Craig M. Booth’s Homepage

Dead Education Theory

2025-06-27T00:00:00-05:00

I think we’d all agree that the internet feels… less human, and if I’m being completely honest, more pointless these days.

Content is increasingly spun up by algorithms for other algorithms to amplify, leaving genuine humanity stranded on the sidelines.

That hollow sensation has a name: Dead Internet Theory. You’ve surely felt this yourself, picture every social media feed nowadays. That endless stream of TikTok-style clips stitched from stock footage, capped with a perky synthetic narrator: “Here are five productivity hacks you won’t believe!” The AI-generated video exists only so a recommendation AI can serve it to another AI bot that dutifully clicks “like.” The cycle rolls on. It’s a perpetual-motion machine whose sole product is futile noise.

Lately I’ve been wondering if the same dynamic is seeping into our classrooms. Let’s call it Dead Education Theory. Imagine the following:

A professor uses AI to draft a rubric and assignment prompt for their class (as enabled by any one of many dozens of free and commercial tools).
A student pastes that prompt into another AI, gets an essay back, and submits (this behavior is becoming common, with a large fraction of submitted essays in colleges showing the hallmarks of AI use).
The professor, pressed for time, runs it through an AI that spits out tidy boilerplate feedback (see, for example, the student suing Northeastern University after she discovered feedback on her paper was generated by ChatGPT)
Off to the side, originality checkers and paraphrasing bots duel in an endless game of evasion and detection.

On paper, the learning loop is complete. The essay is written.

The existence of the end product aside, no moment of human interaction, struggle, or learning ever happened, just people watching progress bars while mathematical models spat out text.

I firmly believe this pattern is already happening hundreds, if not thousands, of times every day. The attraction of AI-powered time-savings is just too sharp.

What, then, if this is taken to its logical conclusion? We’re dutifully performing a ritual whose purpose has drifted away, like lighthouse keepers polishing the lenses even after the shipping lanes have moved.

Where’s the human spark—the struggle, insight, revision that schooling is meant to cultivate? Banning the tools isn’t realistic; the genie’s out. But we can design for a future where AI amplifies authentic thinking instead of hollowing it out.

This is the flag I want to plant in the ground. Human-centric education in a world where we have to work alongside AI is what we have been building — and continue to build — at Packback. We need platforms that shove students (and professors) into the messy, collaborative middle of learning. AI will be in the classroom; the important question to my mind is how we keep humanity there, too.

Roots of Responsible AI: A New Webinar on What Really Matters

2025-06-26T00:00:00-05:00

Today, a conversation I care deeply about is live for everyone to explore.

Earlier this month, I co-hosted a webinar called Roots of Responsible AI: An Educator’s Guide to Ethical Innovation. It wasn’t about the flashiest uses of AI—but about the real work of teaching: ethics, inclusion, pedagogy, and trust.

This session was built around a simple idea: AI in education shouldn’t just be exciting—it should be responsible. That means grounding every innovation in transparency, fairness, and a deep respect for the role of the teacher.

In the webinar, we unpacked key questions:

What does “ethical AI” actually look like in the classroom?
How can we design prompts that teach—not just generate?
Where are the limits of AI, and how do we safeguard against them?

We covered practical frameworks, live demos, and ways to bring these ideas into your own instruction right now. If you’ve been wondering how to move beyond detection and into prevention—how to use AI to build student agency, not just catch shortcuts—this is for you.

I hope you’ll take an hour to watch the recording. The tools are evolving fast. The time to lead with purpose is now.

Year Two Under the Panels: A Straight-Up Look at Our Energy Numbers

2025-06-01T00:00:00-05:00

In my last solar post, I dug through twelve months of data from our brand-new array, marvelled at heat-maps of generation and consumption, and sketched a long-term plan to electrify the house. Eighteen months have slid by, and the spreadsheet has a whole lot more rows. time to plot, squint, and see what the second lap around the Sun actually looked like.

