Semi-Aura and the Perception of Authenticity in AI-Generated Art: An Empirical Study of Hybrid Authorship

Semi-Aura and the Perception of Authenticity in AI-Generated Art: An Empirical Study of Hybrid Authorship | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Semi-Aura and the Perception of Authenticity in AI-Generated Art: An Empirical Study of Hybrid Authorship David Salas Espasa, Maria del Mar Camacho Martí This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8994993/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract This study examines public perceptions of authenticity in AI-generated art through the conceptual framework of semi-aura , defined as the perceived retention of human intentional trace within algorithmic production. Drawing on a non-probabilistic sample of 172 participants, the research employs non-parametric comparative tests (Friedman and Kruskal–Wallis) and Spearman correlational analyses to explore generational, professional, and perceptual determinants of authenticity attribution. A Friedman test revealed significant differences in perceived authenticity across human, hybrid, and fully AI-generated works (χ²(2) = 246.43, p < .001), confirming a clear hierarchical evaluation pattern. Contrary to expectations derived from symbolic capital theory, no statistically significant differences were observed across levels of artistic experience. A small generational effect was identified (H(2) = 11.41, p = .003, ε² = .06), with younger participants (18–29 years) assigning lower authenticity ratings to AI-generated works. Semi-aura demonstrated good internal consistency (Cronbach’s α = .84) and showed a moderate positive correlation with the belief that AI-generated art will become more important than human art in the future (ρ = .46, p < .001), as well as with perceived necessity of professional adaptation (ρ = .39, p < .001). Additionally, a moderate negative association between labeling sensitivity and authenticity attribution (ρ = −.45, p < .001) indicates that origin-awareness mechanisms substantially shape evaluative judgments. Contrary to expectations, theoretical AI knowledge did not significantly correlate with semi-aura perception (ρ = −.085, p = .268). The findings suggest that authenticity perceptions in hybrid creative ecologies are structured less by professional capital defense and more by generational orientation and perceived continuity of human agency within algorithmic systems. Semi-aura is proposed as a perceptual mediator of technological legitimacy in contemporary artistic contexts. Humanities/Cultural and media studies Social science/Cultural and media studies Biological sciences/Psychology Social science/Psychology Social science/Science technology and society Semi-aura AI-generated art Authenticity Hybrid authorship Algorithmic creativity Digital humanities Technological legitimacy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The rapid diffusion of generative AI systems has not only expanded the scale of cultural production but also reshaped the epistemological and evaluative frameworks through which creativity, authorship, and authenticity are interpreted. Contemporary debates on algorithmic aesthetics emphasize that AI-mediated creation reconfigures long-standing assumptions about artistic intentionality and originality (Manovich, 2018 ; Boden, 2016 ). At the same time, critical scholarship has highlighted that AI systems function as sociotechnical infrastructures embedded in broader cultural and political dynamics rather than neutral creative tools (Crawford, 2021 ). From the perspective of digital mediation theory, these transformations affect how cultural meaning and legitimacy are socially constructed within increasingly hybrid human–machine environments (Couldry & Hepp, 2017 ). Within this shifting landscape, questions of authenticity and value attribution acquire renewed urgency. The emergence of diffusion models such as Midjourney or DALL-E has shifted the debate on automation from the sphere of industrial productivity to the core of human creativity, intensifying tensions surrounding authenticity, authorship, and aesthetic value in contemporary art. What Benjamin ( 1936 ) diagnosed as the loss of aura in the age of mechanical reproducibility now reaches a new dimension: unlike the technologies originally analyzed, current generative models do not merely multiply copies of an original, but produce formally new images through algorithmic processes that pose an ontological challenge: can a machine generate “singular” works without a direct human author? This transformation reopens the question of aura in a context where singularity no longer depends exclusively on materiality or irreproducible physical presence. Benjamin’s theory situated aura in distance, uniqueness, and historical inscription. In the contemporary digital environment, however, the relationship between