{"ID":2876174,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.00718","arxiv_id":"2509.00718","title":"Exam Readiness Index (ERI): A Theoretical Framework for a Composite, Explainable Index","abstract":"We present a theoretical framework for an Exam Readiness Index (ERI): a composite, blueprint-aware score R in [0,100] that summarizes a learner's readiness for a high-stakes exam while remaining interpretable and actionable. The ERI aggregates six signals -- Mastery (M), Coverage (C), Retention (R), Pace (P), Volatility (V), and Endurance (E) -- each derived from a stream of practice and mock-test interactions. We formalize axioms for component maps and the composite, prove monotonicity, Lipschitz stability, and bounded drift under blueprint re-weighting, and show existence and uniqueness of the optimal linear composite under convex design constraints. We further characterize confidence bands via blueprint-weighted concentration and prove compatibility with prerequisite-admissible curricula (knowledge spaces / learning spaces). The paper focuses on theory; empirical study is left to future work.","short_abstract":"We present a theoretical framework for an Exam Readiness Index (ERI): a composite, blueprint-aware score R in [0,100] that summarizes a learner's readiness for a high-stakes exam while remaining interpretable and actionable. The ERI aggregates six signals -- Mastery (M), Coverage (C), Retention (R), Pace (P), Volatilit...","url_abs":"https://arxiv.org/abs/2509.00718","url_pdf":"https://arxiv.org/pdf/2509.00718v1","authors":"[\"Ananda Prakash Verma\"]","published":"2025-08-31T06:56:59Z","proceeding":"cs.CY","tasks":"[\"cs.CY\",\"cs.AI\",\"cs.LG\",\"stat.ML\"]","methods":"[]","has_code":false}