Yesterday I handed in my last assignments for my last course in my Master of Educational Technology degree through the University of British Columbia. While I won’t graduate until May, it marks the end of an era – just shy of six years I have been plucking away at courses through UBC, first with my post-Bacc in Teacher-Librarianship, and then this program. Honestly, it probably won’t sink in until this January when the next semester begins and I am not juggling the demands of my day job and academia (and supervising the Varsity Boys Volleyball team, haha).
I am thankful for the learning of these years; the ideas and architecture of teaching, learning, and technology that have been made visible through thinkers and makers with skill much greater than my own. At least once a week I am reminded of the meme posted below, and when I pull back from my own unique situation, lenses, and experiences, I continue to be humbled by all of the things I know I don’t know, and even more so the things that I don’t know that I don’t know.
Random internet meme saved on my phone, original source unknown; thought about constantly.
One thing I wish I’d had throughout this degree — and especially in the final projects — was a small community of co-designers. Working full-time while studying part-time often meant creating in isolation or across time zones and distance, without the iterative conversations that sharpen ideas and reveal blind spots. Learning is ecological, and I felt the absence of that ecology at times. For anyone thinking about MET, I strongly encourage it, but especially for you to take the financial hit and take as many summer institute classes as you can. It was these experiences that really got me keen on the importance of hybridity. Even so, the work felt like a reminder that none of us should be designing the futures of education alone.
The more I learned, the more I realized that technology is never neutral — it always arrives embedded with assumptions, values, and power. I came into this program as a tech optimist, but leave it as a tech realist. This kind of program (both the T-L one and the Ed Tech one), would not have been possible to me given my geographic location. Such programs don’t exist here in Manitoba. This program was a gift — but one that came wrapped inside an increasingly precarious technological landscape. However, to borrow from the words of Cory Doctorow, the enshittification of technology has hollowed out some of my previous optimism. When so much of the truly equitizing and democratizing potential of technology is hidden behind paywalls and gradually whittled away even then for increasing add-ons you begin to wonder if the world-views of yourself and the architects of these applications are in alignment.
As a teacher-librarian trying to think carefully about ed tech, I see the role of libraries, both public, elementary/secondary school, and university as a solution to some of these challenges. This will require sustained public funding, and the (mostly) corporations that design these materials to subsidize access to ensure these technologies don’t become yet another divide between the have and have nots. It is not hard to imagine a future where the ability to participate in digital culture depends entirely on one’s ability to pay for the tools required to access it. Perhaps this is no different than inequities within print culture — but it still runs counter to the vision of education I was raised in, both as a student and later as a teacher.
I also hope that the notoriously slow-moving systems of curriculum-development and education are focused on what strikes me as the most crucial adjustment necessary of the LLM age – assessment. Our paradigm has shifted almost instantaneously, and our previous methods of assessing work at its conclusion feel unsuited for the times we are living in. Honestly, it was never suited – but it was easier, and for the students who were able to submit a polished product thanks to the assistance of a tutor, parent, or other support, their ‘knowing’ was never assured in the time before. I hazard to say that it was our biases about who specific learners are, their backgrounds, and what they look like (plus maybe the discomfort in challenging involved families) that made us look the other way. AI didn’t break assessment — it revealed what was already broken.
I used ChatGPT to create these (imperfect) graphs showing the shift to assessment that AI will necessitate. Pull the tab to the right to see how we’ve traditionally thought about product based assessment, and to the left to see my hypothesized AI assessment paradigm. Some day I will just make ones that more accurately represent my thinking (the AI Use label at the dip shouldn’t be there!)
While I worry that AI will mean increasingly high classroom numbers, I actually think that it calls for more teachers, more human mentorship, more conferencing and check-ins along the way – a necessity that is almost impossible in our current set-up. If AI teaches us anything, it’s that students don’t need us less — they need us differently.
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.”
It is not surprising that we are pulled to these tools – who has the time? Media scholar Zizi Papacharissi calls this tension ‘the unbearable lightness of information vs. the impossible gravitas of knowledge’ – and I feel that in my bones every Sunday night. (This meme was created with imgflip and supplemented with a screenshot of my own use of NotebookLM, plus other art from Canva)
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.
