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**: correct academic performance without the cognitive struggle required for durable schema construction. The central issue is the bypassing of interpretive labor and intellectual accountability that human learning depends upon.

the pedagogical friction framework

The framework acts as the theoretical construct through which school conditions shape teacher practice and student experience in an algorithmic age:

• Cognitive Struggle: Preserving noetic friction needed for deep schema coding.
• Rhetorical Friction: Engaging in active defense, debate, and noetic exchange.
• Existential Friction: Cultivating authorial ownership, agency, and cognitive labor.
• Infrastructural Friction: Building intentional guardrails into district tech deployments.

mixed methods qualitative case study

This pragmatist design integrates Merriam's qualitative case study framework with structural quantitative datasets to produce actionable meta-inferences.

• Qualitative Strand (Dominant): Guided by Merriam's qualitative case study approach—examining semi-structured interviews, temporal student coding, document analysis, and card sorting protocols.
• Quantitative Strand (Contextual): Teacher surveys (N=50-100) and analysis of NCES School Pulse Panel data.
• Agentic Comparison (SAQ): Prompting 3 major LLMs using identical role-prompted human protocols to test the nature of AI contextual reasoning.