{
  "abstract": "AI-based item generation and NLP-based prediction of item parameters are producing item banks that are substantially larger, sparser, and more frequently updated than conventional banks. Hierarchical Bayesian item response theory (IRT) is a natural calibration framework for such banks, but the common practice of refitting the entire accumulated response history at each update is costly and can exceed available memory. We describe \\emph{consensus calibration}, a divide-and-conquer procedure that calibrates each time period independently and reconstructs the pooled posterior in two layers. First, the posterior draws of each period are mapped to a common metric by a robust characteristic-curve linking (Haebara) that is solved separately for each draw, which propagates the uncertainty of the linking transformation into the linked posteriors. Second, the linked item posteriors are combined as a product of Gaussian densities from which the population prior contributed by each period is removed and a single prior---obtained by consensus across the per-period population posteriors---is reinstated. The correction targets the posterior dispersion, not only its location. As evidence for consensus calibration, we compare it to a pooled single-run analysis on a large operational assessment in terms of item-parameter recovery, an uncertainty-by-exposure diagnostic, and the ability distributions.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.13590",
  "author_total": 5,
  "authors": [
    {
      "name": "Paul A. Jewsbury"
    },
    {
      "name": "Steven W. Nydick"
    },
    {
      "name": "Manqian Liao"
    },
    {
      "name": "Siyuan"
    },
    {
      "name": "Chen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1fqx81xd6s053.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.13590",
  "fulltext_url": null,
  "id": "1fqx81xd6s053",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.13590",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.AP"
    ],
    [
      "arxiv",
      "stat.ME"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2609.13590",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-11",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1fqx81xd6s053.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:36Z",
      "native_id": "2609.13590",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.13590"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Bayesian Consensus Calibration of Continuously Evolving IRT Item Banks",
  "title_text": "Bayesian Consensus Calibration of Continuously Evolving IRT Item Banks",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:36Z",
  "url": "https://research.draconitic.com/paper/1fqx81xd6s053",
  "venue": "arXiv",
  "versions": []
}