Important context: other than swapping a gas range for an induction cook-top last spring, nothing about the house or the system has changed. No extra panels, no batteries, no insulation blitz—just the same roof, the same Enphase API, and a slightly different way to boil water.

Self-Sufficiency, Month by Month

Share of our household electricity met by the solar array from January 2023 to May 2025; note the steep winter-to-spring climbs, late-summer dips, and the small post-induction nudge that keeps spring values just shy of 100 %.

Climbing out of winter. Like last year, our curve shoots from ~60 % in the darkest weeks to 100 % as soon as daylight and shoulder-season temperatures align.
The mid-2023 dip. A soggy July–August knocked generation while the AC ran overtime. You can see the wobble down into the 70 % range.

Generation ↔ Consumption by Season

Average daily kilowatt-hours produced (orange) and used (red) through the year, illustrating a healthy spring surplus, near-parity in high summer, and the familiar winter shortfall when short days and extra heating tip the balance.

This chart shows some interesting seasonal trends:

Spring (Apr–May): Generation outpaces demand by roughly a third—ideal “solar surplus” months.
High summer (Jul–Aug): Long days but we use a lot of electricity as the HVAC + dehumidifier fight heat and humidity.
Autumn shoulder (Sep–Oct): A tight squeeze, shorter days, but also our AC use begins to slow.
Deep winter (Dec–Jan): The familiar crunch: short daylight, low-angle sun, holiday cooking, and little help from the roof.

What the Numbers Tell Me

A few interesting takeaways from this exercise:

Seasonality still rules. Without storage the winter gap is unavoidable; even perfect efficiency can’t conjure photons in January.
Incremental electrification is visible, but not catastrophic. The stove nudged consumption upward less than 100 kWh for the whole year—well within the spring surplus.
Spring beats summer for generation: the radar chart shows our panels producing more power in April–May than in high summer—my hunch is that the lower spring sun hits the roof closer to 90°, letting each photon land square on target.
More data beats intuition. I’d have guessed the new range would be a bigger hit, and that we would be generating more electricity int he summer. The plots say otherwise.
Overall electricity use crept up ~4 % year-on-year, almost entirely attributable to the induction range.

Looking Ahead

For now we’ll keep gathering clean baseline data and let the system coast, adding neither batteries nor extra panels. If any of our remaining gas workhorses—the furnace, water heater, or dryer—happen to give up the ghost, their successors will be electric; until then, we’re content to let them live out their natural lifespans rather than sinking capital into premature upgrades.

The Quiet Architecture of Trust

2025-04-15T00:00:00-05:00

Today, something I’ve been quietly proud of was released into the world.

Back in November, I gave a webinar about AI in education—not the flashiest parts of AI, but the parts that really matter: ethics, pedagogy, and the quiet architecture of trust. That talk became a book, and I’m its author. Pulling Back the Curtains on Ethical and Pedagogical AI is now available as a free eBook on Packback’s site.

This project matters to me because it asks the questions I think we all should be asking: What happens when AI enters the classroom—not just as a helper, but as a decision-maker? Who gets to understand the decisions it makes? How do we build systems that reflect the best of what education can be?

In the book, I tried to lay out a hopeful, rigorous, and honest path forward. One where AI is explainable. Where feedback is formative. Where students don’t just pass—but grow. And where teachers remain at the heart of the learning experience.

If you’ve ever wondered what “responsible AI” actually looks like in practice—or if you just want to know what kind of future we’re trying to build at Packback—I hope you’ll take a look.

Read the eBook

Alienation, Autonomy, and AI Art

2025-04-11T00:00:00-05:00

Lately, I’ve been thinking a lot about where the line between humans and machines lies—especially when it comes to creating art. As artificial intelligence continues to evolve, this boundary gets blurrier. I’m left wondering what “art” even means in a world where creative works can be autonomously generated by algorithms.

