Vol. 17 No. 1 (2026): MEDIA EDUCATION – Studi, ricerche e buone pratiche
Articles

Enhancing AI literacy in education: A multi-country pre-post quasi-experimental evaluation study

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  • Maria Ranieri,
  • Gabriele Biagini,
  • Stefano Cuomo,
  • Josephine Christman
Maria Ranieri
University of Florence, Italy
Gabriele Biagini
University of Florence, Italy
Stefano Cuomo
University of Florence, Italy
Josephine Christman
Paul Valéry University Montpellier 3, France
DOI: https://doi.org/10.36253/me-20214

Published 2026-05-30

Keywords

  • AI literacy,
  • educational technology,
  • media education,
  • pre-post intervention,
  • professional development

Copyright (c) 2026 Maria Ranieri, Gabriele Biagini, Stefano Cuomo, Josephine Christman

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

This study examines the effectiveness of targeted professional development workshops in enhancing AI literacy preparedness among educators in Romania, France, and Finland, within the framework of the TADAM (Tools & Awareness about Disinformation, Algorithms & Media) project. Using a pre-post design with independent groups, we assessed educators’ perceived preparedness across four dimensions: AI literacy promotion, local knowledge development, training facilitation, and stakeholder networking, before and after participation in country-specific workshops. Results indicate that participants assessed following workshop completion reported substantially higher preparedness levels across all four dimensions compared to those assessed before the workshops, with effect sizes ranging from medium-large to large. Psychometric analysis confirmed excellent internal consistency and strong unidimensional structure of the preparedness measure. Overall, the findings suggest that competency-based professional development models emphasising active learning, collaborative co-design, and contextual adaptation may be associated with meaningful gains in educators’ perceived preparedness in emerging technological domains, while acknowledging the methodological limitations inherent in non-randomised designs.

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