originality and reproduction is altered by the statistical synthesis capacity of AI systems. From a critical perspective, this shift resonates with Baudrillard’s ( 1981 ) notion of the simulacrum, in which images progressively detach from stable referents. However, contemporary AI-mediated production cannot be reduced to a simple opposition between human authenticity and algorithmic simulation, as hybrid creative configurations introduce more nuanced forms of authorship and intentionality. Empirical research in the psychology of art has consistently shown that knowledge of the production process significantly influences aesthetic evaluation (Newman & Bloom, 2012 ). More recent studies comparing human and AI-generated artworks further indicate that authorship attribution and perceived intentionality substantially shape evaluative judgments (Chamberlain & Pepperell, 2021 ). The perception of effort, agency, and creative intention emerges as a central variable in authenticity attribution. Meanwhile, the sociology of culture has emphasized that artistic legitimacy is not merely a formal property but a socially constructed value embedded within structures of symbolic capital (Bourdieu, 1979 ). Recent work has also identified an anthropocentric bias in the evaluation of AI-generated art, particularly when human authorship is perceived as absent (Millet et al., 2023 ). In this transitional scenario, this article proposes that authenticity should no longer be understood as a binary value, but as a graded perceptual spectrum, which we term “semi-aura.” Rather than replacing the Benjaminian notion, semi-aura seeks to empirically examine whether the presence of a human trace within generative processes—especially in hybrid or co-creative configurations—is perceptually recognized as a qualitative distinction that rescues the artwork from algorithmic inauthenticity. The study aims to validate this construct empirically and to analyze how age, artistic experience, and digital literacy modulate authenticity perception in a sample of 172 participants, with particular attention to professionals and students in the artistic field. In doing so, the study contributes to digital humanities research by empirically investigating how algorithmic mediation reshapes authenticity frameworks in contemporary cultural production. This debate, while ontological in scope, is addressed here from a strictly perceptual and empirical perspective. The objective is not to redefine the metaphysical status of aura, but to examine how audiences attribute differentiated degrees of authenticity according to the perceived level of human intervention. Theoretical Framework 2.1 Aura and Reproducibility: From the Benjaminian Paradigm to Algorithmic Generation The notion of aura formulated by Walter Benjamin ( 1936 ) constitutes one of the central axes of the debate on authenticity in modern art. For Benjamin, aura refers to the irreproducible presence of the artwork in time and space, linked to its material singularity and historical embeddedness. In the contemporary context, algorithmic generation no longer reproduces a pre-existing original, but produces new images based on statistically trained models. This capacity for synthesis raises the question of whether singularity may derive not from materiality, but from the generative process itself (Park, 2024 ). From a critical perspective, if autonomous AI generates what Baudrillard ( 1981 ) calls a “pure simulacrum,” hybridization could disrupt this logic by reintroducing authorial will. 2.2 Trace, Intentionality, and Perception The notion of “trace” formulated by Derrida ( 1967 ) allows the issue to be addressed from a fundamental post-structuralist perspective: the trace is not merely a material mark, but a vestige that refers to a deferred presence. In algorithmic co-creation, audiences no longer seek only a physical mark, but the “intellectual trace” of the artist who has guided the algorithm through prompting, iterative selection, and curatorial decision-making. As Salas Espasa and Camacho ( 2025 ) argue, semi-aura emerges precisely in this dialogue where knowledge of the process influences belief in an underlying human intentionality, thereby modulating emotional and evaluative responses (Newman & Bloom, 2012 ; Pelowski et al., 2017 ). 2.3 Symbolic Capital and Legitimacy in the Artistic Field From the sociology of culture, Bourdieu ( 1979 ) argues that artistic value is embedded in structures of symbolic capital and mechanisms of professional distinction. Legitimacy depends on position within the field and institutional recognition. The emergence of generative systems may activate tensions related to traditional mechanisms of distinction, with anthropocentric bias interpreted as a symbolic protection mechanism of the field in response to technologies that lower entry barriers (Millet et al., 2023 ). Recognized human intervention in hybrid practices shifts the issue toward a terrain in which legitimacy depends on the perceived degree of human participation. 