Proposition 1: outcome-aligned ownership performances will predict later individual transfer beyond AI-assisted product quality alone¶

Claim · All claims
Evidence · 1 study · theoretical r2 · q1
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q1 i? The framework proposes that outcome-aligned ownership performances will predict later individual transfer performance beyond the prediction provided by AI-assisted product quality alone. → William J. Rothwell 2026

Evidence¶

William J. Rothwell 2026¶

William J. Rothwell. (2026). Assessing Learning When AI Can Produce the Product: An Integrated Framework for Cognitive Ownership and Mutual Evaluation. https://orcid.org/0000-0002-4503-1957 doi:10.46787/ijaipil.v2i1.8053

q1 · i? · theoretical · r2

This is a theoretical proposition from a conceptual article, not an empirical result; the author offers it as a relationship requiring empirical testing. The article states the framework "is proposed rather than empirically validated" and proposes this as a testable claim about incremental predictive value.

"Proposition 1 is that outcome-aligned ownership performances will predict later individual transfer performance beyond the prediction provided by AI-assisted product quality alone."

Discussion¶

Related Claims¶

• AI severs the correlation between artefact quality and cognitive engagement, breaking the artefact-as-proxy for learning — the general claim this finding bears on, with the studies behind it
• Agentivism generates six empirically testable propositions linking delegation structure, verification, reconstruction, and reduced-support transfer to learning outcomes — a broader claim this one bears on
• Immediate task success does not establish learning that persists without AI support — a broader claim this one bears on
• The decisive empirical test of AI-augmented instruction frameworks is whether students retain representational competence when AI support is withdrawn — related
• Product-outcome studies report GenAI-related gains on product-quality measures but do not assess durable, transferable learning — related
• Falsifiable hypothesis: unrestricted AI use on deep-processing tasks will produce a product–process dissociation—higher-rated assignments but lower unaided delayed transfer — related
• Engagement remains an observable and evidential proxy for learning that AI has not broken, though it is not invulnerable — related
• The article proposes an empirical validation agenda: comparing perceived competence under LLM-assisted versus unaided conditions, with measurement frameworks and longitudinal designs — related
• RAIL-Ed's integrative commitment holds that the absence of any pillar produces a characteristic pedagogical failure — related