To explore this uncertainty, I’ve been experimenting with AI as a creative partner. It’s a nuanced issue: AI frequently acts as a leech, absorbing and regurgitating existing art, and I fully recognize and grapple with the ethical concerns this raises. Still, I’m deeply interested in discovering if AI might open genuinely new pathways and unexplored spaces for creativity.

In 2023, I created “A Way of Making Consequences for Behavior,” a project designed to express my complicated feelings when AI first surged into mainstream consciousness. It’s a series of AI-generated images and an AI-generated soundtrack reflecting alienation, paired with a contemplation on free will. In this project, my role was significant—I curated, guided, and iterated extensively. Honestly, I’m still not sure how well it captures what I intended, but it remains a genuine snapshot of my mindset during that transformative moment.

More recently, in 2025, I developed “A Map Without Edges,” an experiment that pushes even further. This project tests whether an AI can independently weave together a coherent, ongoing narrative in real time. From a technical standpoint, it’s also my experiment in complete AI autonomy: every single character of the website, from the initial concept down to the final line of code, was generated entirely by AI. Unlike the previous project, here I did little outside of the initial prompting, a reflection of how rapidly AI capabilities are progressing—a thought that’s both exciting and a bit unsettling.

Both of these projects have helped me understand—or perhaps better accept—the strange new space we’re entering. You can explore them yourself here:

See “A Quiet Horizon” here

Part III: Living in the Middle

2025-04-03T00:00:00-05:00

This is Part III of a series exploring meditation and emptiness. For context, you may want to read the series introduction first.

“When I look for the place that awareness comes from, I can’t find a center. When I stop looking, it’s everywhere.”
— from my journal

When the self started to flicker, something else stepped forward: the ordinary.

Not the dull or the boring, but the background hum of daily life. The fan in the room. The shape of my shadow in the corner of my eye. The way my daughter says “just a little bit” when she wants something very much. These things had always been there, but now they glowed. Not because they were more meaningful—but because they weren’t trying to be anything at all.

In the months that followed, I found myself drawn to what I now think of as the grace of ordinary emptiness. Not grand insights, not fireworks, but the way things soften when you stop needing them to stand for something.

Everything Fades (Even the Toaster)

Another insight that arrived around this time was one of the most ordinary, and one of the most strange.

I was sitting in meditation, noticing sound. There was a low hum from a space heater across the room. Normally, I would have called it background noise. Not worth attention.

But something shifted. I started to notice the sound not as a “thing,” but as a flickering. A presence that came and went with each pulse of attention.

Then a thought: The heater doesn’t make a continuous sound. I just keep re-noticing it.

That simple realization cracked open a whole layer of view. Because if the heater only exists when I notice it—what else is like that? How many things in my experience are present but not central?

This led to a phrase that became a kind of koan for me:

Even the toaster fades.

Not everything needs to disappear. Some things just need to be seen in the background.

And that’s enough for freedom.

The View Builds the World

Throughout this process, one thing kept ringing true: view is generative.

It doesn’t just interpret experience. It creates it.

When I sat with the view of impermanence, things flickered. When I sat with the view of not-self, identity collapsed. When I sat with the view of awareness, space opened up. Each view brought forward a different world. Not in theory—in direct perception.

This changed how I approached practice. I stopped looking for the “right” view and started asking: what view serves here? What loosens clinging? What reveals possibility?

It reminded me of something from my physics days: you use different equations for different scales. Newton for the baseball. Relativity for the planet. Quantum theory for the atom. Each model is incomplete, but useful. None are reality.

Maybe dharma views are the same.

The Tension of Being Seen

Around this time, a subtle desire emerged in practice: the wish to be seen.

It wasn’t about visibility in the social sense. It was subtler. The wish for my insights to be known. The hope that my practice would register somewhere. The quiet longing to be a “serious” student in the eyes of someone I respected.

And beneath that, a fear: that if no one saw this work, it wouldn’t count.

