IDEAS home Printed from https://ideas.repec.org/a/spr/soinre/v178y2025i3d10.1007_s11205-025-03544-7.html
   My bibliography  Save this article

Multilevel Modeling of Training Needs in Artificial Intelligence

Author

Listed:
  • Veronica Distefano

    (University of Salento
    European Centre for Living Technology (ECLT)
    Faculty of Economics and Law, Department of Management and Economics)

  • Sabrina Maggio

    (University of Salento)

  • Sandra De Iaco

    (University of Salento
    National Centre for HPC, Big Data and Quantum Computing
    National Biodiversity Future Center)

Abstract

Nowadays, Artificial Intelligence (AI) is playing a rapidly increasing role in several fields of research and in almost all sectors of real life. However, few studies have assessed the effects of AI applications on training needs. This paper proposes an innovative multilevel modeling in order to investigate Awareness, Attitude and Trust towards AI and their reflections on learning needs. In particular, it is shown how a machine learning variable selection algorithm can support the definition of the optimal subset of all relevant covariates with respect to the outcome variable and improve the multilevel model performance for estimating the probability of educational needs. Thus, starting from a complex web survey to European citizens distributed in eight countries, the estimation of a multilevel binary model, defined on the basis of covariates selected through the Boruta random forest algorithm, is proposed. A discussion on the gender differences of the related estimated multilevel logit models is presented. A sensitivity analysis is also included in order to assess the prediction accuracy of the proposed multilevel logit modeling.

Suggested Citation

  • Veronica Distefano & Sabrina Maggio & Sandra De Iaco, 2025. "Multilevel Modeling of Training Needs in Artificial Intelligence," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 178(3), pages 1411-1439, July.
    • Handle: RePEc:spr:soinre:v:178:y:2025:i:3:d:10.1007_s11205-025-03544-7
    DOI: 10.1007/s11205-025-03544-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11205-025-03544-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11205-025-03544-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:soinre:v:178:y:2025:i:3:d:10.1007_s11205-025-03544-7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.