2.4 Systematic Review on Authenticity and AI A recent systematic review on authenticity and AI-generated art (Salas Espasa & Camacho, 2025 ) notes that most studies tend to adopt conceptual approaches or binary comparisons between human and algorithmic production. According to this review, hybrid forms of co-creation have received limited empirical attention despite their increasing prevalence. This gap justifies the exploration of an intermediate construct capable of capturing perceptual variations according to the perceived level of human intervention, rather than asking whether art “has aura” in an absolute sense. In this article, semi-aura is understood exclusively as an operative perceptual category. It is not proposed as an ontological reformulation of the Benjaminian concept, but as an analytical tool to describe variations in authenticity attribution within generative environments. 2.5 Posthumanism and Distributed Creativity Posthumanist approaches (Hayles, 1999 ; Braidotti, 2013 ) propose a relational conception of subjectivity in which the boundary between human and technology becomes porous. From this perspective, creativity is understood as a distributed process between human agents and technical systems, in which the machine does not replace the human but amplifies expressive capacity. Semi-aura functions here as a descriptive category to analyze whether participants perceptually differentiate between autonomous algorithmic production and guided production, capturing recognition of this shared authorship. Objectives 3.1. Validation of the “semi-aura” construct: To determine whether “semi-aura” constitutes a perceptual dimension with sufficient internal consistency to be considered a valid analytical category within contemporary art theory. 3.2. Comparison of authenticity and aura levels according to the origin of the work: To establish a quantitative comparison between works created exclusively by humans, works generated autonomously by AI, and hybrid works (co-creation), in order to identify whether human intervention in prompting and curation significantly alters the reception of the artwork. 3.3. Analysis of the “symbolic capital” bias in the professional sector: To investigate how prior artistic experience and academic training in fine arts and design condition the evaluation of algorithmic art, exploring the possible existence of a defensive or protectionist stance toward the automation of creative processes. 3.4. Analysis of generational differences in perception: To examine the contrast between so-called “digital natives” and senior generations, testing the widespread belief that greater technological familiarity necessarily entails greater acceptance of artificial authenticity. 3.5. Identification of variables associated with AI acceptance in the future of art: To determine which variables (theoretical knowledge, frequency of use, or perception of semi-aura) act as key indicators in predicting the integration and acceptance of these tools in the artistic and professional market in the coming years. Hypotheses The following working hypotheses are established: H1: The influence of theoretical knowledge. It is postulated that greater self-reported theoretical knowledge about the functioning of Artificial Intelligence will positively correlate with the ability to perceive “semi-aura.” H2: The protective bias of artistic experience. It is hypothesized that participants with a consolidated professional or academic background in fine arts and design will assign significantly lower authenticity ratings to AI-generated art compared to novices. H3: The superiority of hybrid works. It is predicted that works perceived as “hybrid” (human–machine co-creation) will obtain significantly higher ratings of artistic value and aura than works generated entirely autonomously by AI. H4: The age paradigm (Digital Natives). It is hypothesized that participants in the youngest cohort (18–29 years), having grown up in a ubiquitous digital environment, will demonstrate greater openness and assign higher levels of authenticity to AI-generated art than senior generations. H5: Semi-aura as the variable most strongly associated with future acceptance. It is postulated that the perception of semi-aura (rather than technical knowledge or age) will show the strongest associative relationship with the acceptance of AI as a legitimate creative tool and with the perceived need for professional adaptation in the future. Method 4.1 Research Design The study adopts a cross-sectional descriptive-correlational design with a quantitative approach. The objective is not to establish causal relationships, but to identify associative patterns in the perception of authenticity attributed to different types of artwork in a context of algorithmic generation. This design is appropriate for exploratory studies on aesthetic perception, particularly when working with complex constructs such as authenticity or perceived human trace, which require comparative measurement across defined conditions (Creswell & Creswell, 2018). 