Two weeks left to contribute to the 2024 Spring Pan-Canadian Digital Learning Survey

The invitation below is from the good folks at that Canadian Digital Learning Research Association (Disclosure: I’m a member of CDLRA and prior to leaving Canada, I was member of the board of directors).


The CDLRA’s Spring Pan-Canadian Digital Learning Survey is open until May 31st.

The purpose of the 2024 Pan-Canadian Digital Learning Survey is to explore critical issues in digital learning and to assess the impacts of the COVID-19 pandemic on digital learning at publicly funded post-secondary institutions in Canada. The survey will ask you to share your personal perspective and will take approximately 10 minutes to complete. The primary objective of the research is to provide institutional leaders and key interest groups in Canadian higher education with valuable information as they develop institutional strategies.

If you work at a post-secondary institution in Canada, you are eligible to take the survey.

Click here to participate in the survey now!

Topics covered in the 2024 Spring Survey include digital learning trends (including Generative AI), attitudes and preferences toward technology, challenges related to digital learning, and feelings about the future. You do not need to be an expert in digital learning to participate. Whatever your experience level with technology may be, we want to hear from you!

More information about the project and ethics approval is available here.

New publication: How do Canadian Faculty Members Imagine Future Teaching and Learning Modalities?

What do future learning environments look like? Is online learning “the new normal?” Or, are we back to the “old normal?” What does the “new normal” look like? Never mind concepts of “normal,”… what do learners and faculty imagine future learning environments, technologies, and modalities looking like? Colleagues and I completed and are planning a series of studies around these ideas, bringing together threads in our research that examines online learning, emerging technologies, challenges facing higher education, and speculative methods. We recently published one of these and I am sharing the pre-print below.

When I prompted ChatGPT to generate an image depicting this paper it generated the image below. This image provides an interesting juxtaposition to our findings, because our findings highlight the relative persistence of the status quo and reveal a lack of more radical futures.

Here’s the paper: Veletsianos, G., Johnson, N., & Houlden, S. (in press). How do Canadian Faculty Members Imagine Future Teaching and Learning Modalities? Educational Technology Research & Development. The final version is available at but here is a public pre-print version.


This study, originally prompted by the impact of the COVID-19 pandemic on educational practices, examined Canadian faculty members’ expectations of teaching and learning modalities in the year 2026. Employing a speculative methodology and thematic analysis, interview responses of 34 faculty members led to the construction of three hypothetical scenarios for future teaching and learning modalities: a hybrid work model, a high tech and flexible learning model, and a pre-pandemic status quo model. In contrast to radical education futures described in the literature, the findings do not depart significantly from dominant modes of teaching and learning. Nevertheless, these findings offer insights into the expectations that Canadian faculty members have with respect to future teaching and learning modalities, the contextual issues and concerns that they face, the use of speculative methodologies in educational technology research, and the potential impacts remote learning trends have on the future of education in Canada.

OpEd: 5 questions schools and universities should ask before they purchase AI tech products

I wrote the op ed below for The Conversation and I am republishing it here for posterity, under their Creative Commons license. Here’s the original article.


5 questions schools and universities should ask before they purchase AI tech products

Every few years, an emerging technology shows up at the doorstep of schools and universities promising to transform education. The most recent? Technologies and apps that include or are powered by generative artificial intelligence, also known as GenAI.

These technologies are sold on the potential they hold for education. For example, Khan Academy’s founder opened his 2023 Ted Talk by arguing that “we’re at the cusp of using AI for probably the biggest positive transformation that education has ever seen.”

‘How AI Could Save (Not Destroy) Education’

As optimistic as these visions of the future may be, the realities of educational technology over the past few decades have not lived up to their promises. Rigorous investigations of technology after technology – from mechanical machines to computers, from mobile devices to massive open online courses, or MOOCs – have identified the ongoing failures of technology to transform education.

Yet, educational technology evangelists forget, remain unaware or simply do not care. Or they may be overly optimistic that the next new technology will be different than before.

When vendors and startups pitch their AI-powered products to schools and universities, educators, administrators, parents, taxpayers and others ought to be asking questions guided by past lessons before making purchasing decisions.

As a longtime researcher who examines new technology in education, here are five questions I believe should be answered before school officials purchase any technology, app or platform that relies on AI.

1. Which educational problem does the product solve?

One of the most important questions that educators ought to be asking is whether the technology makes a real difference in the lives of learners and teachers. Is the technology a solution to a specific problem or is it a solution in search of a problem?

To make this concrete, consider the following: Imagine procuring a product that uses GenAI to answer course-related questions. Is this product solving an identified need, or is it being introduced to the environment simply because it can now provide this function? To answer such questions, schools and universities ought to conduct needs analyses, which can help them identify their most pressing concerns.

2. Is there evidence that a product works?

Compelling evidence of the effect of GenAI products on educational outcomes does not yet exist. This leads some researchers to encourage education policymakers to put off buying products until such evidence arises. Others suggest relying on whether the product’s design is grounded in foundational research.

