Metacognitive Support

1. Hypothesis

Prediction prompts and error reflection will improve calibration more than generic study advice.

2. Experiment design

Design: longitudinal adaptive-learning trial; baseline, active comparison, calibrated AI condition, user-controlled condition, failure or handback scenario and delayed follow-up Methods: randomised classroom pilots; mastery-learning analysis; item-response modelling; learning analytics; interviews; longitudinal follow-up; pre-registered analysis; active comparison; subgroup and accessibility analysis; delayed retention or longitudinal follow-up; adverse-effect capture; reproducibility testing; participant debrief Independent variables: prediction prompts; reflection; feedback; task type Dependent variables: calibration; strategy selection; learning gain Confounders: baseline capability; prior exposure; age; motivation; context; technology access; implementation fidelity Measures: judgement-of-learning accuracy; calibration error; strategy changes Success criteria: Statistically and practically meaningful improvement; preserved or improved human skill and agency; acceptable burden; no disproportionate subgroup harm; reproducible performance; effective handback, correction and recovery. Failure conditions: No meaningful benefit; gains mask reduced understanding or skill; dependency, fatigue, stress or exclusion exceeds benefit; effects fail to transfer or persist; user control is ineffective; correction or handback fails.

3. Seed result / current evidence

DEFENSIBLE SEED RESULT — NON-EMPIRICAL. The current evidence supports Metacognitive Support as a testable research proposition. Problem basis: Current approaches to metacognitive support are fragmented, poorly calibrated or insufficiently measured, making it difficult to distinguish real benefit from substitution, novelty or surveillance effects. Directional expectation: If the hypothesis is supported, the intervention condition should improve calibration; strategy selection; learning gain while avoiding material deterioration in independence, confidence calibration or delayed performance. Proposed observations: judgement-of-learning accuracy; calibration error; strategy changes. Seed data profile: Evidence Strength 10/100; Confidence 25/100; Maturity 20/100; Overall Health 36/100; Novelty 78/100; Strategic Importance 95/100. Evidence boundary: No validated results yet.; experiments 0, studies 0, participants 0. This is suitable for protocol formation and baseline comparison, not as a finding of effect.

4. Seed conclusion

DEFENSIBLE SEED CONCLUSION — PROVISIONAL. Metacognitive Support warrants structured testing because the CSV identifies a defined problem, falsifiable hypothesis, measurable outcomes and relevant literature foundations. The present position is that “Prediction prompts and error reflection will improve calibration more than generic study advice.” is plausible and decision-relevant, but unvalidated. Proceed to controlled testing against the stated success and failure conditions. Confirm, narrow or reject this seed after effect sizes, uncertainty, subgroup outcomes, adverse effects, persistence and handback performance are observed.

Prior-art search performed before starting

PRIOR-ART SEED BASELINE — PARTIAL. The CSV records these literature domains: Mastery learning; retrieval practice; spacing; formative assessment; self-regulated learning; intelligent tutoring systems. It also records: UNESCO Generative AI in Education — https://www.unesco.org/; OECD Education — https://www.oecd.org/education/; AERO — https://www.edresearch.edu.au/; Education Endowment Foundation — https://educationendowmentfoundation.org.uk/. Evidence register status: “Seeded; authoritative source register refreshed; empirical evidence not yet ingested”. This is defensible as a starting prior-art inventory, but not as proof of a completed systematic search because search dates, databases, exact queries, reviewer, result counts, screening decisions, claim mapping and a replayable receipt are absent.

Prior-art material named: Existing literature: Mastery learning; retrieval practice; spacing; formative assessment; self-regulated learning; intelligent tutoring systems. References: UNESCO Generative AI in Education — https://www.unesco.org/; OECD Education — https://www.oecd.org/education/; AERO — https://www.edresearch.edu.au/; Education Endowment Foundation — https://educationendowmentfoundation.org.uk/

Critical gap / next action

Create and attach a dated prior-art search log; lock the protocol; execute the proposed study; link raw data and analysis; then replace the results and conclusion placeholders with evidence-bounded findings.