What now, brown cow?

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.

Why I Made Ed Tech Specialists Compare Search Results for My Professional Development Session on New Materialism

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.

What do LLMs tell me to worry about?

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.

  1. A link so that you can check out my original prompts, and the codes that I gave to them for my analysis
  2. 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. 

Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. New York University Press. https://doi.org/10.18574/9781479833641

Suchman, L. (2023). The uncontroversial ‘thingness’ of AI. Big Data & Society, 10(2) https://doi.org/10.1177/20539517231206794 

But Can You Use It?

Several old wooden wagon wheels leaning against a concrete wall.

What is usability? 

Perhaps it is simpler to view usability through the lens of a stone age technology. The wheel proliferated because it is infinitely usable. To borrow from Issa and Isaias’ usability criteria (2015, p. 33), the wheel was easily understood and adopted across various cultures (learnability). It could be adapted for use in many places (flexibility). When properly designed, it rarely failed (robustness). The wheel significantly reduced the effort required for transportation (efficiency), and its design was simple, effective, easily reproduced, and impossible to forget how to use (memorability). Small imperfections don’t usually effect its utility (error handling), and it made people’s lives better and easier (satisfaction). When we are designing tools for use, usability must be the end goal, or else we are building Rube Goldberg machines; complex machines that perform tasks in indirect and convoluted ways (Wikimedia Contributors, 2019) that are more of a puzzle and pastime for the designer than solutions to widely held problems or ways to improve quality of life. 

What about educational usability? 

From an educational lens several ideas are missing. Unlike the profit-driven motives of the free market, which often lead to excluding certain users in technology design, the education system prioritizes inclusivity, catering to diverse learning needs, and supporting both teaching and learning processes. When technologies have been designed for commercial usability rather than educational, educational outcomes and learning effectiveness are often sacrificed. Adding features necessary for ensuring support for different age groups, modes of learning, technological proficiency and integration with learning standards are not marketable in the same ways.

Data privacy is another concern. Individuals of legal age can consent to technology use and data terms, but schools must prioritize student data privacy, often facing higher costs for technology that adheres to these standards, unlike commercial tech subsidized through data sale (Canadian Centre for Cyber Security, 2023). Ultimately, one could define educational usability as technology that meets Issa and Isaias’ criteria on top of being ethically responsible and inclusive, which prioritizes the learning process, and recognizes the financial and security challenges that are unique to the educational field. 

When usability studies go wrong 

User-centred design is integral . Woolgar effectively argued that in his observed usability study, he saw the configuration of the user to the technology. This is problematic because it can lead to a notable mismatch between user needs and the technology, resulting in a product that is difficult to use, or doesn’t serve the problem it is intended to address. It also leads to potential user frustration and disengagement because they are forced to adapt to a system that doesn’t align with their natural behaviours, expectations, and motivations.  

One issue that stood out for me was the very close presence of testers during the process. The testers were physically in the space and verbally guided the users along the way, telling users when they could give up, or providing positive reinforcement to encourage useful behaviours like reading a manual (Woolgar, 1990, p. 85). Would users outside of this ecosystem persist in their use of the technology, and where would users genuinely struggle? Configuring the users muddies the water. 

Woolgar highlighted the insider/outsider contrast, with insiders like tech support often surprised by outsiders’ real-world use of technology, exemplified by simplified computer instructions posted in a school computer lab. (Woolgar, 1990, p. 72). The sheer depths of knowledge and experience of designers act as blinders to the everyday needs of average users, who may not share the same level of expertise or perspective. Perhaps a better process for usability testing would have helped create a device that was more intuitive. 

Usability over time 

Woolgar’s points on usability are particularly relevant when considering the DOS-based 286 computers he references in 1990, which, due to their novelty and hardware constraints, necessitated user adaptation and lacked key usability aspects like learnability and satisfaction set out by Issa and Isaias 25 years later. It is likely that in Woolgar’s case users legitimately needed to be configured. Now that technology is ubiquitous, and screen recording technology/keystroke-logs exist we can now take the lessons learned from Woolgar and apply them in a way that helps users configure the tech rather than vice versa. Testers no longer need to physically be in a room with those doing the testing, as we can gather helpful data virtually. In Woolgar’s case, usability studies were an end of process project to be completed shortly before heading to market, whereas Issa and Isaias frame usability evaluation as a recursive process of prototype releases; this is a significantly more proactive, user-centric approach. Ultimately, when read together these pieces highlight how important it is that our conceptions of usability do not remain static. 

References 

Canadian Centre for Cyber Security. (2023, August 4). Protecting your information and data when using applications- ITSAP.40.200. Canadian Centre for Cyber Security. https://www.cyber.gc.ca/en/protecting-your-information-and-data-when-using-applications-itsap40200 

Goldberg, R. (1931). Self-operating napkin [Comic]. In Wikimedia Commons. https://upload.wikimedia.org/wikipedia/commons/a/a9/Rube_Goldberg%27s_%22Self-Operating_Napkin%22_%28cropped%29.gif 

Issa, T., & Isaias, P. (2015). Usability and Human Computer Interaction (HCI). In Sustainable Design (pp. 19–36). https://doi.org/10.1007/978-1-4471-6753-2_2 

Wikipedia Contributors. (2019, April 10). Rube Goldberg machine. Wikipedia; Wikimedia Foundation. https://en.wikipedia.org/wiki/Rube_Goldberg_machine 

Woolgar, S. (1990). Configuring the user: The case of usability trials. The Sociological Review, 38(1_suppl), 58–99. https://doi.org/10.1111/j.1467-954x.1990.tb03349.x