Imagined as a rousing political speech, with patriotic music slowly swelling in the background.

Colleagues, I know many of you are excited about NotebookLM, especially that uncannily almost-human podcast feature. We upload our readings, videos, and professional documents, then receive instant synthesis supporting multimodality and differentiated instruction. But I want us to consider what’s happening to our professional expertise when we adopt this tool—or any LLM-based assistant. We’re witnessing the remediation of educational expertise itself, transforming teachers from knowledge-holders into knowledge-brokers. NotebookLM stands out for grounding its responses in uploaded materials, lending its outputs an authority that masks their mediation.

To understand what’s at stake, let me introduce a concept from media studies: remediation. NotebookLM remediates the entire research apparatus of teaching—our file cabinets, OneDrive folders, annotated textbooks, and accumulated professional wisdom. Bolter and Grusin (2000) argue that remediation occurs through networks of formal, material, and social practices:

Formally, it remediates the academic literature review, the planning notebook, even Socratic dialogue; but promises “complete and comprehensive access to information” while obscuring the interpretive labor that transforms information into knowledge (Papacharissi, 2015).

Materially, it replaces physical artifacts of teaching expertise (marked-up curriculum guides, annotated student work, scribbles in margins) with algorithmic processes that appear transparent through source citations yet are hidden behind algorithmic choices. NotebookLM produces what Bolter and Grusin (2000) describe as hypermediacy (visible layers of mediation like source links, formats, AI voices) that paradoxically create a sense of immediacy and authority rather than critique.

Socially, it remediates us as expert practitioners. When we upload materials and receive instant analysis, our professional authority shifts from knowing to prompting—a different kind of expertise entirely.

Goodbye Inquiry, Hello Output

Linguist Adam Aleksic (2025) argues that “truly knowing an answer requires struggling with uncertainty.” Consider planning a unit on New France in Canadian history—a unit Manitoba students often struggle to find relevant. Traditionally, this required understanding primary sources, synthesizing across texts, connecting to standards, curating materials, anticipating misconceptions, designing meaningful assessment.

NotebookLM generates all of this in seconds. But as Aleksic describes, “with each additional abstraction from uncertainty, the easier it is to find answers, and the more confident those answers sound.” The tool produces seeming pedagogical expertise with the “aura of truth, objectivity, and accuracy” that danah boyd and Kate Crawford (2012) identify in Big Data mythology.

Yet can we explain why these particular connections matter? In philosophical terms: do we know what NotebookLM claims, or merely believe what it tells us?

The Question Behind the Question

Aleksic describes how “the lost ritual of asking has collapsed the meaning of the question in the first place.” When we can instantly generate unit materials, we never wrestle with fundamental questions: Why teach about New France? What should students understand? How does this connect to their lived experiences?

These aren’t questions NotebookLM can answer. They require what Haraway calls “critical, reflexive relation to our own practices” (as cited in Papacharissi, 2015). The tool can synthesize curriculum documents but cannot interrogate why we chose those documents, what we’re unconsciously prioritizing, or whose perspectives remain absent.

As Aleksic (2025) writes, “figuring out which question to ask is more important than the answer itself.” But NotebookLM’s efficiency makes all questions appear equivalent. We’re “drowning in a sea of answers, forgetting how to ask the right questions.”

Papacharissi (2015) captures this perfectly: AI outputs “oscillate between the unbearable lightness of information and the impossible gravitas of knowledge.” NotebookLM offers comprehensive information access but cannot deliver genuine pedagogical knowledge; the heavy weight of knowing that emerges only through sustained engagement with uncertainty.

Colleagues, I’m not asking us to abandon NotebookLM, but let’s use it differently. Treat its outputs as another text to interrogate, not authoritative synthesis. Our students need us to model what it means to genuinely know, not merely retrieve.

References

Aleksic, A. (2025, December 3). the importance of not knowing. Substack.com; The Etymology Nerd. https://etymology.substack.com/p/the-importance-of-not-knowing

Bolter, J. D., & Grusin, R. (2000). Remediation : Understanding new media. MIT Press.

Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662–679. https://doi.org/10.1080/1369118X.2012.678878

Papacharissi, Z. (2015). The unbearable lightness of information and the impossible gravitas of knowledge: Big Data and the makings of a digital orality. Media, Culture & Society, 37(7), 1095–1100. https://doi.org/10.1177/0163443715594103

Everything interacts. Always. We shape it. It shapes us. It’s all a big soup.

As a teacher-librarian, I’m constantly making decisions about which databases to subscribe to, which search tools to recommend, which encyclopedias to point students toward. These decisions often get framed as “neutral” by just providing access to information, offering students “the right resources.” But are they?

This question started nagging at me during IP 2 where I analyzed software encyclopedias through McLuhan’s tetrad and Actor-Network Theory. I decided to test something simple: I searched for two controversial topics across different encyclopedia subscriptions our division provides to students. The results weren’t just different—they were fundamentally different.

I sat there staring at two browser windows, and something clicked: this wasn’t a bug. This was a feature. Each platform was enacting a specific epistemology, a particular idea of what knowledge is. And my choice (as a librarian, and as someone who shapes student access to information) wasn’t neutral at all. I was choosing between worlds, while selling the guise of neutrality.

Why start with search?

When it came time to get to brass tacks on this assignment, I knew I needed an entry point that was practical. Not abstract. Not Barad discussing quantum entanglement — even though it’s fascinating.

Because it seems to me like if we want people to think outside of the box, we need them to realize that the tools they hardly think of as technological have been quietly organizing how knowledge appears to us for a very long time. They’re quietly working in the background; and their output exposes what’s going on behind the scenes. I think this is what makes them a great place to start unpacking the complexity of ideas behind new materialism.

How the elements came together

So I designed a professional learning activity: choose a heated topic, search it in three different tools (Google, Wikipedia, TikTok), and compare what appears. Then unpack: How does each tool assemble knowledge?

I think the session would ultimately take about two hours to work through with a group, but could probably be done in an hour and a half. I have embedded audio files into the presentation with my speakers notes, but have also linked them here if you would rather read them. My presentation slides are directly below.

If you’re an educational technology specialist, a teacher, an administrator or if you make decisions about which tools students use, which platforms teachers adopt, which systems organize learning, I’m inviting you to do something simple:

Pick a controversial topic. Search it in three different places. Compare what appears.

Then ask: What differences did this technology create?

It’s not a complicated activity. But I think it’s a critical and worthwhile one.

Because once you see how Google, Wikipedia, and TikTok assemble knowledge differently, you can’t unsee it. And that’s where the real work begins.

Not in finding the “right” tool. Not in establishing “best practices.” But in developing the literacy to read how tools shape what we can know, and the responsibility to choose—and keep questioning our choices—accordingly.

An assignment in which I didn’t quite follow the instructions properly, but came away with a greater understanding because of it. This video was made with the help of Adobe Podcasts and additional images and text were added in CapCut.

References

Black, E. (2001). IBM and the Holocaust : The strategic alliance between Nazi Germany and America’s most powerful corporation. Dialog Press.

Franklin, U. (1989, November 6). The real world of technology: Part 1 [Lecture]. CBC Massey Lectures. https://www.cbc.ca/radio/ideas/the-1989-cbc-massey-lectures-the-real-world-of-technology-1.2946845

Franklin, U. (1990). The real world of technology. House of Anansi Press.

Illich, I., & Sanders, B. (1988). ABC: The alphabetization of the popular mind. York University Press.

Wikipedia Contributors. (2021, December 20). Ursula Franklin. Wikipedia. https://en.wikipedia.org/wiki/Ursula_Franklin

a study of @etymologynerd

When I read this assignment outline, I immediately thought of @etymologynerd. Adam Aleksic’s posts are uniquely meta. He explains the history of our spoken and printed words and shows how that history is shaped by the media we use. In doing so, he invites viewers to understand how we’re shaped by language, how language shapes social media, how social media reshapes us and our language, how we shape the media itself – and how it all comes together to capture our attention. His videos use the same rhetorical hooks and algorithmic tricks that keep people scrolling, but he also exposes those mechanisms, breaking down the walls of manipulation to make them visible.