I sat with that for a while. What I began to notice was that this desire to be seen was actually a kind of contraction—a clinging to identity. An inner leaning forward. A need to locate myself somewhere, anywhere, in the field of view.

Of course, the irony here isn’t lost on me. I’m writing all this in a public blog post.

But there’s a difference between wanting to be seen and letting yourself be seen. The first is about performance. The second is about vulnerability.

It still feels edgy to name all this here. But part of my practice now is being willing to say the quiet parts out loud—not to be validated, but to be honest. To loosen the grip.

The deeper this goes, the less there is to hold up as progress. And the more grace there is in anonymity.

When Vision Goes Quiet

At one point, I became fascinated with visual silence. I wanted to know: what is the quietest thing I can see?

It turns out, vision doesn’t have a “zero.” Even with eyes closed, there’s a dance of light, color, afterimage. A stubborn refusal to be still.

But then I realized: the silence isn’t visual. It’s the moment I stop labeling what I see.

When I stopped looking for silence and just looked at seeing, a kind of peace emerged. Not from absence, but from simplicity. From not needing anything to resolve.

It made me wonder: how often do I confuse quiet with disappearance? How often do I miss stillness because I’m waiting for blankness?

Stillness, it turns out, can shimmer.

Letting the Jewel Turn

One of the metaphors that kept returning in this phase was that of a jewel: many facets, all interconnected.

Emptiness. Fading. Awareness. Not-self. None of these are separate. They’re different ways of looking into the same mystery. And each one illuminates the others.

When I held them gently, let the jewel turn, I saw new reflections. I stopped asking which was right, and started asking what was helpful.

There’s a kind of faith in that. A faith not in belief, but in the process. In the unfolding. In the middle way.

And that’s where I find myself now.

Not at an end. Not at a peak. But somewhere in the middle of a path that keeps dissolving just enough to walk on.

This is Part III of the “Unbuilding the Self” series. View the series overview, read Part I: From Reductionism to Raw Experience, or read Part II: The Vanishing Self.

Part II: The Vanishing Self

2025-04-02T00:00:00-05:00

This is Part II of a series exploring meditation and emptiness. For context, you may want to read the series introduction first.

“Are you good with bad habits, or are you bad with good habits?”
— Shel Silverstein

When I started this study of emptiness, I didn’t expect to run directly into myself.

Not the poetic, capital-S Self. I mean the messy, cobbled-together identity I wear every day without really noticing: smart, competent, kind (mostly), quietly judgmental, a little anxious, ambitious-but-humble, grounded-but-dreamy. The whole contradictory kit.

At some point during this process, I was invited to simply write down the ways I define myself. I did. It was uncomfortable almost immediately.

Here were the contradictions, side by side:

I am kind and I am jealous.
I perceive and accept rather than judge, but sometimes I’m judgy as hell.
I am ambitious and try to win, but my preferred role is to support and take a back seat.

All of these are true. And none of them are consistent. What struck me wasn’t just the contradictions themselves, but how invisible they’d become. I operate within these stories every day, often clinging to one side while denying the other exists.

What I started to see—slowly, reluctantly—was that the problem wasn’t the content of the stories. It was the fact that they were stories at all.

The Stories That Shrink Us

I wouldn’t have told you that my self-concept was a source of suffering. But after sitting with these definitions, I began to feel how much tension they hold.

When I’m being judgmental, it’s like the part of me that believes I’m accepting disappears completely. When I’m chasing a win, the quiet supporter vanishes. There’s no space for both to coexist. And yet, they do.

My intellectual mind can easily hold this complexity. But in any given moment, I tend to collapse into a single, narrow identity—and then suffer when reality doesn’t match it.

And then, one evening, I remembered a poem.

I asked the zebra / Are you black with white stripes? / Or white with black stripes?
And the zebra asked me…

If you know it, you know where it goes.

