Knowing about vs knowing

Why we now need to help students do both

For spring break, I’m on a southwestern road trip with my daughters. For an east coaster, the high desert has remained surreal every day. I’ve done drives like this before, but not for a decade, and images of them don’t do justice to the scale involved.

Monument Valley Navajo Tribal Park

Moving through these landscapes has got me thinking about the difference between knowing about and knowing, and why we now need to make sure students do both.

Knowing About

For my purposes here, I’m defining “knowing about” as having intellectual knowledge, conceptual understanding, or an abstract sense. Knowing about is primarily mental.

In the purpose-development and career-exploration program I run for juniors and seniors, I want students to know about the careers that interest them. That means using sites like O*Net to research degree requirements, salaries and employment trends. It also means watching TikTok videos showing a day in the life of that career. It also means networking with professionals in that field via LinkedIn and other social media to learn more about their experiences.

I encourage students to ask those professionals about the role AI plays in their field, and the role they see it playing in the future. Students need to know about that now too.

AI = Knowing About

In many ways, knowing about is what AI does best. It has ingested the corpus of human knowledge and so knows about seemingly everything. So far on this road trip I’ve asked it how many miles I have left if my specific rental car’s gas tank is half full, had it create an image of what an ancient inland sea in Nevada would’ve looked like overlaid on a photo taken out our car window, and used it to check if the Freedom Tower in Manhattan was indeed taller than a nearby mesa as my daughter insisted (it was).

AI’s seeming omniscience has left many of us in education disoriented and sometimes dispirited. In a sense, AI can now “do” what we sometimes thought school was. If school was for helping students know about the world, and AI knows more about the world than any student or teacher, what is school for now? Why do my daughters need classes in history or geography — or later, geology or anthropology — if AI can give them a personalized tutorial on those topics tied directly to the world out their window?

Indeed, I’ve found it useful to leverage AI’s seemingly inexhaustible store of information in class. In addition to having students use more traditional career research methods, I’ve also built many bots that guide students through personalized career exploration experiences. Students learn about various specific pathways in a field, or work through authentic ethical dilemmas in a field, or get support with field-specific projects. What AI knows about is good for students to know about too.

But school was never only about knowing about.

Knowing

In this context, I’m defining “knowing” as an experiential understanding, related to Michael Polanyi’s concept of tacit knowledge. While knowing about is primarily mental, knowing is primarily physical — learning that emerges directly from living.

In the drives and hikes of our last few days, my daughters and I have known the southwest, not just known about it. That’s part of the point of a trip like this.

That’s also why I try to ensure every student in the career exploration program gets a chance to live a career, not just learn about it. My annual goal is that every student takes at least one field trip related to their career interest, and ideally gets to experience the work of that field while they're there. With around 100 students in the program, we’re not at 100% yet for that goal, but we’re getting closer every year.

A few weeks ago, two seniors interested in marketing careers decided to visit local businesses near our school to see if they could offer their services. The aspiring marketers left school during my class for several days in a row, returning in time for lunch the next period. They walked to a variety of local shops and restaurants, offering to create free marketing campaigns for those establishments in exchange for the experience they’d gain.

The first day or two no one said yes, but they still came back excited, mostly because they had conquered the hurdle of actually going into a business to start a conversation with a stranger. Eventually, though, one local restaurant took them up on the offer. That was right before break, so when we get back they’ll create a social media campaign (using AI tools for support).

Those students now know what it’s like to start a cold conversation in real life, and they’ll soon know what it’s like to run a real marketing campaign. AI can’t teach them that.

AI ≠ Knowing

Almost by definition, AI can’t know. In the sense we’re using it here, knowing means having an embodied experience of the world and learning from that experience. While LLMs can see and hear (in a sense), they don’t even pretend to smell or touch. I find uploading images and screenshots to chats very useful and teach students how to do that themselves, but I’m not sure what uploading the sensation of evening wind in desert sun would even mean.

