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
Condensing text has always struck me as one of gen-AI’s genuine strengths—especially with passages only a page or two long. Because colleagues and I constantly wrestle with teaching complex ideas to readers at wildly different levels, I decided to run a little experiment.
I grabbed a section from an open Canadian-history textbook on Winnipeg’s water supply and its century-long impact on Shoal Lake First Nation. (Copyright dodged!) Then I sent the same passage through two “grade-five level” text-levelling tools. After the fun I had last week coding responses (sadly I am not being sarcastic) I did a bit of the same here. The results were fascinating. My hunch is that these tools perform better in tightly structured subjects like science or math, but I wanted to see how they’d handle a topic that matters deeply in Winnipeg and which structures of power and colonial legacy have significant impact.
In a perfect world you’d use an AI system that lets you spell out the key concepts that must survive the rewrite, but that raises the stakes for prompt quality. For this assignment I stuck with true paste-and-go tools—the kind that lure in brand-new or still-skeptical AI users.
I’ve bundled my heuristic, the side-by-side outputs, and a brief analysis in a Genially presentation (link below). Make sure to use the show interactive elements button in the top right corner, so that you don’t miss any interactive content. I’d love to hear your thoughts.
And what can I figure out from what it doesn’t say?
I went a bit overboard.
I started looking at two LLMs and then I just kept on adding one more to the list and then I ended up with a 20+ minute video and hours worth of unused footage and a look at how Meta AI, ChatGPT (v. o3), Deepseek, and Copilot handle the same question.
Fun Fact: I used the AI features in CapCut for the emoji captions!
Regardless of my overkill, it was fun. I’ve attached a couple of extra things aside from the video itself.
An interactive couple of graphs that I made in Canva so that you can see some of the data I pulled from my analysis. The charts are interactive, so click around a bit -the labels in the white menu bar under the titles allow you to see one set of information at a time.
I have to say, I’m tempted to strip the model names from the responses and my excel sheet with the records and upload it into Chat and Deepseek to see what they notice. Should I do it?
References
Coleman, B. (2021). Technology of The Surround. Catalyst: Feminism, Theory, Technoscience, 7(2), 1–21.
Crawford, K. (2021). Atlas of AI: Power, Politics and the Planetary Costs of Artificial Intelligence. Yale University Press.
The Learning Environment evaluation rubric was an interesting assignment for me, as I joined a group focused on post-secondary education, despite all of my teaching experience being at the middle and high school levels. Specifically choosing Canadian Memorial Chiropractic College (CMCC) as our organization provided an excellent opportunity to explore how technology could be leveraged in a program that relies heavily on in-person and hands-on practicum. As I joked in one of our meetings this week—I don’t think I would be willing to go to a chiropractor who was trained only virtually! As such, it became clear that the platform we recommended needed to complement, not replace, face-to-face and practical training.
I had a lot of fun collaborating to develop the rubric for this assignment and weaving together elements from both the SECTIONS and CITE models to create a more holistic overview – what we have entitled the LEARNERS Institutional Needs Assessment Scale and the LEARNERS Learning Tool Assessment Calculator. While the SECTIONS model offers a clear lens for classroom integration, the CITE framework (aimed at global development) brings in valuable perspectives around equity and community benefit—something I believe should be considered in a Canadian context as well. That said, the CITE model can be difficult to navigate, which led us to focus on identifying overlaps and building something new that worked for our scenario. You can see the Needs Assessment Scale here, and the Assessment Calculator here.
One key realization for me during this process was the difference between equity and accessibility in evaluating a technology’s appropriateness. Coming from a public school background, I often prioritize equitable access across diverse devices and connectivity levels. However, in the context of CMCC, with a smaller and more homogenous student body, these concerns were not as high on the institutional priority list. This highlighted how institutional context truly shapes which values are seen as essential—and which are optional.
This project also gave me the opportunity to explore two LMS platforms I hadn’t previously encountered: Docebo and Google Classroom. Docebo, which is used largely in corporate settings, did not sit well with me. Its marketing—“There is no reason we can’t quadruple revenue in the next two years… Docebo has allowed us to create an education engine that’s very plug-and-play and very scalable” (Docebo, 2025)—left me wondering whether education was being reduced to a one-size-fits-all revenue model. That’s obviously beyond the scope of our rubric, but it left a lasting impression (and not a good one). That being said, it offered almost all of the bells and whistles you could be looking for 🙂
Google Classroom is a more familiar and affordable option, but I worry that its low cost is being subsidized through user data collection. The recent bankruptcy of 23andMe (Allyn, 2024), and the concerns about what might happen to user data post-collapse, made me reflect on the fragility of digital trust. While Google Classroom receives a passing grade from Common Sense Media, even their evaluation notes several red flags around data use.
The following two images (Common Sense Media, 2022) show concerns re: data in the Google Classroom ecosystem.