Zebra Questions, by Shel Silverstein. Silly, sharp, unexpectedly profound. And suddenly, it felt like the dharma in a children’s book.

Are you good with bad habits? Are you noisy with quiet times? Are you neat with some sloppy ways?

That zebra had seen through the game. I was just beginning to.

Spaciousness by Way of Inconsistency

Around the same time, I started paying attention to how my sense of self changed throughout the day.

It was surprisingly variable.

When I was emotional or distracted, my body felt tight, contracted. The sense of “I” became small and central. When I was relaxed, or in flow, the opposite happened—the sense of self faded, and awareness felt large and diffuse.

I started tracking these shifts. Morning to evening. Calm to tense. Spacious to collapsed.

Every day held a thousand selves. And not one of them stuck around for long.

This was more than just noticing impermanence. It was the beginning of understanding anattā—that none of these self-states could rightly be called me or mine. They were arising and passing with conditions. And if that was true… who, exactly, was watching all this happen?

The Little Guy in My Head

One afternoon in meditation, while doing a practice from the Emptiness and Awareness chapter of Seeing That Frees, something strange happened.

I was tracking my experience of awareness—not the content, but the background. The thing that seemed to be aware.

And then I saw it: the little guy in my head. The one I unconsciously imagine lives behind my eyes, watching the movie of my life on an internal screen. He’s got monitors and speakers and probably a swivel chair.

Except… he wasn’t there.

Or rather, the space between the watcher and the screen collapsed. There was no distance. No gap. No separation between the one who sees and what is seen.

Just perception, just sound, just sensation.

And in that moment, awareness stopped being inside anything. It had no center. No edges. It didn’t belong to anyone. It just was.

This wasn’t some dramatic enlightenment moment. More like a perceptual shift. But it was enough to shake something loose.

Because I realized: the little guy had always been assumed. I’d never even thought to question him. And now that he was gone, things were… quieter. Lighter. Less lonely.

John Peacock once said in a talk: “It is lonely being an I.”

He was right. And for a few moments, I wasn’t one.

Letting It Be

One technique that helped open this up was deceptively simple: let it be.

When I noticed contraction or aversion in meditation, instead of analyzing it or trying to make it go away, I just allowed it. Stayed with it. Let it be part of the landscape.

This opened into deeper states of samādhi than I’d experienced before. And more interestingly, it gave me a way to feel into the characteristic of dukkha.

Dukkha started to show up not just as pain or dissatisfaction, but as contraction. As narrowing. As collapse around an identity or a view. And letting it be—not fixing it, not transcending it, just making space for it—gave it room to breathe. Sometimes that was all it needed to release.

This isn’t a glamorous insight. It’s not something to write down in a notebook and underline. But it’s practice. And it works.

A Self Made of Reactions

One last realization from this phase:

So much of what I had thought of as “me” was actually just the aggregates doing their thing.

In one sit, I watched an itch appear.

First came the sensation (viññāṇa). Then the recognition of “itch” (saññā). Then the unpleasant tone (vedanā). Then the impulse to act (saṅkhāra), which arrived as a little mental image of me scratching it.

It was all so fast. But none of it was “me.” It was just a process. A loop. An impersonal chain.

And yet, outside of meditation, I would have said: “I had an itch, so I scratched it.”

But who is this “I”, exactly?

And what happens when we stop assuming it’s in charge?

There were no final answers in this phase. Just cracks. Just questions.

But through those cracks, light started to filter in.

What had felt stable began to flicker. And in that flickering, space.

Not a void. Not a loss.

A freedom I hadn’t known I was missing.

This is Part II of the “Unbuilding the Self” series. View the series overview, read Part I: From Reductionism to Raw Experience, or continue to Part III: Living in the Middle.

Part I: From Reductionism to Raw Experience

2025-04-01T00:00:00-05:00

This is Part I of a series exploring meditation and emptiness. For context, you may want to read the series introduction first.

