Italian School Teachers’ Attitudes Toward Artificial Intelligence and Perceptions of AI in Teaching Practices

Socio-Professional Correlates

Bibliographic Data

ID22042544
AuthorsAndrea Fiorucci (0000-0002-1686-145X, University of Salento), Alessia Bevilacqua (0000-0001-7624-7809, University of Salento)
Year2026
Volume16
Issue5
Pages755
Publication date2026-05-10
Open AccessYes
TypeARTICLE
VenueEducation Sciences (JOURNAL)
Journal identifiersISSN: 2227-7102 • E-ISSN: 2227-7102
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/educsci16050755
OpenAlexW7162105317
LanguageEN
References cited41

The rapid development of artificial intelligence (AI) and Generative AI (GenAI) based on large language models (LLMs) is reshaping teaching practices, assessment criteria, and ethical questions regarding authenticity, source reliability, and educational responsibility. Understanding teachers’ attitudes toward AI is crucial for identifying acceptance, resistance, and professional development needs. This study aimed to adapt and validate, for the Italian context, the questionnaire developed by Alsudairy and Eltantawy for assessing teachers’ attitudes toward AI in education, and to explore attitudinal differences according to selected socio-professional variables. A convenience sample of 682 in-service teachers from different school levels and Italian regions completed the 36-item questionnaire on a 3-point Likert scale. Exploratory factor analysis suggested an interpretable two-factor structure, although some items showed weak, non-salient, or cross-loadings. A confirmatory factor analysis conducted on a refined 32-item ordinal model supported a correlated two-factor solution with good global fit indices. However, the strong correlation between the two latent factors and the presence of selected weak indicators suggest that further refinement and cross-validation are needed. Educational attainment was the only socio-professional variable significantly associated with attitudes toward AI, although the effect size was small. Post hoc analyses showed a significant difference between teachers holding a postgraduate Master’s degree and those holding only a high school diploma, whereas other differences should be interpreted as descriptive trends. Taken together, these findings provide preliminary support for the Italian adaptation of the instrument and offer initial insight into the role of professional characteristics in shaping teachers’ attitudes toward AI in educational settings

Confirmatory factor analysis · Descriptive statistics · Educational attainment · Exploratory factor analysis · Likert scale · Perception · Artificial Intelligence in Healthcare and Education · Ethics and Social Impacts of AI · Teaching and Learning Programming

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