pedagogical friction in the age of generative ai.
a mixed-methods collective instrumental case study examining how k-12 educators and recent learners navigate the friction-reducing affordances of generative ai.
micah j. miner, cetl, ed.s.
ed.d. curriculum, advocacy, & policy
national louis university
the problem of practice.
Generative AI forces a reorganization of the symbolic environments in which students learn. We are transitioning into a state of Tertiary Algorithmicity—a media-ecological condition where algorithmic systems do not just curate content, but generate it, making human authorship optional at scale.
This creates a structural bias toward Unproductive Success (Kapur, 2016): correct academic performance without the cognitive struggle required for durable schema construction.
Educators are left navigating this friction-reducing affordance, often without policies grounded in learning science. The central issue is not academic integrity; it is the bypassing of interpretive labor, intellectual accountability, and the developmental conditions human learning depends upon.
the national structural context.
Analysis of NCES School Pulse Panel data reveals a critical pedagogical infrastructure gap:
- Device Access Parity: 88% of high-poverty schools and 89% of low-poverty schools report 1:1 device programs.
- Policy Gap: Higher-poverty schools are 9% less likely to have a written AI policy (24% vs 33%).
- Literacy Gap: Higher-poverty schools are 10% less likely to teach AI topics to students (39% vs 49%).