Development and validation of the AI-assisted psychoemotional regulation scale (ERP-IA-6)

Briones-Llamoctanta B.E.; Pacheco-Cavero J.A.; Callacondo-Quispe V.S.; Turpo-Chaparro J.E. · Frontiers in Psychology (2026) · DOI: 10.3389/fpsyg.2026.1917798

Resumen

Background – Conversational and generative artificial intelligence tools are increasingly used to organize thoughts, clarify emotional experiences, and cope with everyday distress. However, brief instruments assessing perceived AI-assisted psychoemotional regulation remain scarce. Objective – To develop the AI-Assisted Psychoemotional Regulation Scale (ERP-IA-6) and examine its initial psychometric properties among Peruvian adults. Methods – The initial eight-item pool was reviewed by five psychologists, and all items obtained Aiken’s V values above 0.80. A total of 538 adults participated (M age = 24.51 years, SD = 7.48; range = 18–81); 81.6% were aged 18–29 years, 52.0% were women, and 93.1% reported AI use. Using a reproducible sex-stratified procedure, the sample was divided into exploratory (n = 266) and confirmatory (n = 272) subsamples. Exploratory factor analysis used polychoric correlations and unweighted least squares; confirmatory factor analysis used WLSMV for ordinal indicators. Reliability, AVE-based convergent evidence, sensitivity analyses, shared-method diagnostics, and ordinal measurement invariance across sex were also examined. Results – Exploratory analyses supported reducing the scale from eight to six items based on conceptual redundancy and parsimony. The final version showed adequate factorability (KMO = 0.910; Bartlett’s χ2 (15) = 1128.37, p < 0.001) and a one-factor solution explaining 67.4% of standardized item variance, with loadings from 0.750 to 0.872. In the confirmatory subsample, loadings ranged from 0.839 to 0.930. Robust incremental and residual fit were favorable (CFI = 0.981; TLI = 0.968; SRMR = 0.019), although robust RMSEA was elevated, 0.118, 90% CI [0.093, 0.145]. Sensitivity analyses showed that the one-factor structure persisted, while localized residual dependence partly accounted for the elevated RMSEA. Reliability was high (α = 0.935; ordinal α = 0.945; ω = 0.948), as were composite reliability (CR = 0.959) and average variance extracted (AVE = 0.794). Threshold, metric, and scalar models provided favorable but cautious evidence of invariance across sex. Conclusion – The ERP-IA-6 provides favorable initial evidence as a brief measure with a dominant unidimensional structure for assessing perceived AI-assisted psychoemotional regulation among Peruvian adults. Scores should be interpreted as perceived regulatory support, not as evidence of clinical effectiveness, therapeutic safety, or equivalence to professional care. Copyright

Instituciones: Universidad Peruana Unión