Aleksic is a Gen Z Harvard linguistics graduate. As a high school student, he started an etymology blog and became a prolific Reddit poster, learning how to game a much simpler algorithm than we face today (Peterson, 2025). After earning his BA in Linguistics, he shifted into short-form video in 2023. By 2024 many of his videos regularly surpassed one million views. Today he has over 800,000 followers on TikTok and more than one million on Instagram, where his posts often circulate beyond each platform’s walls. I first encountered his “dolphin language” series while not even using TikTok.

Although he keeps his following list small, the accounts he does follow map onto his niche interests: other linguistics educators (@linguisticdiscovery, @lingonardi), academically adjacent creators (@magic.c8.ball, @astro_alexandra), and quirky internet-culture archivists (@depthsofwikipedia). These affiliations reflect the tone that defines his work: grounded and absurd.

That blend is especially visible in the dolphin videos, which offer a helpful starting point for analysis (Fig. 1).

Bolter and Grusin (2000) define a medium as something that remediates. Or rather, it refashions the social significance and techniques of previous media forms into something new (p. 45). We see what would traditionally be a written linguistics exercise transformed into a multimodal micro-lesson and performance. The linguistic concepts in the video are remediated through absurdist humor: a parody that simultaneously entertains and instructs. It is a modern take on the joke about comma usage between the phrases ‘Let’s eat, Grandma’ and ‘Let’s eat Grandma’. Despite the absurdity, the underlying lesson remains intact: languages are built from arbitrary rules. This “theory by parody” makes complex linguistic concepts legible to a broader audience by turning exposition into performance. The humor functions pedagogically: Aleksic embodies the rule rather than explaining it. Ultimately, the clip demonstrates Bolter and Grusin’s logics of immediacy and hypermediacy. Immediacy appears in the illusion of direct communication and the sense of transparency created through voice, sound, and viewer address, while hypermediacy emerges in our constant awareness of the medium itself—through captions, editing cuts, and the dolphin-language grammar chart superimposed behind him (pp. 71, 81–82).

That being said, no one is going to watch this video and come away able to create their own conlang. This is a hook rather than a full lesson—an entry point that sparks wonder rather than mastery. It is engagement through wonder, a perfect example of affective pedagogy. The brevity and interactivity turn learning into an invitation; the comment section, duets, and follow-ups become the extended classroom. It makes linguistic systems accessible to people who might never encounter academic linguistics. In this sense, it self-remediates and thus translates scholarly explanation into algorithmically optimized curiosity. One has to wonder, to borrow from Latour’s Actor–Network Theory, how these technological prescriptions (such as brevity, performance, and engagement incentives) will ripple out into the future of learning, shaping not only how knowledge circulates but how attention itself is disciplined (Latour, 1988).

Aleksic is aware of the attentional demands placed upon him by the medium. Like many, his videos are known for their fast pace and use of an influencer accent. In his recent book Algospeak, he describes it as a form of uptalk in which there is a rising tone on a stressed syllable with continued high tones until the completion of speech. This emphasis serves to hold a viewer’s attention (Aleksic, 2025). It is speech, optimized for the algorithm.

This is an example of how retention-rate metrics shape the TikTok ecosystem. In fact, “retention rate” appears on nine out of the book’s 220 pages (Aleksic, 2025, p. 242). He explicitly references his use of trending terminology as video frames because of how it impacts engagement metrics, stating that many topics interest him—but that he knows they will not bring the same viewers as trending ones. This leads to videos like the following, which harness trending brainrot and memetic terminology to introduce the concept of the use-mention distinction (Fig. 2).