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.
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 influenceraccent. 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.
A light, but interesting read from a TikTok Linguist on how algorithms are shaping the way we speak.
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.
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
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.
Media ecology is the study of how media and technology function as environments that shape human perception, communication, and understanding. It looks not just at content, but at the structures and systems we build and are surrounded by, from language to smartphones, and how they shape what we can think, say, and do. Like an ecosystem, these media interact with one another and with us, constantly reshaping our cultural and intellectual “habitat.” Media ecology asks the right questions because it recognizes that anything that impacts our communication or interaction provides limits and biases to what is possible. Without recognizing the impact of these impositions we risk mistaking the shape of our media environment for the shape of reality itself.
This definition aligns with Strate’s (2000, as cited in Strate and Lum, 2020) argument that media ecology is a perspective; “a way of seeing”—that treats media as environments rather than just channels. Neil Postman (as cited in Lum, 2000) similarly calls media ecology “the study of the cultural consequences of media change that affects our social organization, cognitive habits, and political ideas” (p. 4). In other words, it is not enough to examine what media say; we must examine what they make possible and what they make difficult to imagine.
Strate and Lum (2000) situate media ecology within a tradition that is ecological, interdisciplinary, and activist, drawing on Patrick Geddes’ view that intellectuals must act as shapers of environments, not just observers (p. 60). This implies that media ecology cannot belong neatly to one discipline. It is necessarily intersectional, combining insights from communication studies, history, sociology, semiotics, and philosophy. Thinking ecologically means remembering that, as Miller (1989) puts it, “no living organism can be understood except in terms of the total environment in which it functioned” (as cited in Strate & Lum, 2000, p. 68).
Mumford’s historical schema illustrates how environments structure society over time. His three phases—eotechnic, paleotechnic, and neotechnic—mark shifts from renewable energy to industrial extraction to machine-human symbiosis, with each stage reorganizing labor, power, and social classes (Strate & Lum, 2000, pp. 63–65). These phases demonstrate that technological environments are not neutral: they define the “rules of the game” for entire cultures.
Lum (2000) further explains that media have both physical and symbolic dimensions, each with distinct biases. These biases can be temporal, spatial, sensory, political, social, metaphysical, or epistemological (p. 2). To do media ecology is to surface these biases and ask what forms of knowledge, attention, and relationship they enable—and which they foreclose. This is why media ecology “asks the right questions.” It does not merely catalog media; it interrogates how each medium privileges some possibilities and silences others.
Model of Educational Media Ecology
A still of my completed vision of educational media ecology.
References
Lum, C. M. K. (2000). Introduction: The intellectual roots of media ecology. New Jersey Journal of Communication, 8(1), 1–7. https://doi.org/10.1080/15456870009367375
Strate, L., & Lum, C. M. K. (2000). Lewis Mumford and the ecology of technics. New Jersey Journal of Communication, 8(1), 56–78. https://doi.org/10.1080/15456870009367379
Okay, so my video is a bit longer than 5 minutes, but I swear there’s a reason! My video tour is presented as a mock news segment with Artie Smarts, an animated robot newscaster. I went with this format to keep things conversational and fun while still walking through the project in detail, drawing on Mayer’s (2009) personalization principle to show that professional learning can be engaging as well as practical. The playful opening, a few jokes, and the closing segment were all part of setting that tone and holding attention — though they do push the video slightly past the suggested runtime. I was having a lot of fun making it (using a combination of Adobe Express, Apple Clips, and CapCut), and I hope that comes through when you watch.
Sometimes you plan something and follow your itinerary to the letter; other times, despite your best intentions, another path calls to you and you end up going in a completely different direction. Such was the case with my learning throughout ETEC 524. In hindsight, it’s probably not surprising that my main work ended up focusing on AI and educators; it is a topic I’ve been almost obsessively engaged with for the past two years. But this was not where I initially set out to go in May.
