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October 19th, 2026

“The Wavicle, or wave-particle duality, suggests that all objects exhibit both wave and partical [sic] properties. Between observations as it evolves on its own, it behaves like a wave; distributed across space, exploring different intermixing paths to all possible destinations. However, when its location or speed is measured, it appears definite and concrete, like a particle. Its wave nature gives this measurement a curious property: the more certain we are about either speed or position, the more uncertain we become about the other. Uncertainty/doubt. Valuing both process and outcome.” (brown, 2017, p. 45)

As we embark on the fourth series of Western’s Generative AI challenge, the conversation around generative AI in education seems to have simultaneously changed drastically and not shifted at all. Generative AI continues to dominate the social and educational imagination, with continued conversations about academic integrity and concerns around cognitive erosion, renewed focus on the politics of intelligence, and the purpose and future of postsecondary education, now peppered with stories of rogue agents and the rise of Big Tech doomerism.

What I have realised through this series and other ongoing work focused on generative AI is that multiple, seemingly contradictory, things can be true simultaneously. A useful concept for understanding what I mean, with the caveat that I do not have a background in quantum physics, is the idea of wavicles. Wavicles, a term first used by Arthur Eddington (1928), simultaneously exhibit properties of both a wave and a particle. A particle is something that is tangible and discrete. A wave is something that is dynamic, changeable, and constantly in motion. I first came across the concept of wavicles in the work of adrienne maree brown (2017), also not a physicist, who provides this concept as a way of acknowledging that something can hold multiple truths at once.

Although Eddington introduced the term wavicles in the 1920s, he was describing the already theorized phenomena of wave-particle duality, which posits that a fundamental property of matter is that it can exhibit both wave-like and particle-like properties, depending on how it is measured.  Karen Barad extends the idea of wave-particle duality in her theory of agential realism (2007), which suggests that reality emerges only through entanglements, including with the tools through which that reality is observed or measured. In describing a two-slit experiment conducted by Niels Bohr, Barad observes that matter exhibits particle-like behaviour when observed with one tool and exhibits wave-like behaviour when observed with a different tool, thus “the nature of the observed phenomenon changes with corresponding changes in the apparatus” (Barad, 2007, p. 106).

So, why am I talking about waves, particles, and quantum physics in a post about generative AI and education? At the risk of further oversimplification, depending on the lens through which we observe it, generative AI can rationally be positioned in similarly contradictory ways. For example, if the goal of education is to get high grades, using generative AI to complete assessments is a logical practice. However, if the goal of education is to independently develop knowledge and skills, undiscerning generative AI use may be detrimental. When the apparatus (in this case, the purpose of education) changes, the observed phenomenon changes.

The Generative AI Challenge asks that we consider the specific lens through which we are engaging with matters of concern related to AI in education and think about how that lens shapes what we believe is real, significant, or desirable. What this also highlights is the importance of scope and scale. When we engage in critical discussions around generative AI impact in education, should we be focused on the AI-use policy for a specific assignment or a single instance of academic misconduct? Or should we be focused on the long-term implications of AI adoption at scale and what is being normalised in education through each decision?

Over the past 3 series of Western’s Generative AI challenge, we’ve seen the paradox of AI unfold in real time. We’ve had challengers focus on collective and individual agency, the possibilities of AI in science education, the environmental impact of generative AI, the euro-centric lens through which AI ethics is often defined, and the perils of human-AI relationships. The challenge has never been about deciding which of these truths is most true. It’s always been about understanding how the lenses through which we explore generative AI narratives and possibilities shape what we deem as true, important, and desirable.

In this upcoming series, we will engage with new matters of concern and with previous matters of concern through new lenses. Our all new challengers will touch upon AI Literacy, how technology mediates experiences of space, the economics of being human, new assessment paradigms, simulations of humanity, and more.  

If you are looking for absolutes, you won’t find them here. The Generative AI Challenge is a place of wavicles: possibilities, contradictions, and discomforts. The challenge is to simultaneously sit with each moment of reflection (the particles) and move through them all (the waves) to see how you emerge on the other side.

The Challenge

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References

Barad, K. M. (2007). Meeting the universe halfway: quantum physics and the entanglement of matter and meaning. Duke University Press. https://doi.org/10.1515/9780822388128

brown, adrienne maree. (2017). Emergent strategy: shaping change, changing worlds. AK Press.

Eddington, A. S. (1928). The nature of the physical world. Macmillan.

Photo by Brecht Corbeel on Unsplash

Disclosure

I did not intentionally engage with any generative AI tool in drafting this post. However, I used Google to find relevant news articles, Omni to find academic sources, and Microsoft Word to draft the article; all technologies that I suspect have some amount of integration with AI technologies. I sourced the post image from Unsplash, which provided no information on generative AI use for making images. I cannot honestly claim that this post was not shaped by generative AI in some way, but can state that any AI influence was unintentional.

Your Challenger: Dani Dilkes

Dani is an Educational Developer with the Centre for Teaching and Learning (CTL) at Western University. She specializes in critical digital pedagogy, accessibility, and approaches to inclusive teaching and learning. She recently completed her PhD at the University of Toronto where her research focused on how how to reimagine educational designs and practices to be more inclusive in times of great disruption, including the emergence of new technologies like generative AI. Dani has approached the design and facilitation of generative AI programming for the CTL holistically by balancing technological, pedagogical, ethical, and affective considerations of Generative AI use across diverse perspectives.