Abstract
The rapid adoption of generative artificial intelligence in K–12 schools has outpaced educators' capacity to evaluate its effects on learning. This conceptual article argues that the dominant institutional responses treating AI as either an integrity threat or a productivity tool share an unexamined assumption that the fundamental nature of learning remains unchanged. Drawing on Walter Ong's media ecology and extending it through the concept of tertiary algorithmicity, the article offers a theoretical framework for understanding generative AI as a qualitative shift in the symbolic environment of schooling. For the first time, the prevailing communication technology ctively generates symbolic content, enabling what Kapur's productive failure research identifies as unproductive success: competent performance without underlying cognitive development rather than just mediating symbolic content. The article introduces the Pedagogical Friction Framework, a four-dimensional model (noetic, rhetorical, existential, and infrastructural) for designing learning environments that preserve the productive cognitive struggle essential to genuine understanding. Implications for curriculum and pedagogy, institutional leadership and policy, and the student experience of learning are examined from a practitioner perspective grounded in K–12 technology leadership.
Recommended Citation
Miner, Micah J.. (). When the Output Looks Like Learning: Tertiary Algorithmicity, Unproductive Success, and the Case for Pedagogical Friction in K–12 Schools. i.e.: inquiry in education: Vol. 18: Iss. 1, Article 4.Retrieved from: https://digitalcommons.nl.edu/ie/vol18/iss1/4