater the pressure for corruption, and the more it distorts the situation it’s intended to measure.
For example, if reducing crime rates matters for law enforcement jobs, it creates an incentive to under-report cases or downgrade crimes.
The Streetlight Effect: Looking where it’s easiest to look, rather than where it matters. Also known as the drunkard’s search or looking under the lamppost, the Streetlight Effect is named after the economists’ joke of a person scrabbling on the ground for their car keys under a street light. When asked where they lost them, the person says they dropped them “over there,” but the light’s much better over here.
For example, optimising an existing product because it is known and does well, rather than working on an uncertain new product.
You Get What you Measure: The instrument you use to measure affects what you see.
“For example, in school it is easy to measure training and hard to measure education, and hence you tend to see on final exams an emphasis on the training part and a great neglect of the education part.”—Richard Hamming
The Cobra Effect: When an implemented policy backfires causing the opposite of the intended outcome. Named after a British attempt to reduce cobras in Delhi by introducing a bounty on dead cobras. Seeing the lucrative bounty, people started farming cobras thereby increasing their numbers.
For example, the Streisand Effect is a well-documented case of a cobra effect when Barbara Streisand tried to suppress images of the coast that included her house. The attention drawn to her attempts to suppress the images brought thousands of people to look at them who otherwise would never have considered it.
And all of these are examples of the broader Law of Unintended Consequences, which comes from trying to regulate a complex system with a simple system.
If the ideas above aren’t enough, you might also see:
In theory, practice is the same as theory, but not in practice
All models are wrong but some are useful (from last week)
Reliability vs validity: You can hit the target but miss the point.
The map is not the territory (no sketch!)
For a super discussion on the measurement of all sorts of things, including acute (being run over by a bus) and chronic risks (smoking), I recommend the fun podcast episode: Microlives & The Art of Uncertainty with Sir David Spiegelhalter
The article about bike weight is: The McNamara Fallacy and Bikes by Peter Verdone
Daniel Yankelovich, who named the McNamara Fallacy, stressed no disrespect to “one of our most distinguished citizens,” Robert McNamara, “a brilliant and dedicated man who brings a vital intensity to bear on his work.”
Some of the complexities of the war and McNamara are covered in the Academy Award-winning documentary The Fog of War, which makes fascinating and, at times, uncomfortable viewing.
With AI tutors underperforming the expectations of their creators and kids feeling increasingly negative about artificial intelligence, there is an opportunity and a mandate to pivot edtech towards humanity. What does that mean and what would it look like? Here is how I think about it.
Pivoting edtech towards humanity means using the power of technology to align one human’s desire to learn with another human’s desire to teach.
For Example
The physical classroom.
A bunch of people want to learn. A bunch of people want to teach. Until we connect them in time and space, those people are misaligned, their desire wasted. The technology of the physical classroom brings those desires into alignment.
Digital media.
As a new teacher, I noticed kids had a desire to learn how math connected to the world outside the classroom. I had a desire to teach them about those connections but very little ability to do so because the world outside of the classroom was outside and we were inside. The technology of digital projectors and cameraphones let me capture the world outside the classroom and bring it inside for our analysis. Technology brought our desires to learn and teach into alignment.
For Counterexample
If you ever have the feeling that edtech isn’t all that interested in humanity, it’s frequently because:
Edtech companies try to align human desire to the power of technology.
Edtech companies often take a particular technology as the answer and then retrofit teaching and learning into the question. This is why, for years, various companies insisted that teaching is something very close to playing a video of an explanation, which makes “just play YouTube videos” seem like the answer to the question “why is teaching hard?”
Edtech companies ignore second-order effects.
A student feels like their class is moving a little slower than they’d like. An edtech company then suggests having every kid learn on computers which let them work at different paces. The company ignores the second-order effect that “kids also like learning together and now they can’t.”
Misalignments That Interest Me Currently
Kids want to work on paper and it’s hard for teachers to know what they’re doing.
Teachers struggle to support a student’s thinking if it isn’t visible. Lots of thinking happens on paper and teachers often lack the time necessary to review and respond to it. How can we make that paper-based learning more legible for more teachers?
Coaches want to support teachers and teachers want their support.
Coaches often have too many teachers on their roster to support with model lessons and walkthroughs. Also, many teachers want support but not in the form of a model lesson or walkthrough. The desire to give and receive support are misaligned here.
Teachers want support in leading whole-class discussions.
Whole-class discussion is some of the most satisfying work for teachers and productive learning for students. But it is very hard work. Misaligned.
