Construction and validation of the Emotional Trust in Artificial Intelligence Scale (CEIA)
Background: Emotional trust in Artificial Intelligence (AI) has become a crucial component of human-AI interaction, especially in academia, where generative systems are used for academic, communicational, and socio-emotional purposes. While research has advanced in cognitive trust, there is a lack o...
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| Lenguaje: | inglés |
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2026
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| author | Berle Estalin Briones-Llamoctanta Ronald Garnique-Hinostroza Roberto Estrada-Medina Josue Edison Turpo-Chaparro |
| author_browse | Berle Estalin Briones-Llamoctanta Josue Edison Turpo-Chaparro Roberto Estrada-Medina Ronald Garnique-Hinostroza |
| author_facet | Berle Estalin Briones-Llamoctanta Ronald Garnique-Hinostroza Roberto Estrada-Medina Josue Edison Turpo-Chaparro |
| author_sort | Berle Estalin Briones-Llamoctanta |
| collection | Producción científica |
| description | Background: Emotional trust in Artificial Intelligence (AI) has become a crucial component of human-AI interaction, especially in academia, where generative systems are used for academic, communicational, and socio-emotional purposes. While research has advanced in cognitive trust, there is a lack of validated instruments that rigorously assess the affective dimension of trust, which involves processes of perceived authenticity, emotional validation, affective recognition, and the establishment of symbolic bonds with artificial agents. Objective: To develop and psychometrically validate the Emotional Confidence in Artificial Intelligence Scale (CEIA) in university students, examining its factor structure, reliability, convergent and discriminant validity, as well as its measurement invariance by sex. Methods: Six hundred and fibe Peruvian university students, aged 16 to 88 years (M = 22.93, SD = 7.35), participated in the study. Item distributions, item-total correlations, and descriptive statistics were analyzed. Internal structure was assessed using Exploratory Factor Analysis (EFA) with polychoric correlations and Confirmatory Factor Analysis (CFA). Reliability was estimated using Cronbach's alpha and McDonald's omega. Convergent and discriminant validity were examined using AVE, CR, and HTMT. Sex invariance was assessed using multigroup CFA with the WLSMV estimator. Results: The EFA supported a multidimensional structure consistent with the theoretical model. The CFA confirmed a four-factor solution-Perceived Authenticity, Emotional Validation, Affective Recognition, and Symbolic Bonding-with adequate fit indices (CFI = 0.944; TLI = 0.937; RMSEA = 0.073; SRMR = 0.033). The dimensions showed high internal consistency (α = 0.91-0.94; ω = 0.91-0.94) and satisfactory evidence of convergent and discriminant validity. Invariance analysis demonstrated metric invariance and partial scalar invariance between men and women. Conclusion: The CEIA is a valid, reliable, and partially sex-invariant instrument for assessing emotional trust in AI among university students. It constitutes a robust tool for research, technological design, and the ethical development of AI systems with affective capabilities. |
| format | Article |
| id | openalex:W7135100624 |
| institution | UPeU |
| language | English |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| record_format | dspace |
| spelling | openalex:W7135100624Construction and validation of the Emotional Trust in Artificial Intelligence Scale (CEIA)Berle Estalin Briones-LlamoctantaRonald Garnique-HinostrozaRoberto Estrada-MedinaJosue Edison Turpo-Chaparroscale developmentperceived authenticityemotional validationemotion recognitionhuman-AI interactionuniversity studentssex invariancemeasurement invariancegenerative AIAI ethicsSocial Robot Interaction and HRIAI in Service InteractionsPsychometric Methodologies and TestingBackground: Emotional trust in Artificial Intelligence (AI) has become a crucial component of human-AI interaction, especially in academia, where generative systems are used for academic, communicational, and socio-emotional purposes. While research has advanced in cognitive trust, there is a lack of validated instruments that rigorously assess the affective dimension of trust, which involves processes of perceived authenticity, emotional validation, affective recognition, and the establishment of symbolic bonds with artificial agents. Objective: To develop and psychometrically validate the Emotional Confidence in Artificial Intelligence Scale (CEIA) in university students, examining its factor structure, reliability, convergent and discriminant validity, as well as its measurement invariance by sex. Methods: Six hundred and fibe Peruvian university students, aged 16 to 88 years (M = 22.93, SD = 7.35), participated in the study. Item distributions, item-total correlations, and descriptive statistics were analyzed. Internal structure was assessed using Exploratory Factor Analysis (EFA) with polychoric correlations and Confirmatory Factor Analysis (CFA). Reliability was estimated using Cronbach's alpha and McDonald's omega. Convergent and discriminant validity were examined using AVE, CR, and HTMT. Sex invariance was assessed using multigroup CFA with the WLSMV estimator. Results: The EFA supported a multidimensional structure consistent with the theoretical model. The CFA confirmed a four-factor solution-Perceived Authenticity, Emotional Validation, Affective Recognition, and Symbolic Bonding-with adequate fit indices (CFI = 0.944; TLI = 0.937; RMSEA = 0.073; SRMR = 0.033). The dimensions showed high internal consistency (α = 0.91-0.94; ω = 0.91-0.94) and satisfactory evidence of convergent and discriminant validity. Invariance analysis demonstrated metric invariance and partial scalar invariance between men and women. Conclusion: The CEIA is a valid, reliable, and partially sex-invariant instrument for assessing emotional trust in AI among university students. It constitutes a robust tool for research, technological design, and the ethical development of AI systems with affective capabilities.Articlehttps://doi.org/10.3389/fpsyg.2026.1755160https://public-pages-files-2025.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1755160/pdfW7135100624enFrontiers in Psychology1664-10782026-03-12 |
| spellingShingle | scale development perceived authenticity emotional validation emotion recognition human-AI interaction university students sex invariance measurement invariance generative AI AI ethics Social Robot Interaction and HRI AI in Service Interactions Psychometric Methodologies and Testing Berle Estalin Briones-Llamoctanta Ronald Garnique-Hinostroza Roberto Estrada-Medina Josue Edison Turpo-Chaparro Construction and validation of the Emotional Trust in Artificial Intelligence Scale (CEIA) |
| title | Construction and validation of the Emotional Trust in Artificial Intelligence Scale (CEIA) |
| title_full | Construction and validation of the Emotional Trust in Artificial Intelligence Scale (CEIA) |
| title_fullStr | Construction and validation of the Emotional Trust in Artificial Intelligence Scale (CEIA) |
| title_full_unstemmed | Construction and validation of the Emotional Trust in Artificial Intelligence Scale (CEIA) |
| title_short | Construction and validation of the Emotional Trust in Artificial Intelligence Scale (CEIA) |
| title_sort | construction and validation of the emotional trust in artificial intelligence scale ceia |
| topic | scale development perceived authenticity emotional validation emotion recognition human-AI interaction university students sex invariance measurement invariance generative AI AI ethics Social Robot Interaction and HRI AI in Service Interactions Psychometric Methodologies and Testing |
| topic_facet | scale development perceived authenticity emotional validation emotion recognition human-AI interaction university students sex invariance measurement invariance generative AI AI ethics Social Robot Interaction and HRI AI in Service Interactions Psychometric Methodologies and Testing |
| url | https://doi.org/10.3389/fpsyg.2026.1755160 https://public-pages-files-2025.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1755160/pdf |
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