Purposeful Friction Design Framework (PFDF) Pilot
Session 1 (~25 min): surveys and a critical-analysis writing task. Session 2 (~15 min, complete when ready): retention and transfer checks. You will be randomly assigned to one of three AI conditions.
12 participants enrolled · 12 completed (100%) · goal 120
12
Enrolled
0
Active
15
Tasks
About this study
PFDF pilot template wired to wide analysis export, blinded dual-rater coding, in-platform ANOVA monitoring, and completion codes. Session 2 unlocks after Session 1 tasks are complete (production deployments may add a 48–72h delay when study waves ship).
Hypothesis
Purposeful-friction AI preserves higher self-reported agency and rubric-coded argument quality than frictionless AI, without sacrificing completion rates relative to the non-AI control.
Methodology
Between-subjects RCT with three conditions. Primary outcomes: agency scales, offloading scales, retention/transfer items, and dual-rater coded critical-analysis responses.
Related frameworks
Research artifacts
Protocols, instruments, and findings published from this study. Browse the full repository.
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Protocol · v1.0
PFDF Session ProtocolThree-arm PFDF pilot protocol with Session 1 and Session 2 tasks.
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Instrument · v1.0
PFDF Agency & Offloading ScalesSeven-point Likert items for agency and post-task offloading.
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Dissemination
Related articles, media, frameworks, and external resources connected to this study.