Despite the world models on the horizon, we are a long way from AI being in our world and therefore knowing like we know.

Schooling for knowing about and knowing

It can be tempting to decide, as some currently are, that school now needs to be about knowing, since AI can do all the knowing about. In this line of thinking, we need to make sure students “know about” just enough to pass outdated assessments. Once they’ve cleared that bar, the rest of their time can be reserved for knowing: projects, quests, trips, and other kinds of experiential learning.

I’m all for the knowing; that’s why I want every student I work with to have the same kind of experience as those aspiring marketers.

But in the AI era it can be tempting to throw out the baby of “knowing about” with the bathwater of school models that are suddenly anachronistic.

Knowing about will need to look different now, but it still needs to happen.

For one thing, students can’t maintain critical distance when using AI if they don’t know about anything besides what the AI tells them. Mike Caulfield’s SIFT method (which he’s now revising for the AI era) is based in part on the same premise. In addition, AI doesn’t actually know everything about whatever it’s explaining. It knows what’s in its training data — itself a fraught topic — and what it finds on the internet and then chooses to explain. As someone pointed out to me recently, just because AI consults 650 sources in its deep research doesn’t mean any of those sources are good ones.

Most importantly to me, though: we can’t let students believe that knowing about is something machines do, while knowing is what we do. Not only is that a dystopian contraction of what it means to be human, it’s also a recipe for knowing less overall.

Knowing relies on knowing about. Those marketing students had better conversations with those local business owners because they had spent the week beforehand studying successful social media marketing techniques used by restaurants. My daughters were laughing when they put their hands into the red sand in Arches National Park partly because of how the sand felt and also because they knew about how they would’ve been sitting underwater in this place at another time.

The suggestion that students can speedrun knowing about to get to knowing shortchanges both. Instead, we need to help students connect knowing about and knowing, creating a virtuous cycle in which one enriches the other and students become more enthusiastic about both.

What Whitman Knew

One of my favorite poems is Walt Whitman’s “When I Heard the Learn’d Astronomer.” Re-reading it this week, I realize it can help explain the one-sided school models now appearing and also help argue for the harder synthesis we need to pursue.

When I heard the learn’d astronomer,
When the proofs, the figures, were ranged in columns before me,
When I was shown the charts and diagrams, to add, divide, and measure them,
When I sitting heard the astronomer where he lectured with much applause in the lecture-room,
How soon unaccountable I became tired and sick,
Till rising and gliding out I wander’d off by myself,
In the mystical moist night-air, and from time to time,
Look’d up in perfect silence at the stars.

The speaker of the poem leaves an astronomy lecture focused on knowing about stars so that he can instead know the stars himself. The arrival of AI has enabled some school leaders to jettison the “knowing about” they already found outmoded in favor of the “knowing” they already wanted students to focus on. And they’re right that knowing about divorced from knowing is thin gruel.

But Whitman didn’t write a poem just about someone looking up at the night sky. He wrote a poem that contains both knowing about and knowing. And I’d argue he doesn’t end the poem rejecting knowing about in favor of knowing; he’s instead pointing out the insufficiency of one without the other. After all, Whitman the poet was balanced by Whitman the journalist and newspaper editor, and his work in each world enhanced the other.

Night Skies

As I finished writing this, I went onto the porch of our hotel cabin near Monument Valley, looked up for a while, and then took this photo.

I knew the stars looked beautiful. Gemini then told me the bright star on the right isn’t a star at all, it’s Jupiter making its way through the constellation Gemini. Knowing about that, I had an optical-illusion experience and suddenly saw that Jupiter was much closer to me than the stars scattered around the rest of the galaxy. I asked Gemini how much further away some of the stars were. The closest one in that part of the sky is 11 light years away, while some of the fainter ones are more than 1000 light years away. (Look at the photo above again with that in mind and see how it looks different now.)

For me, the sky takes on more than one dimension and so becomes a world unto itself. That’s the kind of world I want my students, and my daughters, to live in.