Task-specific AI learning gains weaken at follow-up; reporting limits qualify the retention claim

Teaching leaders need to know whether improved performance persists beyond supported practice. Bring faculty, tutors, institutional research and student representatives into discovery. Ask what skill should endure, when it can be reassessed and how withdrawal will affect the estimate. Offer a bounded evaluation of an existing approved assistant with a delayed checkpoint. The value hypothesis is better evidence for a course decision, rather than a guaranteed positive result. Reporting inconsistencies make this a design prompt, not a sales benchmark. Do not promise lasting learning gains, assert that AI universally harms cognition or use the study to justify staffing reductions.

Fit a local evaluation to a learning platform that can distinguish practice access from independent testing. Prerequisites include equivalent assessment forms, content approval, consent arrangements and a documented model configuration. Record availability and tool use without collecting unnecessary personal content. Test answer leakage, inaccessible questions and failure recovery before enrollment.

Proposed proof of value

compare baseline-adjusted immediate and delayed independent performance, reporting missing data and uncertainty. Keep supported outputs separate from scores used to infer competence. A mixed-resource comparison may better match local study habits, but it would be a new design rather than a replication of this restricted-tool experiment.

A course lead should own the educational decision and an evaluator should own the analysis plan. Schedule follow-up before students leave the course, train markers and document exclusions consistently. Dependencies include assessment capacity, accessible alternatives and privacy approval.

Proposed acceptance criteria

all planned endpoints reported, denominators reconciled, independent scoring completed and delayed results reviewed before expansion. These are local gates, not observed achievements. Track attrition, support time and deviations alongside outcomes. The source's inconsistent counts illustrate why delivery teams should reconcile participant flow before communicating effect sizes. Retain human teaching support while evaluating whether the intervention changes learning.