Instead of choosing to focus on academic and abstract examples, @etymologynerd moves into popular and concrete spaces. The algorithm itself begins to co-author meaning: language is a performance, a signal of in-group belonging, and an object designed to optimize metrics. In this video, linguistic meaning shifts from semantic work (referring to something in the world) to social work (signaling a shared cultural context). The reference is the joke, and thus it becomes meta-communication about referencing the reference. His delivery shows his awareness of how the algorithm scripts his choices, but the content itself shows what the platform is scripting for all of us. TikTok remediates language from a communication tool into a participation badge – a way to show that you belong to the network. Educational theory becomes a theory of belonging, not just understanding.

But TikTok also dis-enables important dimensions of learning. The rapid pacing, short runtime, and endless For You scroll reward quick consumption rather than slow thinking. Reflection is costly on a platform where attention is the primary currency. Videos can be difficult to relocate later unless a viewer follows the creator, which makes deeper engagement fragile and fleeting. Misinterpretation rises when nuance is compressed into punchlines and edits.

Citation (Fig. 3) poses a distinct challenge. Aleksic makes a noticeable effort to visually cite academic sources on screen, but those references disappear as quickly as they appear. They cannot be clicked, traced, or peer-verified in the moment. The platform’s ephemerality means that sources, while visible, remain challenging to verify. In this way, TikTok pushes creators to monetize attention and charisma over ideas. The medium privileges credibility-as-aesthetic over credibility-as-evidence. It also leads to potential misuse and abuse as poor-faith actors use them to build credibility.

In my estimation, as an educational tool, TikTok is mostly hook, but no line. It can lead to new understanding, and Aleksic’s work feels like a kind of harm-reduction approach to academic thinking: a way to keep deeper ideas alive within the feed. As Latour notes, technologies prescribe actions and competencies in advance, imagining a user who will behave as scripted. Yet real users often comply partially, resisting or reshaping those prescriptions. He describes how networks of aligned set-ups form what Waddington called a chreod—a necessary path that silently channels users toward the expected behavior (Latour, 1988, p. 308). TikTok’s chreod rewards speed, humor, outrage, and referential belonging. Real learners may diverge, but the medium continually nudges them back onto that narrow track of attention. To land a big fish, we need the hook of attention and the line of sustained learning. Hopefully, too much of one doesn’t destroy the other.

References

Aleksic, A. [@etymologynerd]. (2023). #conlang #language #linguistics #dolphin #harvard #greenscreen [Video]. In TikTok. https://www.tiktok.com/@etymologynerd/video/7544794226249321741

Aleksic, A. [@etymologynerd]. (2024). words become funny because they are words that are funny #etymology #linguistics #language #hawktuah #skibidi #philosophy #logic #semantics [Video]. In TikTok. https://www.tiktok.com/@etymologynerd/video/7421288562651319582

Aleksic, A. [@etymologynerd]. (2025). BOO 👻 #semiotics #culture #halloween #costume #sociology [Video]. In TikTok. https://www.tiktok.com/@etymologynerd/video/7562712621909019917

Aleksic, A. (2025). Algospeak. Knopf.

Bolter, J. D., & Grusin, R. (2000). Remediation : Understanding new media. MIT Press.

Johnson, J. (1988). Mixing humans and nonhumans together: The sociology of a door-closer. Social Problems, 35(3), 298–310. https://doi.org/10.1525/sp.1988.35.3.03a00070

Peterson, A.H. (Host). (2025, September 10). How algorithms are changing the way that we talk [Audio podcast episode]. In Culture Study Podcast. https://podcasts.apple.com/ca/podcast/how-algorithms-are-changing-the-way-we-speak/id1718662839?i=1000725863919

Analyzing the ‘Bridge Encyclopedia’ through McLuhan’s Tetrad and Latour’s Actor Network Theory

I can’t be the only person who was obsessed with Microsoft Encarta’s Mindmaze game. I had always yearned for a set of Encyclopedias… but when that free disc comes with your computer, I shortly stopped asking for them.