Spanish Adaptation and Validation of the General Attitudes Towards Artificial Intelligence Scale (GAAIS)

• Zeinab Arees ^[1] ; Sergio Guntín ^[1] ; Francisca Fariña ^[2] ; Mercedes Novo ^[2]
  1. [1] Universidade de Santiago de Compostela

     Universidade de Santiago de Compostela

     Santiago de Compostela, España

     
  2. [2] Universidade de Vigo

     Universidade de Vigo

     Vigo, España

     
• Localización: EJIHPE: European Journal of Investigation in Health, Psychology and Education, ISSN 2174-8144, ISSN-e 2254-9625, Vol. 15, Nº. 11, 2025, págs. 230-230
• Idioma: inglés
• DOI: 10.3390/ejihpe15110230
• Enlaces
  • Texto completo
• Resumen

  • Artificial intelligence (AI) is generating a profound and quick transformation in several areas of knowledge, as well as in industry and society on a global scale, and is considered one of the most significant technological advances of the present era. Understanding citizens’ attitudes toward AI is essential forguiding its development and implementation. To achieve this, valid and reliable instruments are needed to assess attitudesin different sociocultural contexts. With this objective, the General Attitudes towards Artificial Intelligence Scale (GAAIS) was adapted to Spanish. The sample comprised 644 participants: 327 men and 316 women, aged between 18 and 78 years (M = 33.06, SD = 14.91). The original two-factor structure (Positive GAAIS and Negative GAAIS) was validated using Confirmatory Factor Analysis (CFA). Both the fit indices and the internal consistency of the scale were adequate. Furthermore, the validity of the measure (i.e., convergent and discriminant) and the invariance of the model were confirmed. The analyses performed support the adequacy of the model and, therefore, the usefulness of the instrument, considering the ambivalence that people often experience regarding AI. The limitations of the study and the implications for the design of public policies and intervention strategies that promote the ethical, equitable, and socially responsible use of AI are discussed in this study.