My original goal was to outline and begin developing a hybrid online/classroom course for Grade 11/12 students, centred on skill development and mastery in an area of their choice. I’ve long wanted to create space for students who are not drawn to more traditional academic programming to pursue a deep dive into something meaningful to them. However, as the course unfolded, I shifted toward building a professional development module for educators in my school division. This shift came partly from recognizing the immediate usefulness of such a resource, and partly from seeing an opportunity to help teachers design more accessible, less cluttered Edsby environments for their students.
When I compared my initial and final projects, I noticed that both aimed at the same underlying challenge: addressing crucial shortcomings in current pedagogical models. The difference was that the PD module would allow me to act on these ideas sooner and in a context where I could model thoughtful technology use for colleagues as well as students. That reframing not only changed the direction of my final assignment, but also reframed how I now think about my role as a teacher-librarian — not just supporting student learning directly, but shaping the digital spaces and professional practices that make deeper learning possible.
I suppose we always live in ‘interesting times’, but the phrase seems particularly apropos of our current moment. Large Language Model tools, economic models that incentivize the capture of our attention and data, and political dialogue are currently shaking the foundation of what it means to learn, and therefore what it means to teach. I leave this course with many great resources to strengthen my toolbox, but also quite a few existential questions about where we move on from here.
In terms of resources, several were especially important in shaping my thinking throughout the course. I’m always one for an acronym, and Bates’ (2015) SECTIONS model and its clear breakdown of considerations for technology selection was a very helpful frame. I still struggle with it in some ways, but only because I see that it may lead organizations to prioritize immediate cost over sustainability. Of course, this is a systemic issue. Planned obsolescence, increasing energy demands, and security and privacy issues create a scenario where tech requires frequent updating and replacement, while the majority of companies that have significant interest in developing hardware and software run on a model of infinite profit and growth. This results in devices where parts can’t be swapped out, or where our data is traded like a commodity. Fighting against this means using open source or older technologies that require more in-house tech support, and often significantly less ease of use. I can’t help but worry that we’re paying out of our future for ease of use today.
Outcome development and assessment was another area of growth for me. Given my role in the public school system, I am much more familiar with assessing by outcomes that have been provided to me, rather than creating those outcomes myself. My part one of my second assignment showed my weakness in that area. Assessing for PD learning rather than an academic course was something that I hadn’t really thought about. In most of my school-based PD learning experience, assessment seems to boil down to your name being on the attendance sheet, or (for online modules), a series of automatically graded multiple choice and true/false questions that staff often did as a group. But we know that simply being in the room isn’t learning something, and that tests generally only measure lower-order skills (Mazur, 2013). As such, Mazur’s suggestions to improve assessment by mimicking real life, focusing on feedback not ranking, and assessing skills rather than content were especially useful, and I tried to mindfully incorporate them into the activities I planned in my unit. His fourth point about resolving the coach/judge conflict is tricky for online learning especially, as instructors are often spread more thinly. For older users, peer and self-assessment can be a useful workaround.
Media literacy (particularly around images, and also video)has also emerged for me as an essential skill for both students and educators. Yousman’s (2016) discussion of speed versus depth, appearances versus analysis, and the emotional pull of images resonates strongly with my concerns about online-only learning. In a digital environment where learners are often inundated with visuals, the skill to pause, question, and analyze becomes a prerequisite for critical engagement.
Ultimately, this course has left me feeling more positive about the state of in-person teaching, and with significant question marks about the long-term sustainability of online-only-asynchronous education — especially given generative AI’s rise. Many edtech tools, whether devices, applications, or Learning Management Systems, allow for holistic application of UDL principles into a blended learning environment; a fully inclusive environment that allows for multiple means of engagement, representation, and action & expression (Bourlova, 2025). But assessing learning without a real connection to the learner in an online-only environment becomes increasingly challenging in a world where almost anything can be made for you in seconds. When we are digitally siloed, it becomes far too easy to “other” the entire world. Bringing learning back to community, even in a hybrid format, becomes a moral as well as a pedagogical imperative.