4.2 Procedure and Data Collection Data collection was conducted between February 5 and February 12, 2026, using the Google Forms platform. The questionnaire was distributed through academic networks, educational environments partially linked to the artistic field, and personal contacts, using convenience sampling (Bryman, 2016). Three equivalent linguistic versions (Catalan, Spanish, and English) were developed, maintaining conceptually aligned structure, order, and wording. The use of multiple languages aimed to reduce potential semantic comprehension biases in theoretical terms such as “aura,” “authenticity,” or “human intervention.” The three resulting datasets were consolidated into a single analytical matrix, standardizing nomenclature and categories prior to statistical analysis. 4.3 Participants The initial dataset included 182 responses. Before analysis, predefined exclusion criteria were applied: Removal of participants under 18 years of age (n = 9). Removal of one record with a manifestly erroneous age (397 years). The final analyzed sample consisted of N = 172 participants, aged between 18 and 78 years (M = 35.61; SD = 13.77). A detailed overview of sociodemographic and professional characteristics is presented in Table 1. Table 1 Sociodemographic and professional characteristics of the sample Variable n % Age cohorts 18–29 71 41.3 30–40 49 28.5 41+ 52 30.2 Artistic experience Professional / Student 100 58.1 Amateur 13 7.6 No formal experience 59 34.3 Descriptive statistics for age: M = 35.61, SD = 13.77, range = 18–78. Regarding artistic experience: 58.1% professionals or students in fine arts, design, or related fields. 7.6% amateurs. 34.3% without formal artistic experience. The 18–29 age cohort represents 41.3% of the sample, reflecting an overrepresentation of younger participants relative to the general demographic distribution. Full distribution of sociodemographic and professional characteristics. It is important to note that, given the non-probabilistic nature of the sampling, the results should be interpreted as internal patterns within the analyzed sample composition rather than as representative estimates of the general population. 4.4 Instrument The questionnaire was structured into four sections: Sociodemographic variables (age, gender, level of education, country). Artistic experience and educational background. Degree of use and knowledge of generative AI tools. Authenticity and semi-aura perception scale. Ratings were collected using 0–10 scales. The use of 11-point scales is justified by their greater discriminative capacity compared to narrower scales (Revilla et al., 2014) and is consistent with common practices in social science research (Allen & Seaman, 2007). Labeling sensitivity was operationalized as a single-item measure (Item 5.3 in Appendix A), assessing the extent to which participants reported that knowledge of AI authorship would alter their perception of authenticity. This variable was treated as an indicator of origin-awareness effects in authenticity attribution. The internal consistency of the semi-aura block was assessed using Cronbach’s alpha coefficient. The semi-aura construct was operationalized using four items (6.3–6.6), which specifically capture the perceived retention, transformation, or procedural mediation of aura in AI and hybrid artworks. Items 6.1 and 6.2 were excluded from this composite measure, as they assess baseline perceptions of human and AI aura independently rather than the graded or hybrid dimension conceptualized as semi-aura. Given the exploratory nature of the study and the available sample size, confirmatory factor analysis was not conducted. At this stage, construct validation was limited to internal consistency assessment. Given the limited number of theoretically aligned items (four), exploratory factor analysis was not deemed necessary. The obtained value (α = .84) exceeds the recommended threshold of 0.70 for social science research (Nunnally & Bernstein, 1994) and is interpreted as an indicator of high reliability (Taber, 2018). Item-level statistics and corrected item–total correlations are reported in Table 2. Table 2 Descriptive statistics and reliability indices for the semi-aura scale (N = 172) Item (abridged) M SD Corrected Item–Total r AI works retain a certain degree of aura 4.32 3.00 .69 Human prompt intention increases authenticity 4.38 3.08 .65 Hybrid works may have distinct aura/value 6.63 2.68 .62 AI transforms rather than eliminates aura 4.99 3.09 .73 Cronbach’s α = .84 The full questionnaire is provided in Appendix A. 4.5 Statistical