• Referencias bibliográficas
  • Ali, I., Warraich, N. F., & Butt, K. (2024). Acceptance and use of artificial intelligence and AI-based applications in education: A meta-analysis...
  • Arees, Z., Rivas, A., Novo, M., & Fariña, F. (2024). Inteligencia artificial (IA): Actitudes y diferencias de género entre hombres y mujeres....
  • Bandalos, D. L., & Finney, S. J. (2018). Factor analysis: Exploratory and confirmatory. In G. R. Hancock, L. M. Stapleton, & R. O....
  • Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238–246.
  • Bentler, P. M. (1995). EQS, structural equations program manual. Multivariate Software.
  • Bergdahl, J., Latikka, R., Celuch, M., Savolainen, I., Soares Mantere, E., Savela, N., & Oksanen, A. (2023). Self-determination and attitudes...
  • Brill, T. M., Munoz, L., & Miller, R. J. (2019). Siri, Alexa, and other digital assistants: A study of customer satisfaction with artificial...
  • Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen, & J. S. Long (Eds.), Testing structural...
  • Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W....
  • Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling: A Multidisciplinary...
  • Chui, M., Manyika, J., & Miremadi, M. (2018). The promise and challenge of the age of artificial intelligence. McKinsey Global Institute.
  • Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334.
  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
  • Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models....
  • Dueber, D. M., Toland, M. D., Lingat, J. E., Love, A. M. A., Qiu, C., Wu, R., & Brown, A. V. (2021). To reverse item orientation or not...
  • Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V.,...
  • Fariña, F., Arce, R., & Novo, M. (2002). Heurístico de anclaje en las decisiones judiciales [Anchorage in judicial decision making]. Psicothema,...
  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal...
  • Fundación Española para la Ciencia y la Tecnología. (2025). Encuesta de percepción social de la ciencia y la tecnologíaen España (EPSCT) 2024....
  • Galindo-Domínguez, H., Delgado, N., Losada, D., & Etxabe, J. M. (2023). An analysis of the use of artificial intelligence in education...
  • Glas, D. F., Kanda, T., & Ishiguro, H. (2016, March 7–10). Human-robot interaction design using interaction composer: Eight years of lessons...
  • Grassini, S. (2023). Development and validation of the AI attitude scale (AIAS-4): A brief measure of general attitude toward artificial intelligence....
  • Grassini, S., & Ree, A. (2023). Hope or doom AI-attitude? Examining the impact of gender, age, and cultural differences on the envisioned...
  • Haenlein, M., & Kaplan, A. (2021). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence....
  • Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation...
  • Hentout, A., Aouache, M., Maoudj, A., & Akli, I. (2019). Human–robot interaction in industrial collaborative robotics: A literature review...
  • High-Level Expert Group on Artificial Intelligenceof the European Commission. (2019). A definition of AI: Main capabilities and scientific...
  • Hong, J.-W., Cruz, I., & Williams, D. (2021). AI, you can drive my car: How we evaluate human drivers vs. self-driving cars. Computers...
  • Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives....
  • Johnson, K. B., Wei, W. Q., Weeraratne, D., Frisse, M. E., Misulis, K., Rhee, K., Zhao, J., & Snowdon, J. L. (2021). Precision medicine,...
  • Kaya, F., Aydin, F., Schepman, A., Rodway, P., Yetişensoy, O., & Kaya, M. D. (2022). The roles of personality traits, AI anxiety, and...
  • Kieslich, K., Lünich, M., & Marcinkowski, F. (2021). The Threats of Artificial Intelligence scale (TAI): Development, measurement and...
  • Kim, S. W., & Lee, Y. (2024). Investigation into the influence of socio-cultural factors on attitudes toward artificial intelligence....
  • Leal, S., Vrij, A., Deeb, H., Burkhardt, J., Dabrowna, O., & Fisher, R. P. (2023). Verbal cues to deceit when lying through omitting information:...
  • Lozano, I. A., Molina, J. M., & Gijón, C. (2021). Perception of artificial intelligence in Spain. Telematics and Informatics, 63, 101672....
  • Manoharan, S. (2019). An improved safety algorithm for artificial intelligence enabled processors in self-driving cars. Journal of Artificial...
  • McDonald, R. P. (1999). Test theory: A unified approach. Lawrence Erlbaum Associates.
  • Méndez, M. J. P., Armayor, N. C., Navarlaz, M. T. D., & Wakefield, A. (2008). The potential advantages and disadvantages of introducing...
  • Mittelstadt, B. (2019). Principles alone cannot guarantee ethical AI. Nature Machine Intelligence, 1, 501–507.
  • Montag, C., & Ali, R. (2024). Starting the journey to understand attitudes towards artificial intelligence in global societies. In C....
  • Rosenberg, M. J., & Hovland, C. I. (1960). Cognitive, affective, and behavioral components of attitudes. In C. I. Hovlandy, & M. J....
  • Rosseel, Y. (2012). Lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36.
  • Sacco, F., Manzi, F., Di Dio, C., Schepman, A., Rodway, P., Massaro, D., & Marchetti, A. (2025). Validation of the general attitudes towards...
  • Schepman, A., & Rodway, P. (2020). Initial validation of the general attitudes towards Artificial Intelligence Scale. Computers in Human...
  • Schepman, A., & Rodway, P. (2023). The General Attitudes towards Artificial Intelligence Scale (GAAIS): Confirmatory validation and associations...
  • Selaya, A., Vilariño, M., & Arce, R. (2024). In search of an empirical definition of a social model for the assessment of the quality...
  • Seo, Y. H., & Ahn, J. W. (2022). The validity and reliability of the Korean version of the General Attitudes towards Artificial Intelligence...
  • Sheikh, H., Prins, C., & Schrijvers, E. (2023). Mission AI: The new system technology. Springer.
  • Sindermann, C., Sha, P., Zhou, M., Wernickle, J., Schmitt, H. S., Li, M., Sariyska, R., Stavrou, M., Becker, B., & Montag, C. (2021)....
  • Sobrino-García, I. (2021). Artificial intelligence risks and challenges in the Spanish public administration: An exploratory analysis through...
  • Steiger, J. H. (1990). Structural model evaluation and modification: An interval estimation approach. Multivariate Behavioral Research, 25(2),...
  • Stein, J. P., Messingschlager, T., Gnambs, T., Hutmacher, F., & Appel, M. (2024). Attitudes towards AI: Measurement and associations with...
  • Şahin, M. G., & Yıldırım, Y. (2024). The General Attitudes towards Artificial Intelligence (GAAIS): A meta-analytic reliability generalization...
  • Tomás, J. M., Galiana, L., Hontangas, P., Oliver, A., & Sancho, P. (2013). Evidencia acumulada sobre los efectos de método asociados a...
  • Tomás, J. M., Sancho, P., Oliver, A., Galiana, L., & Meléndez, J. C. (2012). Efectos de método asociados a ítems invertidos vs. ítems...
  • Tucker, L. R., & Lewis, C. (1973). The reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38, 1–10.
  • United Nations System Chief Executives Board for Coordination. (2022). Principles for the ethical use of artificial intelligence in the United...
  • Uzougbo, N. S., Ikegwu, C. G., & Adewusi, A. O. (2024). Legal accountability and ethical considerations of AI in financial services. GSC...
  • Valavanidis, A. (2023). Artificial Intelligence (AI) Applications: Τhe most important technology we ever develop and we must ensure it is...
  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a united view. MIS...
  • Wang, P. (2019). On defining artificial intelligence. Journal of Artificial General Intelligence, 10(2), 1–37.
  • Weijters, B., Baumgartner, H., & Stillwater, N. (2013). Reversed item bias: An integrative model. Psychological Methods, 18(3), 320–334....
  • World Economic Forum. (2024). Leveraging generative AI for job augmentation and workforce productivity: Scenarios, case studies and a framework...
  • Zhai, X., Chu, X., Wang, M., Zhang, Z., & Dong, Y. (2022). Education metaverse: Innovations and challenges of the new generation of Internet...
  • Zhu, W., Huang, L., Zhou, X., Li, X., Shi, G., Ying, J., & Wang, C. (2025). Could AI ethical anxiety, perceived ethical risks and ethical...
  • Zumbo, B. D., Gadermann, A. M., & Zeisser, C. (2007). Ordinal versions of coefficients alpha and theta for Likert rating scales. Journal...