This is why I leave this course particularly invigorated to see how learning technologies can be applied to hybrid environments, especially in the realm of professional development. When I plan professional development in schools, I often hear how great it is to have bespoke learning that is relevant, personalized, and even a bit fun when topics are difficult. I know, though, that these sessions are limited in their universal design, as some individuals need more time to process, different modalities, or repeated exposure to key ideas. What if this kind of work can be done at my divisional level to plan PD that reaches more of us, on locally relevant topics, and what if that trickles into our classrooms? One next step I see is reaching out to upper administration to share my vision of hybrid-learning PD using our Edsby system. I don’t know of anyone within my division with this specific background and training, and I wonder if I might be able to shape a role for myself in this space. McErlean’s (2018) work on interactive narratives also strikes me as especially relevant here — using immersion to engage participants while still controlling the delivery of key content. I think hybrid learning could benefit greatly from this balance.
I’m also especially interested in making Open Educational Resources that align with UDL standards. In creating accessible and Creative Commons-licensed resources, I can work toward reducing the paywall creep that has marked the shift from the open optimism of the Web 2.0 era to today’s increasingly commercialized edtech landscape. This work would not only address accessibility and equity concerns but also provide sustainable, adaptable materials that could serve both students and educators long after their initial creation. In my job as a teacher-librarian, I can promote the heck out of these resources to teachers; we don’t have to be in the pocket of big textbook anymore.
This course has reinforced for me that educational technology is at its best when it strengthens human connection, promotes equity, and cultivates critical engagement; not when it simply delivers content faster or more efficiently. The challenge, especially in “interesting times,” is to hold on to those values in the face of rapid change, commercialization, and the seductive ease of automation. My next steps (from advocating for hybrid, UDL-informed professional development to creating accessible OERs) are grounded in a belief that technology should expand possibilities for both teachers and learners, without locking us into closed systems or shallow engagement. The tools will keep changing, but the responsibility to use them thoughtfully remains the same.
Issa, T., & Isaias, P. (2022). Sustainable design : HCI, usability and environmental concerns. Springer.
Mayer, R. E. (2009). Multimedia learning (2nd ed.). Cambridge University Press.
McErlean, K. (2018). Interactive narrative. In Interactive narratives and transmedia storytelling: Creating immersive stories across new media platforms (pp. 120-151). New York: Routledge.
The New London Group. (1996). A pedagogy of multiliteracies: Designing social futures. Harvard Educational Review, 66(1), 60–93.
Yousman, B. (2016). The text and the image: Media literacy, pedagogy, and generational divides. In J. Frechette & R. Williams (Eds.), Media education for a digital generation (pp. 157-170).
AI Essentials for Educators; one step closer to reality
I love a good digital story—that’s the teacher‑librarian in me. So when Part II called for one, I went big. I split the module into three chunks: a gentle, non‑academic primer on LLM limitations; Shannon Vallor’s short talk on AI as a mirror; a practical on‑ramp via a choose‑your‑own‑adventure (CYOA) story; and then some hands on AI tool interaction. My audience (Senior Years teachers new to gen‑AI) was not going to read Crawford’s Atlas of AI or Noble’s Algorithms of Oppression, so I let Clippee do the ranting instead.
I actually started in Twine, but Edsby doesn’t play nicely with Twine embeds. Google Sites would have worked, but I wanted to model inside the LMS they already use. So I pivoted to Canva. That constraint forced me to prune eight branches down to four core scenarios. I think this was ultimately a blessing, because it sharpened the key myths I needed teachers to bump into. Guided by Bruner’s claim that “practice in discovering for oneself” makes knowledge more usable in problem solving (Bruner, 1961), the branching story lets teachers feel the pitfalls before I name them. In Bruner’s “hypothetical mode,” learners aren’t “bench‑bound listeners” but co‑constructors; every click in the story and every prompt revision in the lab puts them in that role.
