A Mixed-Methods Collective Instrumental Case Study exploring how educators and students navigate the transition from pre-GenAI to post-GenAI learning environments.
Explore the StudyTo investigate how K–12 educators navigate the friction-reducing affordances of GenAI, and how university students describe changes in learning across the pre-GenAI and post-GenAI transition.
Grounded in Pragmatism and Technoskepticism, the study acts as a critical evaluation of media ecology, seeking actionable knowledge for AI policy development.
How do K–12 teachers understand and navigate the friction-reducing affordances of generative AI?
What institutional conditions enable or constrain friction-preserving pedagogy?
How can the Pedagogical Friction Framework inform AI policy development in K–12?
Extending Walter Ong's framework to account for generative artificial intelligence.
Memory-based, communal, and situational. Knowledge is preserved through repetition and mnemonic devices.
Writing externalizes memory, enabling analytical detachment, individual authorship, and abstract reasoning.
Electronic broadcast media (radio/TV) retrieves communal qualities, but operates through one-to-many literate infrastructure.
Social media platforms where humans create symbolic content, but opaque algorithms determine distribution and curation.
Neural networks generate the symbolic content itself, making human authorship optional at scale. The source of content is no longer human.
Three defining characteristics that threaten the cognitive processes education depends on.
The externalization of cognitive labor moves from storage to generation. The work of synthesizing and making meaning is offloaded to the system rather than occurring in the learner's mind.
The flooding of communicative environments with synthetic discourse. The origin of content becomes uncertain, and simulated interlocutors replace genuine contestation.
Symbolic production is severed from lived experience. Text is produced without personal commitment, intellectual risk, or accountability for the claims advanced.
When generative AI is used to bypass these cognitive processes, the result is what Manu Kapur (2016) calls Unproductive Success. A student generates fluent, correct output without ever engaging in the schema construction required for durable understanding. The artifact of learning is visible, but the process of learning has been bypassed.
Four dimensions of resistance necessary for durable learning.
Cognitive struggle. The necessary resistance encountered when wrestling with complex ideas, synthesizing information, and engaging in deliberate practice.
Engagement with real audiences. The friction of translating internal thought into a structure that an external audience can understand.
Intellectual ownership. The personal stake and authorial stance that connects the learner's identity to the work produced.
Policy and institutional conditions that either enable or constrain the preservation of the other three dimensions of friction.
A collective instrumental case study bridging Qualitative (QUAL) and Quantitative (quan) strands.
Interviews with educators & students, a Card Sort Protocol on friction activities, a Teacher Survey on friction practices, and Secondary Data compilation (NCES SPP, RAND).
A priori coding of qualitative data through the Pedagogical Friction lens. Descriptive analysis, disaggregation, and cross-tabulation of quantitative data.
Joint displays comparing qualitative themes with quantitative patterns, documenting convergence and divergence between student experience and teacher observation.
Administering the same instruments to AI platforms to conduct a structured comparison between human and AI discourse.
Exploring both sides of the friction equation.
Testing theoretical claims about what AI-generated pedagogical reasoning lacks.
A digital resource hub expanding on the Qualifying Paper and Dissertation.
Foundational material expanding on the history of machine intelligence.
Dedicated summary of claims related to the pedagogy of friction.
Theoretical claims regarding medial ecology and Walter Ong.
Literature review material for the qualifying paper and dissertation.