Your new experience awaits. Try the new design now and help us make it even better

ORIGINAL RESEARCH article

Front. Psychol.

Sec. Personality and Social Psychology

General Attitudes towards Artificial Intelligence Scale (GAAIS): Hungarian Adaptation and Links to Personality Traits

Provisionally accepted
Sandor  ROZSASandor ROZSA1*Szabolcs  BandiSzabolcs Bandi2István  HartungIstván Hartung3Imre  A. TörökImre A. Török4Julia  É VargaJulia É Varga3Eszter  H SomlaiEszter H Somlai3Robert  HeroldRobert Herold3Janos  KallaiJanos Kallai5
  • 1Károli Gáspár University of the Reformed Church in Hungary, Budapest, Hungary
  • 2Pecsi Tudomanyegyetem Klinikai Kozpont Pszichiatriai es Pszichoterapias Klinika, Pécs, Hungary
  • 3Department of Health Sciences, University of Pécs,, Pécs, Hungary
  • 4Kiskunhalas Semmelweis Hospital, University Teaching Hospital, Psychiatry Unit, Kiskunhalas, Hungary, Kiskunhalas, Hungary
  • 5Pecsi Tudomanyegyetem Magatartastudomanyi Intezet, Pécs, Hungary

The final, formatted version of the article will be published soon.

The present study undertook the adaptation and psychometric validation of the Hungarian version of the General Attitudes toward Artificial Intelligence Scale (GAAIS) to assess both positive and negative attitudes toward artificial intelligence (AI) in relation to psychosocial functioning and personality traits. A total of 704 participants (557 women, 144 men) aged 18–60 years completed the GAAIS together with several validated self-report measures: Mental Health Continuum–Short Form, Self-Concept Clarity Scale, Problematic Internet Use Questionnaire, and Schizotypal Personality Questionnaire–Brief Revisited. The Hungarian version showed solid internal consistency (α = 0.85 for the positive and 0.81 for the negative subscale) and a clear two-factor structure, supported by CFA. The frequency of AI use in daily life emerged as the strongest predictor of both positive and negative attitude scores lending further support to the construct validity of the scale. The association analysis revealed that the behavioral components of AI-related attitudes are shaped by the competing motivational forces. Specifically, the frequent use of AI is linked to the positive attitudes of GAAIS. In contrast, the unfavorable use of AI is associated with the negative attitudes of GAAIS. In the affective domain, anxiety sensitivity is associated with a negative attitude, and in the cognitive domain, schizotypal cognitive characteristics and difficulties in self-integration are linked to elevated negative attitudes in GAAIS. However, on the other pole of this cognitive dimension, adequate self-integration does not play a significant role in the formation of an AI-related positive attitude. These findings confirm the reliability and validity of the Hungarian GAAIS and highlight the importance of personality traits in shaping adaptive and maladaptive attitudes toward AI. The results underscore the value of a multidimensional framework for understanding AI attitudes. Adaptive traits were associated with psychological resilience, effective self-regulation, and constructive digital engagement, whereas maladaptive traits were correlated with social anxiety and problematic interactions with the internet and artificial intelligence (AI) technologies. A critical question remains: What outcomes may arise from when individuals hold positive attitudes toward AI but simultaneously experience difficulties with self-integration? This paradox highlights the need for further research into the complex interplay between personality structure and digital adaptation.

Keywords: artificial intelligence, General Attitudes towards Artificial Intelligence Scale, Reliability, validity, personality trait patterns

Received: 11 Sep 2025; Accepted: 29 Oct 2025.

Copyright: © 2025 ROZSA, Bandi, Hartung, Török, Varga, Somlai, Herold and Kallai. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Sandor ROZSA, rozsaqqq@gmail.com

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.