Unfortunately, a central source for product information and evaluation does not exist, which means that the onus of assessing products falls on the consumer. My recommendation is to consider a pre-GenAI recommendation: Ask vendors to provide independent and third-party studies of their products, but use multiple means for assessing the effectiveness of a product. This includes reports from peers and primary evidence.

Do not settle for reports that describe the potential benefits of GenAI – what you’re really after is what actually happens when the specific app or tool is used by teachers and students on the ground. Be on the lookout for unsubstantiated claims.

3. Did educators and students help develop the product?

Oftentimes, there is a “divide between what entrepreneurs build and educators need.” This leads to products divorced from the realities of teaching and learning.

For example, one shortcoming of the One Laptop Per Child program – an ambitious program that sought to put small, cheap but sturdy laptops in the hands of children from families of lesser means – is that the laptops were designed for idealized younger versions of the developers themselves, not so much the children who were actually using them.

Some researchers have recognized this divide and have developed initiatives in which entrepreneurs and educators work together to improve educational technology products.

Questions to ask vendors might be: In what ways were educators and learners included? How did their input influence the final product? What were their major concerns and how were those concerns addressed? Were they representative of the various groups of students who might use these tools, including in terms of age, gender, race, ethnicity and socioeconomic background?

4. What educational beliefs shape this product?

Educational technology is rarely neutral. It is designed by people, and people have beliefs, experiences, ideologies and biases that shape the technologies they develop.

It is important for educational technology products to support the kinds of learning environments that educators aspire for their students. Questions to ask include: What pedagogical principles guide this product? What particular kinds of learning does it support or discourage? You do not need to settle for generalities, such as a theory of learning or cognition.

5. Does the product level the playing field?

Finally, people ought to ask how a product addresses educational inequities. Is this technology going to help reduce the learning gaps between different groups of learners? Or is it one that aids some learners – often those who are already successful or privileged – but not others? Is it adopting an asset-based or a deficit-based approach to addressing inequities?

Educational technology vendors and startups may not have answers to all of these questions. But they should still be asked and considered. Answers could lead to improved products.The Conversation


more on erasure and edtech

Last week I wrote a post on erasure and edtech, and this morning I saw that Stephen Downes has replied.

He writes that he “can’t verify whether Audrey Watters ever wrote this, because a Google search doesn’t turn it up.” Fair. I added a link to the original post, but here it is, as well.

Stephen also writes that the Woolf whitepaper discussed didn’t actually vanish, as he can find a copy through the Internet archive. My original post included a link to a copy (second paragraph here, linked from the original), so as to be clear that the whitepaper isn’t gone as in “no one can ever find it.” It vanished as in: “it’s no longer prominent, visible, accessible, and readily available.” And certainly, Internet sleuthing, given time, effort, skill, and some knowledge about the thing you’re looking for may yield evidence of it, though your mileage might vary.

One way to read “vanish” is to do what Stephen does, which is to zoom in and ask a literal question: Is the paper available somewhere? Another way is to zoom out, and ask: Have there been attempts to erase, rewrite, and reframe histories of edtech (e.g., through practices like removing references and ignoring critiques)? That’s how I understand erasure to work.

New paper: Treating AI as a real psychological other

In a recent talk, Punya Mishra claimed that “whether we like it or not we will start treating these bots as a if they are a psychological real other.” There’s quite a lot of evidence from social psychology going back to the 1990’s that humans consistently (and unconsciously) treat computers as social actors. This paradigm has been further refined in recent years, but overall there’s evidence that we do treat technologies in social ways (e.g., by being polite).

In a paper that we published this month, we show that learners imagine their interactions with an AI as abiding by social processes, including encompassing issues such as respect, honesty, and trust. Equally importantly, this finding isn’t uniform. At times learners imagine AI as a tool/object that can be used in the service of learning, while other times they imagine AI as a subject as one who has agency and possibly some kind of internal subjectivity.

Here’s the paper

Veletsianos, G., Houlden, S., & Johnson, N. (in press). Is Artificial Intelligence in education an object or a subject? Evidence from a story completion exercise on learner-AI interactions. Tech Trends. The final version is available at but here is a public pre-print version.


Much of the literature on artificial intelligence (AI) in education imagines AI as a tool in the service of teaching and learning. Is such a one-way relationship all that exists between AI and learners? In this paper we report on a thematic analysis of 92 participant responses to a story completion exercise which asked them to describe a classroom agreement between an AI instructor and a learner twenty years into the future. Using a relational theoretical framework, we find that the classroom agreements between AI and learners that participants produced encompassed elements of education, boundaries, affordances, and social conventions. These findings suggest that the ways learners relate to AI vary. Some learners relate to AI as an object, others relate to AI as a subject, and some relate to AI both as an object and a subject. These results invite a deeper engagement with the ways in which learners might relate to AI and the kinds of ethics and social protocols that such relations suggest.


