Exploratory Bayesian Multidimensional IRT with Rater Effects for Scoring Generative AI Usage

Leon Camus; Sebastian Gombert; Fabíola Ribeiro; Gianluca Romano; Carmen Köhler; Hendrik Drachsler

Exploratory Bayesian Multidimensional IRT with Rater Effects for Scoring Generative AI Usage

Leon Camus, Sebastian Gombert, Fabíola Ribeiro, Gianluca Romano, Carmen Köhler, Hendrik Drachsler

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Abstract

How students use generative AI is a configuration of distinct behaviors, not one skill, yet scoring typically collapses it onto a single proficiency axis, conflating process with product. We develop a multidimensional Bayesian IRT model with rater effects and post-hoc Varimax-permutation identification, surfacing three substantive dimensions: prompting effort, content delegation, AI-delivered citations. Domain familiarity shifts students toward more active engagement and away from delegation.

Anthology ID:: 2026.aimecon-main.3
Volume:: Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers
Month:: October
Year:: 2026
Address:: Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States
Editors:: Joshua Wilson, Christopher Ormerod, Magdalen Beiting-Parrish
Venue:: AIME-Con
SIG:
Publisher:: National Council on Measurement in Education (NCME)
Note:
Pages:: 13–29
Language:
URL:: https://aclanthology.org/2026.aimecon-main.3/
DOI:
Bibkey:
Cite (ACL):: Leon Camus, Sebastian Gombert, Fabíola Ribeiro, Gianluca Romano, Carmen Köhler, and Hendrik Drachsler. 2026. Exploratory Bayesian Multidimensional IRT with Rater Effects for Scoring Generative AI Usage. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers, pages 13–29, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).
Cite (Informal):: Exploratory Bayesian Multidimensional IRT with Rater Effects for Scoring Generative AI Usage (Camus et al., AIME-Con 2026)
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PDF:: https://aclanthology.org/2026.aimecon-main.3.pdf

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@inproceedings{camus-etal-2026-exploratory,
    title = "Exploratory {B}ayesian Multidimensional {IRT} with Rater Effects for Scoring Generative {AI} Usage",
    author = {Camus, Leon  and
      Gombert, Sebastian  and
      Ribeiro, Fab{\'i}ola  and
      Romano, Gianluca  and
      K{\"o}hler, Carmen  and
      Drachsler, Hendrik},
    editor = "Wilson, Joshua  and
      Ormerod, Christopher  and
      Beiting-Parrish, Magdalen",
    booktitle = "Proceedings of the Artificial Intelligence in Measurement and Education Conference ({AIME}-Con): Full Papers",
    month = oct,
    year = "2026",
    address = "Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States",
    publisher = "National Council on Measurement in Education (NCME)",
    url = "https://aclanthology.org/2026.aimecon-main.3/",
    pages = "13--29",
    ISBN = "979-8-9983004-0-0",
    abstract = "How students use generative AI is a configuration of distinct behaviors, not one skill, yet scoring typically collapses it onto a single proficiency axis, conflating process with product. We develop a multidimensional Bayesian IRT model with rater effects and post-hoc Varimax-permutation identification, surfacing three substantive dimensions: prompting effort, content delegation, AI-delivered citations. Domain familiarity shifts students toward more active engagement and away from delegation."
}

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%0 Conference Proceedings
%T Exploratory Bayesian Multidimensional IRT with Rater Effects for Scoring Generative AI Usage
%A Camus, Leon
%A Gombert, Sebastian
%A Ribeiro, Fabíola
%A Romano, Gianluca
%A Köhler, Carmen
%A Drachsler, Hendrik
%Y Wilson, Joshua
%Y Ormerod, Christopher
%Y Beiting-Parrish, Magdalen
%S Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers
%D 2026
%8 October
%I National Council on Measurement in Education (NCME)
%C Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States
%@ 979-8-9983004-0-0
%F camus-etal-2026-exploratory
%X How students use generative AI is a configuration of distinct behaviors, not one skill, yet scoring typically collapses it onto a single proficiency axis, conflating process with product. We develop a multidimensional Bayesian IRT model with rater effects and post-hoc Varimax-permutation identification, surfacing three substantive dimensions: prompting effort, content delegation, AI-delivered citations. Domain familiarity shifts students toward more active engagement and away from delegation.
%U https://aclanthology.org/2026.aimecon-main.3/
%P 13-29

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Markdown (Informal)

[Exploratory Bayesian Multidimensional IRT with Rater Effects for Scoring Generative AI Usage](https://aclanthology.org/2026.aimecon-main.3/) (Camus et al., AIME-Con 2026)

Exploratory Bayesian Multidimensional IRT with Rater Effects for Scoring Generative AI Usage (Camus et al., AIME-Con 2026)

ACL

Leon Camus, Sebastian Gombert, Fabíola Ribeiro, Gianluca Romano, Carmen Köhler, and Hendrik Drachsler. 2026. Exploratory Bayesian Multidimensional IRT with Rater Effects for Scoring Generative AI Usage. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers, pages 13–29, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).