Analysis Strategy Prior to inferential analysis, normality of distributions was examined using the Shapiro–Wilk test. Results indicated significant deviations from normality (p < 0.05), which motivated the use of non-parametric tests, following methodological recommendations by Field (2017) and Hair et al. (2019). The following analyses were conducted: For within-subject comparisons between the three artwork conditions, a Friedman test was conducted. Spearman correlations (ρ) were used to examine associative relationships. Analyses were performed using Python 3.x, employing the Pandas library for data management (McKinney, 2010) and SciPy for inferential statistics. Given the cross-sectional design, all identified relationships are interpreted in associative rather than causal terms. Post hoc pairwise comparisons following the Friedman test were performed using Dunn’s test with Bonferroni correction. Results 5.1 General Evaluation Pattern According to Artwork Type The descriptive analysis shows clear differences in perceived authenticity according to artwork origin (see Table 3 and Figure 1). Works created exclusively by a human artist obtain the highest mean score (M = 9.14), with a relatively low standard deviation, indicating a high level of consensus among participants. In contrast, works generated autonomously by an AI system receive the lowest mean score (M = 2.67), with moderate dispersion and concentration in the lower values of the scale. Hybrid works—defined as productions generated with explicit human intervention in the algorithmic process—occupy an intermediate position (M = 6.66), with greater dispersion than human-produced art, suggesting higher heterogeneity in their perception. A Friedman test revealed statistically significant differences in perceived authenticity across the three conditions, χ²(2) = 246.43, p < .001, Kendall’s W = .73, indicating a large effect size according to conventional benchmarks for non-parametric repeated-measures designs. The Friedman test was conducted on complete cases (N = 168), as four participants had missing responses in at least one of the three artwork conditions. The overall valid sample consisted of N = 172 participants after applying predefined exclusion criteria. Post hoc Dunn–Bonferroni comparisons confirmed that all pairwise differences were statistically significant (see Figure 1). TABLE 3 Comparison of perceived authenticity across artwork conditions Condition M SD Human-generated artwork 9.14 1.21 Hybrid (human–AI co-creation) 6.66 2.18 Fully AI-generated artwork 2.67 2.36 Friedman Test Results χ²(2) = 246.43, p < .001 Kendall’s W = .73 N = 168 Note. The Friedman test was conducted on participants with complete responses across the three conditions (N = 168), within a final sample of 172 valid participants. Post hoc pairwise comparisons using Dunn’s test with Bonferroni correction indicated that all condition contrasts were statistically significant (human vs. hybrid: p < .001; human vs. AI: p < .001; hybrid vs. AI: p < .001). 5.2 Differences According to Artistic Experience Participants were initially classified into three categories of artistic experience: professionals/students in the arts, amateurs, and individuals without formal artistic experience. However, due to the very small number of valid responses within the amateur subgroup for the AI-authenticity variable, statistical comparisons were conducted between participants with formal artistic experience (professionals/students) and those without formal experience. This decision ensured sufficient statistical power and robustness of non-parametric group comparisons. A Mann–Whitney U test indicated no statistically significant differences in perceived authenticity of AI-generated art between participants with formal artistic experience (professionals/students; n = 114) and those without formal experience (n = 58), U = 2983, p = .286 (see Figure 2). The distributions showed substantial overlap between the two groups. Regarding hybrid works, all groups show a notable increase in valuation compared to autonomous AI production, with less pronounced internal differences than in the case of exclusively algorithmic production. Additionally, no statistically significant differences were observed in perceived authenticity as a function of participants’ gender or geographic origin (p > 0.05). 5.3 Differences According to Age Cohort A Kruskal–Wallis test revealed statistically significant differences in perceived authenticity of AI-generated art across age cohorts, H(2) = 11.41, p = .003, ε² = .06. Participants aged 18–29 reported the lowest mean scores (M = 1.97), whereas those aged 41 and above assigned comparatively higher authenticity ratings (M = 3.34) (see Figure 3). The intermediate cohort (30–40 years) fell between these groups. The distributions suggest notable within-group variability, particularly among older participants. 