Edtech history, erasure, udacity, and blockchain

This thought in Audrey’s newsletter (update: link added March 30th) caught my attention, and encouraged me to share a related story.

 [Rose Eveleth] notes how hard it can be to tell a history when you try to trace a story to its primary sources and you simply cannot find the origin, the source. (I have been thinking a lot about this in light of last week’s Udacity news. So much of “the digital” has already been scrubbed from the web. The Wired story where Sebastian Thrun claimed that his startup would be one of ten universities left in the world? It’s gone. Many of the interviews he did where he said other ridiculous things about ed-tech – gone. What does this mean for those who will try to write future histories of ed-tech? Or, no doubt, of tech in general?) Erasure.


Remember how blockchain was going to revolutionize education? Ok, let’s get into the weeds of a related idea and how most everything that happened around it has also disappeared from the web.

One way through which blockchain was going to revolutionize education was through the development of education apps and software running on the blockchain. Around 2017, Initial Coin Offerings (ICOs) were the means through which to raise money to build those apps. An ICO was the cryptocurrency equivalent of an initial public offering. A company would offer people a new cryptocurrency token in exchange for funds to launch the company. The token would then provide some utility for ICO holders relating to the app/software (e.g., you could exchange it for courses, or for study sessions, or hold on to it hoping that its value would increase and resell, etc). The basic idea idea here was crowdfunding, and a paper published in the Harvard International Law Journal estimates that contributions to ICO’s exceeded $50bn by 2019. The Wikipedia ICO page includes more background.

A number of these ICOs focused on education. Companies/individuals/friends* would create a website and produce a whitepaper describing their product. Whitepapers varied, but they typically described the problem to be solved, the blockchain-grounded edtech solution they offered, use cases, the team behind the project, a roadmap, and the token sale/model.

To give you a sense of the edtech claims included in one of those whitepapers:

“The vision is the groundbreaking disruption of the old education industry and all of its branches. The following points are initial use cases which [coin] can provide … Users pay with [coins] on every major e-learning platform for courses and other content they have passed or consumed… Institutions can get rid of their old and heavy documented certification process by having it all digitalized, organized, governed and issued by the [coin] technology.”

I was entertaining an ethnographic project at the time, and collected a few whitepapers. For a qualitative researcher, those whitepapers were a treasure trove of information. But, looking online, they’re largely scrubbed, gone, erased. In some cases, ICO’s founders’ LinkedIn profiles were scrubbed and online communities surrounding the projects disappeared, even as early as ICOs didn’t raise the millions they were hoping for.

Some of you following this space might remember Woolf, the “world’s first blockchain university” launched by Oxford academics. And you might also remember that, like other edtech projects, it “pivoted.” See Martin Weller’s writing and David Gerard’s writing on this. Like so many others, the whitepaper describing the vision, the impending disruption of higher ed through a particular form of edtech, is gone. David kept a copy of that whitepaper, and I have copies of a couple of whitepapers from other ventures. But, by and large, that evidence is gone. I get it. Scammers scam, honest companies pivot, the two aren’t the same, and reputation management is a thing. But, I hope that this short post serves as a small reminder to someone in the future that grandiose claims around educational technology aren’t new. And perhaps, just perhaps, at a time of grandiose claims around AI in education, there are some lessons here.



Open Access fees are exorbitant

After signed another publishing agreement, and I was, once again, taken aback by the exorbitant OA fees that publishers charge.

Publishing open access with us (gold OA) lets you share and re-use your article immediately after publication.

The article processing charge (APC) to publish an article open access in Educational technology research and development is:

Article processing charge (excluding local taxes)
£2,290.00 / $3,290.00 / €2,590.00

Some organisations will pay some or all of your APC.

If you want to publish subscription, instead of open access, there will be an option to do that in the following steps.

I know, I know, we probably shouldn’t have submitted to journal that isn’t gold and free OA by default, *but* the system is structured in such ways that my junior co-authors would benefit from being published in this journal.

While not a solution to this problem, it’s worth noting the terms in the publishing agreement around sharing the article. This is in the terms:

The Assignee grants to the Author (i) the right to make the Accepted Manuscript available on their own personal, self-maintained website immediately on acceptance.

This is the approach that I use for nearly all my papers, but it’s worth remembering that what this really does is suggest an individual solution to a systemic problem, which will do little to solve the broader problem of lack of access to research.

There are other statements in the terms around placing one’s article in an institutional repository, but author self-archiving is generally the first and immediate option available to individuals. And perhaps google scholar will index the author’s personal website, making the article available, as shown below. Google scholar’s approach of identifying articles and placing publicly-available versions in search results is a systemic solution to the problem. Unpaywall is similar in that respect.


[To be clear: this post isn’t about ETR&D. It’s about the publishers & the publishing system]

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