“Everything, all the way down, is models.” — a sentence I wrote in my journal that I’m still unpacking

I didn’t come to meditation looking for emptiness.

I’ve actually been meditating since 2014. It’s been a part of my life for over a decade now—but in the time leading up to this journey, my practice had gone a little stagnant. What had once been intimate and curious had become mechanical. Full of dead inertia. Sit, breathe, hope something shifts. Nothing really did.

Then I found Rob Burbea.

Or more accurately, a friend shared a link to one of Rob’s jhāna retreats. I clicked play while folding laundry. And within minutes, I was completely taken.

There was something in the way he spoke—unhurried, lyrical, precise—that bypassed the usual filters. He didn’t talk like a man trying to explain something. He talked like someone inviting me to look again. I trusted him immediately.

So I started practicing again. First samādhi, then some gentle jhāna work. His talks became part of my days—guiding walks, anchoring mornings, accompanying moments of friction. Titles like Selflessness and a Life of Love, Unbinding the World, Attitude, Effort, Attainment, and View became familiar companions.

Eventually, I picked up Seeing That Frees, the one book Rob wrote before he died. I wanted more of this—more of that.

But when I started reading, I hit a wall.

The Book That Bounced Off Me

I made it a few chapters in and stalled out.

The writing felt dense. The arguments abstract. Nothing landed. I found myself re-reading the same paragraph five times, then putting the book down and staring out the window. It wasn’t making contact.

My first thought was: maybe this book is beyond me. Maybe I wasn’t practiced enough to receive what it was pointing to.

But something in me knew this wasn’t a book to give up on.

So when I came across a yearlong study program based around Seeing That Frees—complete with monthly lectures, guided readings, sutta study, and group journaling—I signed up almost immediately.

The idea was simple: sit with the teachings over twelve months, and reflect. Slowly, deliberately. Let the thing sink in.

A Spark on the Body, a Grain in the Eye

There’s a line from Düdjom Lingpa’s The Foolish Dharma of an Idiot Clothed in Mud and Feathers that struck me like lightning:

“Without procrastinating until tomorrow and the next day, arouse a sense of urgency, as if a spark landed on your body or a grain of sand fell in your eye.”

It stopped me cold.

Because the truth is: it’s a miracle I’m even here. That I have a mind capable of reflection, a life that supports practice, teachings I can access, and a sangha I can learn with.

This doesn’t happen by accident.

And it won’t last forever.

This moment—this exact moment—might be the only chance I get. That realization didn’t make me panic. It made me still.

A kind of quiet resolve settled in: Don’t wait. Don’t squander this. Practice like something matters.

Armed with this urgency, I began the program. This post is the first of those reflections—the beginning of a process I could already feel was going to change me in ways I didn’t understand yet.

Everything is Models (Even That Statement)

I have a PhD in astrophysics. I spent years building mathematical models of galaxies—trying to simulate how they form, how they change, how they behave across incomprehensible spans of time.

And that meant I lived and breathed reductionism. It was in my bones.

You break a system down into parts. You isolate variables. You simplify the real so you can do the math. You make models.

And somewhere in the back of your mind, you still believe—however humbly—that if you go deep enough, if you keep zooming in, you’ll hit something solid. The bottom layer. The real real.

I used to say, only half-jokingly: “All models are wrong, but some are useful.”

And then I started practicing with emptiness.

I still remember the moment from Seeing That Frees—a line that casually pointed out that even reductionist materialism is just another view. Not truth. Not the world. Just a lens.

It broke something open.

“Everything, all the way down, is models.”

I remember writing that line in my journal and then staring at it for a long time.

“There is no magical level below which the models are any more real than what I was building. Protons? Models. Quarks? Models. Gravity? A model.”

The thought didn’t frighten me. It didn’t even really surprise me. But it unhooked something. Something I hadn’t realized was binding me.