Multimodality mattered too. The New London Group’s push for multiliteracies (1996) and UDL principles nudged me to balance text, images, and short audio clips. I recorded voices in CapCut (yes, shameless self‑promotion—I want invites to co‑teach CYOA projects). Vallor’s mirror metaphor (2024) shaped Clippee’s tone: he “magnifies” what the AI quietly distorted, echoing Crawford’s critique of data extraction and Noble’s warnings about encoded bias. But in a much more accessible way.
Try out Teacher’s AI Adventure!
Within the walls of my module, H5P’s paywall (thanks, D2L) pushed me to CurrikiStudio for the formative checks. That choice wasn’t just budget—Curriki is something teachers can actually replicate in their own Edsby pages tomorrow, without approvals or fees. Peer interaction is Edsby’s Achilles’ heel, so I farmed discussion to Padlet. It’s clunky to add another tool, but the final course task also lives on Padlet, so repeated exposure helps. I even seeded sample posts so no one stares at a blank board.
Overall, this module blends hands‑on pragmatism with just enough theory for what my audience needs: useful, not heavy.
References
Bruner, J. S. (1961). The act of discovery. Harvard Educational Review, 31(1), 21–32. Crawford, K. (2020). Atlas of AI. Yale University Press. New London Group. (1996). A pedagogy of multiliteracies: Designing social futures. Harvard Educational Review, 66(1), 60–92. Noble, S. U. (2018). Algorithms of oppression. NYU Press. Vallor, S. (2024). AI is a mirror of humanity [Video]. Institute of Art and Ideas.
Hey readers! I’ve just finished my first stab at Assignment 2 in my Learning Technologies: Selection, Design, and Application course. It has been a learning experience full of ups and downs. But I see something of utility shaping up. You can get a login link to my course sandbox on our course Canvas page (sorry internet lurkers, this one isn’t for you).
Platform Choice
I built the course in Edsby, our division’s Learning Management System. I knew this decision would impose limits—Edsby lacks several features common in other LMSs—but I welcomed the challenge of adapting those features within a familiar environment. Also, it’s sort of fun to find ways to work around limitations and problems 🙂
Designing the course as a divisional certificate PD offered a double benefit. Many teachers have never seen Edsby “from the student side,” so completing the course lets them experience its interface firsthand. That perspective shift—alongside activities such as embedded Padlets, polls, and streamlined content panels—forms a hidden curriculum in which participants learn both about large language models (LLMs) and about effective Edsby design. Knowing my audience includes colleagues who describe themselves as “not tech-savvy,” I recorded short, captioned tutorials for every unfamiliar action—changing a Padlet display name, uploading a file, finding copilot, etc.—so nobody is left guessing.
Assessment is intentionally lightweight but still purposeful. Every required Padlet activity and the final AI-analysis assignment is marked on a single pass/fail checklist: if all criteria are met the first time, the task is marked Complete; if anything is missing, I’ll return a brief note—usually within 48 hours—pinpointing what needs to be added or clarified. This approach models formative, mastery-oriented assessment, keeps marking manageable for me, and gives even tech-skeptical colleagues multiple low-stakes chances to succeed.
Challenges & Pivots
Problems surfaced quickly: Professional Development Groups in Edsby accept assignment submissions, yet those submissions vanish because PD groups aren’t linked to a gradebook. I pivoted to a student-course framework for this prototype and plan to share it as proof of concept for divisional staff learning.
Edsby’s main feed clutters fast and lacks threaded discussion, so I outsourced dialogue to Padlet. This aligns with Chickering & Ehrmann’s (1996) call for active learning and Bates’s (2015) three interaction types (learner–content, learner–teacher, learner–learner). The workaround—email notifications for every Padlet post—is clunky, but Padlet’s LTI integration could resolve that if I can get my IT to enable it. This is not something that will happen during the time we are in the course, but would be a great feature for other teachers in the future.
By confronting Edsby’s constraints head-on—and documenting practical pivots—I aim to model the same critical, creative mindset toward technology that the course has encouraged us to embrace so far.
Chickering, A., & Gamson, Z. (2001). Implementing the seven principles of good practice in undergraduate education: Technology as lever. Accounting Education News, Journal, Electronic. https://go.exlibris.link/N0tYMtWd
themarksey blog 