5.4 Associative Relationships Spearman correlation analyses revealed a moderate positive association between semi-aura perception and the belief that AI-generated art will become more important than human art in the future, ρ = .46, p < .001 (N = 172) (see Figure 4). A secondary correlation indicated that higher semi-aura scores were also associated with stronger agreement that artists will need to adapt to AI tools to remain relevant, ρ = .39, p < .001 (see Figure 5). Additionally, a moderate negative association was found between labeling sensitivity and perceived authenticity of AI-generated art, ρ = −.45, p < .001 (see Figure 6). These findings support the construct validity of semi-aura as a perceptual mediator closely associated with future-oriented attitudes toward AI in artistic contexts. Contrary to H1, no statistically significant association was found between self-reported theoretical knowledge of AI and semi-aura perception (Spearman’s ρ = −.085, p = .268; N = 172). This suggests that perceptual recognition of human trace in hybrid production is not directly linked to technical familiarity with AI systems. 5.5 Scale Reliability The semi-aura scale demonstrates high internal consistency (α = .84), substantially exceeding the recommended threshold for social science research. Item–total correlations remain within adequate ranges, with no indication of dysfunctional items (see Table 2). Discussion Beyond the statistical description presented in the previous section, the results allow for a theoretical reading of how perceptions of authenticity are reconfigured in environments of algorithmic generation. 6.1 Semi-Aura as a Transcendence of the Simulacrum One of the central contributions of this study is the empirical articulation of “semi-aura” as a perceptual construct. The data indicate that exclusively AI-generated works receive comparatively low authenticity ratings (M = 2.67), in contrast with human-produced art (M = 9.14). This marked evaluative distance, confirmed through non-parametric testing (see Table 3), suggests that algorithmic production is not yet perceived as equivalent to human authorship in terms of authenticity attribution. Rather than interpreting this pattern as a definitive ontological rupture, the findings invite a reconsideration of classical aesthetic frameworks. In Benjaminian terms (1936), aura does not appear to function as a binary category of presence or absence, but as a graded perceptual spectrum. Hybrid works, positioned at an intermediate level of authenticity (M = 6.66), indicate that audiences are capable of recognizing procedural human mediation within algorithmic systems. This intermediate position may be interpreted, in Derridean terms (1967), as the persistence of a “trace”: a residual sign of human intentionality that anchors the work to a subject, even when the material execution is algorithmically mediated. The audience does not appear to require exclusive manual authorship, but rather perceptible evidence of decision, direction, or conceptual agency. In this sense, AI-generated art may be read not strictly as a simulacrum in the Baudrillardian sense (1981), but as a production whose legitimacy remains contingent upon the visibility of such trace. This suggests that aura, rather than dissolving under algorithmic reproducibility, undergoes a perceptual displacement toward procedural mediation. One of the most theoretically suggestive findings of this study concerns the non-confirmation of Hypothesis 4 (H4). While significant generational differences were observed in perceived authenticity of AI-generated art, these differences were small in magnitude (ε² = .06) and do not support a deterministic association between technological familiarity and greater acceptance of algorithmic authorship. The youngest cohort (18–29 years) reported the lowest authenticity scores (M = 1.97), challenging the conventional assumption that digital nativity necessarily entails aesthetic endorsement of artificial production. The delimitation of this age range follows the conceptualization of “digital natives” (Prensky, 2001). The 18–29 cohort is commonly used in contemporary survey research to capture early adult populations socialized in digital environments. Importantly, this generational skepticism cannot be reduced to professional self-interest. The analysis of artistic experience revealed no statistically significant differences in authenticity attributions across levels of formal artistic training. This suggests that resistance toward AI-generated authorship is not confined to professionally invested actors but reflects a broader cultural negotiation of authorship, agency, and human intentionality in the age of generative systems. A central explanatory factor for this skepticism appears to be the weight attributed to the genesis of the artwork, commonly described as labeling bias. As Newman and