Agency
Did the learner set goals, monitor uncertainty, justify choices, and revise with evidence—not merely report feeling in control?
Every paper in the source corpus, made easier to search, compare, question, and critique—without hiding access limits or review status.
Preserve the useful finding. Name where the evidence ends. Measure what should come next.
3,311 raw records → 2,192 deduplicated → 262 strict inclusions
The original corpus had breadth. This version adds comparison, progressive disclosure, and an honest appraisal layer to every record.
Margin now evaluates every question against Tina R. Austin’s The Learner Is Not a Constant and her human–AI evaluation field guide. It does not imitate Tina. It makes the published standard visible.
Did the learner set goals, monitor uncertainty, justify choices, and revise with evidence—not merely report feeling in control?
What remained when support changed, failed, or disappeared: retention, transfer, calibration, recovery, or independent performance?
What did the person verify, accept, reject, revise, retrieve, explain, or delegate in response to the model?
Did the design follow changing behavior, context, treatment, and capability across enough observations to test its mechanism?
What independent learner, educator, assessor, institutional, or community evidence widened the model’s narrow view?
The source searched arXiv, OpenAlex, Crossref, Semantic Scholar, and PubMed with exact-term and close-variant query families. Inclusion required AI plus cognitive offloading, cognitive outsourcing, deskilling, over-reliance, or metacognitive laziness to be central—not merely cited in passing.
“Full text” means an openly accessible paper was extracted and inspected. “Abstract only” means the summary is bounded to accessible metadata. Margin’s Tina-aligned verdicts are rule-based syntheses of repository fields and the supplied framework—not live responses from Tina and not completed formal risk-of-bias reviews.