By the end of this assignment, I found myself increasingly skeptical that a truly ethical, learner-centered LMS exists. This exercise sharpened my ability to evaluate tools critically—but it also reinforced my concerns about the broader systems behind them.
(Or to those of you not currently obsessed with working your way through the Duolingo German course—good day my readers.)
For those of you new to my blog, willkommen! I’m Morgan, a secondary school teacher-librarian and current student in the Masters of Educational Technology program through the University of British Columbia. I’m just starting ETEC 524, Learning Technologies: Selection, Design and Application, and this seems like the perfect excuse to dust off my poor, neglected blog. If you scroll through past posts, you’ll get a sense of my background—but here’s the Coles notes version.
This is my fifteenth-year teaching in the public school system in Manitoba, mostly at the middle and high school levels. On paper, I think I was supposed to be a history teacher, but I’ve done a little bit of everything—core classrooms and upper-middle humanities. Seven years ago, I was asked to move into a teacher-librarian role, and I haven’t looked back since. As this blog shows, this is my second program at UBC; my first was the LIBE Diploma, which gave me excellent training in running a well-rounded library program. Librarianing is the best. I get to buy books, collaborate with teachers, curate across multimodalities, nag people about copyright (not gonna lie, my least favourite part), and help guide future-focused pedagogy. I considered a Masters in Library Studies but felt that this program better fit my interests, the needs of our space, and where I see the future of libraries heading.
For this course, I’m interested in bridging the gap between healthy communities and the overwhelming amount of digital content at our fingertips. How do I help students not just find information, but apply it to their own lives? Moving between in-person and virtual spaces is part of daily life, but how do we make that shift feel practical for learners? Maybe it’s the creep of middle age making me critical, but many students seem increasingly disillusioned with school. How do we build learning environments where students critically engage with tech beyond academic checkboxes? And how do I ensure I’m using technology for true redefinition (Puentedura, 2009) rather than using resource-heavy tools for tasks that could just as easily be done on paper? As a librarian, I see the aftermath of a lot of poorly planned tech investments, and I don’t want what I design to add to the mess.
Best golden grill, best fluffy texture, best unusual fillings. One could learn much, mastering the perfect pancake.
What I hope to develop is a course where students choose a demonstrable skill—something they truly want to learn—and build it over a semester. They would set goals, manage their time, reflect on their progress, tackle challenges, and share their learning with others. For example, I might choose to learn how to make the perfect pancake (a worthy pursuit, in my opinion). I’d network with cooks, test recipes, reflect on my process, and document what I learn so I could share it with others. The course would wrap up with a community celebration where students showcase their skills. It’s still just the glimmer of an idea, but I’m hopeful this class will help me turn it into something practical and worth running.
The challenge, of course, is designing something meaningful and manageable when students will pick skills I know nothing about—and that’s kind of the point. I won’t be the expert, but I can build structures to help them find reliable sources, network and connect with experts, and reflect on their learning. That’s where I hope this course will help me grow—giving me the tools to better select and apply technologies that support diverse, self-directed learning without turning the course into a chaotic free-for-all.
This course feels like the right fit to help me move that idea forward. The frameworks we’ll explore—like SAMR and SECTIONS (Bates, 2014)—can help me evaluate whether my design choices are meaningful or just adding extra steps. The focus on learning environments, interaction, and engagement will help me balance student independence with community-building. The work on assessment will push me to clarify what success looks like when every student is learning something different. Later modules on content creation, multimodal presentation, and communication will give me practical tools to support students in sharing their learning in ways that go beyond the traditional slideshow or essay. The final assignments are perfectly timed to help me produce both a structured unit and a tech integration proposal—directly aligned with my course concept.
In short, I hope this course will help me move from intention to implementation—grounding my ideas in research-backed frameworks and best practices, and giving me peer and instructor feedback on my course design. Specifically, I hope to strengthen my ability to design learning environments that foster student agency, apply digital tools purposefully, develop process-based assessment strategies, and support students in sharing their learning in meaningful ways.
To do this, I’ll need access to examples of blended learning structures, readings on assessment for self-directed learning, and opportunities to experiment with digital tools for documenting learning. I also hope to learn from my peers—many of whom bring different teaching contexts and insights that could help me refine my thinking.
By the end of our time together, I know this course will help me take a meaningful step forward in becoming a digital-age teaching professional—someone who not only navigates the evolving world of educational technology but helps students do the same, critically, creatively, and ethically.
An accurate representation of me during this 13 hours.
Check out this link to take a sneak peak at 13 hours of my life, on what I thought would be a pretty chill, unstructured day. This time, featuring a Pig in Lipstick.
This week I chose to focus my attention on Media Convergence, and you can view my MindMap on the topic here! Below, you can watch as Bovine Morgan takes you on a journey through his many thoughts about the topic.
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