It wasn’t a rejection of science. It was a deeper application of its humility. A kind of full-circle realization: that even the frameworks I used to decode reality were just more helpful fictions. Useful—but still fictions.

There was no ground. Just view layered on view.

The Described, Not the Description

One of the first things I started to notice in practice, once I began this study in earnest, was how quickly my mind would leap from experience to narration.

If something interesting happened in meditation—a curious sensation, a spacious moment, a flicker of stillness—my thinking mind would immediately begin describing it. Usually in the voice I imagined using to explain it to my group.

It was subtle, but it pulled me out of the experience every time.

Eventually, a phrase arose that became a kind of touchstone:

The described, not the description.

Every time I noticed myself analyzing, packaging, preparing a narrative, I came back. To the sensation itself. To the breath. To the unfiltered moment.

This simple phrase started to shift how I meditated. I stopped trying to turn experience into insight and just let the experience unfold. Sometimes it was ordinary. Sometimes beautiful. Sometimes painful. But it was finally mine, not my commentary on it.

And that, as it turns out, made all the difference.

The First Crack in the Mirror

That was the beginning.

Not just of a course, or a reading plan, but of something much harder to name. A shift in orientation. A soft unraveling of assumptions I didn’t know I had.

What came next surprised me.

I started looking at the idea of a “self.” At how I define myself, contradict myself, cling to certain traits and hide from others. And slowly, that sense of identity—so central, so stable—started to flicker.

There was even a moment when the “little guy in my head”—the one I thought was me—just disappeared.

But that’s a story for another time.

This is Part I of the “Unbuilding the Self” series. View the series overview, or continue to Part II: The Vanishing Self.

Opening the Black Box: Helping Faculty and Staff Understand an AI-Driven World

2025-01-06T00:00:00-06:00

I’m excited to share that my latest article, “Opening the Black Box: Helping Faculty and Staff Understand an AI-Driven World”, is now published in University Business. Here it is in full:

Artificial intelligence is transforming higher education, influencing recruitment, research and classroom experiences. But while we tend to talk about AI as if it’s a single, monolithic technology, that’s of course not the case.

AI encompasses a huge variety of algorithms that range from simple, rule-based systems to hugely complex, deep-learning models. On one end of this spectrum are simple algorithms whose logic can be easily understood and communicated—such as those that sort emails. On the other end are impenetrable “black box” systems, whose decision-making processes are so intricate that even their creators struggle to explain how they arrive at conclusions.

Today, colleges and universities too often tend to treat all AI tools as the same, in part because too many people making decisions about the technology do not know exactly how it works. Institutions are delegating human tasks to machines while not maintaining control or oversight, and these automated—and often unexplainable—decisions are affecting students’ lives in ways both good and bad.

Institutions introducing AI have an obligation to deploy it responsibly and explain its workings clearly to faculty, staff and students. Simply put, institutional decision-making around AI should center on “explainability”—that is, ensuring that the way a given AI tool works is transparent and understandable to humans.

When we talk about AI, it’s immediately apparent that there’s a tension between the quality of its output and its explainability. Most AI tools draw on a series of algorithms that instruct machines to analyze data, perform tasks and improve over time in ways that resemble human intelligence. It’s relatively simple to describe an automated process that makes music and movie recommendations based on an individual’s previously expressed preferences, but it’s nearly impossible to explain the inner workings of an algorithm that churns out essays after scanning billions of pages of written work. The decision-making processes of these deep-learning systems are locked away in an impenetrable black box. Even the builders of these large language models lack the keys that allow them entry.

What makes AI so powerful is that it’s able to sift through enormous amounts of digital data much faster than humans. But while AI that is confined and structured can be useful in higher education settings, AI that is unpredictable, unmanageable and unexplainable has the potential to lead institutions down a dangerous road where accountability goes missing. Some academic integrity tools, for instance, have falsely flagged student work as AI-generated, leading some schools to switch off these detection capabilities altogether.

