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
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.
Here’s a link to my assignment this week. I wanted to embed it directly into my blog, but some features weren’t working that way, and you deserve the full experience. You’ll find my references embedded in my presentation.
I went a bit overboard this week, something I may not be able to sustain long term – but I had a lot of fun putting this together! The animated video and clipart are courtesy of Adobe Express.
It should be known that the astronaut is just a preset character in the animate from audio function in Adobe Express, but how serendipitous. Little guy looks a lot like me!
Sutton, R. S. (2020). John McCarthy’s definition of intelligence. Journal of Artificial General Intelligence, 11(2), 66–67. https://doi.org/10.2478/jagi-2020-0003
