Evidence map›Paper›PMID 40597372›Full record

ArticleBMC psychology2025

The General Attitudes towards Artificial Intelligence Scale (GAAIS): validation and psychometric properties analysis in the Italian context.

Lavinia Cicero, Adriano Russo, Giovanni Di Stefano, Andrea Zammitti

Abstract readValidation Study
In one paragraph

Article in BMC psychology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Lavinia CiceroDepartment of Theoretical and Applied Sciences, eCampus University, 22060, Novedrate (Como), Italy. laviniacicero@gmail.com.
Adriano RussoDepartment of Theoretical and Applied Sciences, eCampus University, 22060, Novedrate (Como), Italy.
Giovanni Di StefanoDepartment of Psychology, Educational Science and Human Movement, University of Palermo, 90128, Palermo, Italy.
Andrea ZammittiDepartment of Educational Sciences, University of Catania, 95124, Catania, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This two-study investigation aimed to assess the psychometric properties of the Italian version of the General Attitudes towards Artificial Intelligence Scale (GAAIS). In study 1 (N = 236 adults) confirmatory factor analysis (CFA) was conducted to examine the factorial structure of the scale. Study 2 (N = 177) assessed the concurrent and predictive validity of GAAIS. Concurrent validity was examined by correlating positive and negative attitude scores toward AI with another measure of attitude toward AI. Predictive validity was assessed by measuring the predictive ability of attitude on intention to use AI. The findings support the two-factor model, including for this Italian version of the scale consistent with the original one (Study 1). Moreover, positive attitudes towards artificial intelligence, as measured by the GAAIS positive factor, were positively correlated with general attitudes towards the use of AI, while the negative factor showed a significant negative correlation with the same general attitudes (Study 2). Then, the Italian version of the GAAIS can be considered as a valid tool for assessing people orientations towards artificial intelligence in the context analyzed, confirming the relevance of studying attitudes and its measurement towards artificial intelligence in different cultural contexts. This measure which relates to a strong psychosocial perspective, may support future research to deepen the understanding of the factors influencing attitudes towards AI as well as to develop more effective communication and training program interventions, being this a very crucial topic in actual society.

Indexed as

Artificial IntelligenceAttitudePsychometricsAdolescentAdultAgedFactor Analysis, StatisticalFemaleHumansItalyMaleMiddle AgedReproducibility of ResultsSurveys and QuestionnairesYoung AdultArtificial intelligenceAttitudes toward AIIntentions to use artificial intelligencePsychometric properties of Italian attitude scaleTechnology acceptance model

Identifiers

PMID40597372
PMCPMC12211962

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.