Of course, appropriate uses of black box AI do exist in the classroom. Deep-learning systems powered by generative AI are great for coaching students to write better without doing the writing for them. Because the ultimate decision of whether to accept or reject the writing advice is up to the student, explainability isn’t as crucial—it’s more important in this case to judge the quality of advice than to be able to explain how the algorithm came up with its suggestions.

Yet as black box AI proliferates in higher education, explainability has emerged not just as a sound philosophical approach to institutional AI usage. It’s increasingly becoming a legal mandate as well. The EU’s 2024 AI Act requires transparency, risk assessment and human oversight for high-risk AI applications. In the United States, seven states and New York City have recently enacted or proposed new laws that regulate AI’s use in consequential decisions that affect employment, finances and education.

Institutions should approach AI with intentionality, prioritizing tools that are transparent, ethical and aligned with their educational goals. Explainability when AI is making consequential decisions should be a non-negotiable criterion, ensuring that decision-making processes are clear and understandable to all stakeholders. AI should enhance the human parts of teaching by automating repetitive tasks, rather than attempting to replace human judgment. Selecting and deploying AI is not just a technological choice but an ethical responsibility. By making such commitments, institutions can harness it to empower instructors and students while upholding the trust and integrity that are the foundation of education.

Institutions should hold themselves accountable for consequential decisions that affect students’ lives and not simply hand them over to machines. The first step in responsible AI use is explaining it clearly, ensuring institutions and students understand its impact.

Read it in its original context here

Visualizing My Energy Usage

2024-02-12T00:00:00-06:00

At the end of 2022, I got a solar system installed on my home. Outside of feeling good that we’re doing our little bit to help the environment (and save ourselves some money at the same time), I was excited to dive into the numbers

How much electricity I was generating?
How much I was using? What is using it?
How much do I give back to the grid?

My solar system is an Enphase, it has a nice app for looking at my numbers, but happily it also has a pretty solid API that I can use to pull down my data. I wrote a little Python script that pulls down my system’s data in 15 minute increments (generation, usage and net flow into or out of the grid), and ran this to collect data for the whole of 2023.

When We Generate Electricity

Each pixel here is an individual 15 minute increment. The color of the pixel represents the amount of electricity generated in that 15 minute period. The x-axis is the day of the year, and the y-axis is the time of day.

Rather obviously, the system generates most electricity in the middle of the day, and in summer and the dark vertical stripes correspond to very cloudy or snowy days. One thing I liked about this plot is that it’s very easy to see the days getting longer and shorter as the year progresses, and the little jumps in spring and fall when we change the clocks.

When we Use Electricity

This heatmap is similar to the one above, but is now showing when we use electricity. The color of the pixel represents the amount of electricity used in that 15 minute period.

The things that stand out to me on this chart are:

Our biggest electricity hog by far is the AC, we see lots of vertical dark stripes throughout the summer as the AC kicks on and off.
In August we purchased an electric car, and you can see the car charging in the evenings. We initially charged it on a Level 1 charger, so you see long vertical stripes throughout the evening as it charged overnight.
Towards the end of the year, we started charging the car on a Level 2 charger, and you can see the charging time drop off significantly (the few dark spots all the way on the right)

Net Electricity Usage

Same as above, but now showing when we sent electricity to the grid, or consumed it. Throughout the year we had produced slightly less than we used, and our two largest consumers of electricity were the AC and car.

Next Steps

This exercise taught me a lot about our electricity use, and informed what I plan to do to the house during 2024. Over time, we plan to fully electrify the house, which means replacing the remaining gas applicances (oven, water heater, dryer, furnace) with electric ones, so our electricity usage is only going to go up.

We already use slightly more than we generate, so I’m going to have to think about increasing our home’s efficiency, likely with a home energy audit. I don’t really have the appetite to install more solar panels right now!

Header photo courtesy of Anders J on Unsplash.