What have I been up to during these past three years since the explosive emergence of the AI wave in 2022? Diligently gathering data, then tirelessly navigating the muddy waters of data analysis and theoretical exploration. By the time I graduate with my doctorate in 2025, the field of AI in education will be, if not containing enough books to make a cow sweat carrying them (汗牛充栋, hàn niú chōng dòng 01), it will certainly be a dazzling collection (琳琅满目, lín láng mǎn mù 02). As a “latecomer,” I’m eager to find a “hot topic” as an entry point to join this conversation.
I’m surrounded by all sorts of discussions. Among the ideas that particularly resonate with me are the automation of education, high-order thinking, and AI literacy. In addition, I also began reading articles related to teacher education as a part of a research project. But the moment I truly felt a sense of “authenticity” from my reading was when I started focusing on “The Ontology of AI.” This phrase comes from the work of Bill Cope, Mary Kalantzis, and Duane Searsmith (2021). It refers to focusing on the way in which AI systems name, classify, and structure knowledge and data.
This phrase brought me an “aha moment,” perfectly encapsulating the insights I had gained from a series of articles I browsed before reading Cope’s work. These articles were shared by my friend and mentor, Professor Jennifer Jenson at the University of British Columbia, and I will gradually share the reflections they sparked in me across several sub-themes under this topic.
I quickly began to appreciate one of the benefits of reading these articles, even just skimming them (with the help of AI): they effectively filter out the noise from the vast amount of research, motivating me to return to the fundamental questions: What is the essence of education? What is the essence of AI? And what kind of connection can be forged between the two? This feeling is something I hadn’t experienced when pursuing “trendy” readings driven by my own interests or other research projects.
To the extension of my reading so far, the ontology of AI includes but not limited to the following themes: The First Principle of Education-ChatGPT The Thingness of AI “Hard” Part of AI: Mineral and Hardware “Soft” Part of AI: Algorithm and Data “Human” Part of AI: Labour, Classification and Labeling Sociological perspective of AI: Culture, Politics, Ethics and History
P.S 01-”汗牛充栋” (hàn niú chōng dòng) literally means “enough books to make a cow sweat carrying them; books piled to the ceiling.” It’s an idiom that describes a vast number of books or written works. (AI translation)
02-”琳琅满目” (lín láng mǎn mù) describes a dazzling array of beautiful things; a feast for the eyes. It suggests a rich and diverse collection. (AI translation)
References: Cope, B., Kalantzis, M., & Searsmith, D. (2021). Artificial intelligence for education: Knowledge and its assessment in AI-enabled learning ecologies. Educational Philosophy and Theory, 53(12), 1229–1245. https://doi.org/10.1080/00131857.2020.1728732
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