With this Correction, the authors would like to replace references 46, 48 and 50 in the published paper

Background: Resilience has received considerable attention in recent years and is a psychological characteristic that favors positive adaptation to adversity. Objective: The objective of this work is to generate a Spanish adaptation of the Resilience Scale (ER, acronym in Spanish) and to study the dimensionality of the scale through confirmatory factor analysis (CFA). Methods: The ER was administered to 1058 young Spanish people. The original English version of the Resilience Scale (25 items) was translated into Spanish, and the translation was confirmed through a backtranslation. The original version consists of two major factors: personal competence and acceptance of oneself and life. Results: The results confirm the goodness of the psychometric characteristics of the scale (internal consistency and criterion validity) with 23 items and the original two-factor model proposed by the authors. There was a positive correlation between the ER-23 adaptation and the Connor–Davidson Resilience Questionnaire (2002) and subjective psychological well-being and a negative correlation between the ER-23 adaptation and anxiety. Conclusions: The ER-23 adaptation is a valid and reliable tool that can be used in future research in a university population.

Extended Reality (XR) and Artificial Intelligence (AI) are increasingly converging within cyber–physical infrastructures, including digital twins, the Spatial Web, and smart-city systems. These environments require new frameworks for understanding how human performance emerges through sustained interaction with immersive interfaces and adaptive computational agents. This paper introduces the TAXI–XI-CAP framework, a two-layer model that links psychobiological mechanisms of XR–AI interaction to higher-level, experimentally testable capability constructs. The TAXI layer defines 42 mechanisms spanning perception, cognition, physiology, sensorimotor control, and social coordination, while XI-CAP organizes these into capability patterns such as remote dexterity, distributed cognition, and adaptive workload regulation. Derived through a theory-guided synthesis across XR, neuroscience, and human–automation interaction, the framework models performance as emerging from interacting mechanisms under real-world constraints. A validation-oriented research agenda is proposed, emphasizing mechanism-level measurement, capability-level evaluation, and longitudinal testing. The TAXI–XI-CAP framework provides a structured basis for hypothesis generation, comparative analysis, and empirical validation of XR–AI systems, supporting the development of reliable, scalable, and human-centered Extended Intelligence infrastructures.