Caregivers want to support their kids but don’t know how.
Many parents and caregivers want to do more to support their kids’ learning than they do currently. But they often lack visibility into student learning and may need some education themselves. The reports that schools send home are frequently summative, low resolution, and a waste of ink or pixels overall. Teacher emails are much more useful but time-consuming for the teacher. What can schools and edtech companies do to help align caregivers, teachers, and kids here?
Who will do this work?
I could point to dozens of people doing this work of pivoting edtech towards humanity. They are exceptional. Many of them are my coworkers. Common to each of them is an excitement for new technologies and a desire to understand the work of teachers and the lives of students that I can only describe as “insatiable.” If that’s you, let me know what you’re working on in the comments.
Featured Comments
My obituary for Khanmigo and AI tutors inspired so many of you to share your own stories of grief. This newsletter is here for you. This was a common interesting interaction. From deep within their grieving process, someone would ask:
What’s the alternative? If Khanmigo doesn’t work, what’s the Plan B?
I’d point out the effect of Saga’s tutoring interventions in Chicago Public Schools as one of many interventions that has had a positive effect on student learning. Still grieving, this person would respond that this intervention is “difficult to implement at scale.”
This is such a strange standard for evaluating interventions in education. It is very true that good things are often difficult and expensive while useless things are often easy and cheap. Many people mistake this fact as an argument for doing useless things! (My colleague Chris Blackett develops this idea more at Talent Lab.)
Mae Baltz mentions another kind of subsidy Khanmigo frequently received: administrative mandate.
As a teacher in one of the areas that received money for Khanmigo I was asked to have my students interact with Khanmigo at least 10 times per month (each student).
In spite of those mandates, Khan Academy reported yesterday that “only around 15% of students who have access to Khanmigo engage with it.” That indicates pretty serious misalignment.
Katelynn Petersen describes the difference between AI and human tutors. Please write this down somewhere!
As soon as I started hearing about tutors being replaced by AI, I knew that the people responsible for such nonsense had never tutored a day in their life. 40% is remembering to ask about the novel they are writing, the tea they spilled about their friends, or the language test they’ve been studying for all year. 40% of my time is spent just building confidence and reassuring students they’re doing the right thing. 20% is actually teaching math.
Odds & Ends
¶ I have very little to say about Khan Academy’s new venture, announced just before I posted my obituary last week. An edtech visionary is stymied by traditional education and retreats to the friendlier terrain of corporate e-learning. Am I talking about the $10,000 degree Khan Academy will offer in partnership with ETS and TED? Or am I talking about the $7,000 degree Udacity offered in partnership with Georgia Tech after their disastrous experience trying to support college freshmen 13 years ago. Answer: yes. Hop in the time machine, kids. NB: Read Glenda Morgan’s pre-mortem or John Warner’s polemic.
¶ Marc Watkins writes about the same crisis of purpose in higher ed that I am seeing in a local eighth-grade class.
It’s easy to dismiss lazy students or burned-out teachers turning to AI, as many seem to do in the comment sections of social media posts, where we hear a litany of solutions from folks that range from bluebooks to oral exams to entire technology bans. But AI isn’t simply a crisis in assessment. No, the true crisis here is purpose.
¶ A couple of tremendous writers and thinkers take on the AI chatbot tutor’s promise of “infinite patience.” First, John Warner talks about his gratitude for the finite patience of his teachers.
Some of my most important formative educational experiences involved some teacher or authority figure losing patience with me.
Second, Julia Freeland Fisher asks, in a world of infinite patience, “whose while are you worth”:
AI’s champions often laud it as “infinitely patient.” AI’s unerring support is undoubtedly powerful, especially when time and resources are scarce. But it falls short of the experience that accompanies real patience: not just material support, but the feeling you are worth someone else’s while.
¶ I’ve worked in curriculum development for over a decade and this comment from Stanford’s Sam Wineburg on Justin Reich’s podcast earlier this year is a really rare insight.
Ultimately, curricula are not for kids. Curricula are for the teachers. And if the teachers don’t feel exuberant and don’t feel ennobled by being the mediators and the adapters of those curricula, they can be the best and most thought-out curricula in the world, but they’re ultimately going to find dust on some shelf.
¶ I’ll have more to say about the digital backlash someday. For now, I’ll let it suffice to say three things.
I think the coalitions that are forming are among the wackiest I’ve ever seen on any issue.
As a parent, I wish schools would scrutinize their use of edtech more closely.
I think the edtech companies that understand teaching and learning, that prioritize the humanity of teachers and learners, are probably going to be fine.
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