Bloom (2012) demonstrate, aesthetic evaluation is deeply intertwined with perceived origin history. In the present study, a moderate negative correlation was found between sensitivity to origin labeling and authenticity ratings of AI-generated art (ρ = −.45, p < .001). In other words, participants who reported that knowing an artwork’s origin would significantly alter their judgment were precisely those who assigned lower authenticity scores to algorithmic production. This pattern aligns with experimental findings showing that when origin information is concealed, AI-generated works may be evaluated more favorably, whereas explicit disclosure of algorithmic authorship significantly reduces perceived authenticity (Bellaiche et al., 2023). Together, these results suggest that skepticism is not merely a reaction to visual output, but to the ontological framing of non-human production itself; the label “AI” operates as a heuristic trigger that modulates perceived aura. While this resistance is present across the sample, it manifests more clearly within the youngest cohort. Rather than interpreting this pattern exclusively as a defensive strategy of symbolic capital protection in the Bourdieusian sense (Bourdieu, 1979), the absence of significant professional differentiation points toward a more generalized anthropocentric orientation. As Millet et al. (2023) propose, contemporary responses to automation often involve a defensive reaffirmation of human creative exclusivity. In this context, skepticism toward AI-authenticity may reflect not merely institutional self-preservation but a generational rearticulation of human exceptionalism under conditions of algorithmic ubiquity. This interpretation remains provisional, given the non-probabilistic nature of the sample. Further research employing representative designs will be necessary to determine whether the observed generational pattern persists at the population level and to clarify the relative contribution of symbolic capital, anthropocentric bias, and perceptual semi-aura orientation in shaping authenticity judgments. 6.3 Auratic Literacy and the Future of the Profession The empirical findings do not indicate that mere frequency of AI-tool usage is sufficient to account for semi-aura perception. Rather than technological familiarity per se, the data suggest that perceptual orientation toward human trace is the more decisive factor. Semi-aura, operationalized as a perceptual construct (α = .84), appears to capture a cognitive disposition toward recognizing intentional mediation within algorithmic production. Conceptually, this pattern indicates that the perception of “human trace” in hybrid environments shifts from manual gesture toward decision-making, selection, and conceptual refinement. Semi-aura does not imply a restoration of traditional material aura, but rather a recognition of distributed yet human-directed agency within generative systems. The data further indicate that semi-aura shows strong associative alignment with both future-oriented technological acceptance (ρ = .46, p < .001) and perceived necessity of professional adaptation (ρ = .39, p < .001). Rather than functioning as a statistical predictor in a causal sense, semi-aura emerges here as the variable most consistently associated with future-oriented legitimacy attitudes within the present sample. At the same time, a moderate negative association between labeling sensitivity and authenticity attribution (ρ = −.45, p < .001) suggests that origin-awareness mechanisms significantly modulate evaluative judgments. Given the cross-sectional and non-probabilistic nature of the sample, these relationships must be interpreted as associative rather than causal. No multivariate modeling was conducted; therefore, potential confounding interactions between age, professional experience, and perceptual orientation remain open to further investigation. Rather than asserting a definitive transformation of aura in the Benjaminian sense, the results point toward a perceptual reconfiguration of artistic value in hybrid contexts. Professional legitimacy may increasingly depend not on manual execution alone, but on curatorial agency, conceptual direction, and procedural authorship. In this light, the artist does not disappear but is repositioned within a shared creative ecology, consistent with the reframing proposed by Salas Espasa and Camacho (2025). Limitations and Future Research Despite the statistical significance of the findings and the high reliability of the analyzed constructs, this study is subject to several limitations that must be carefully considered when interpreting the results. 7.1 Sampling Bias and Professional Representativeness Although the sample includes a high proportion of participants with formal artistic training, no statistically significant differences were observed across levels of artistic experience. Therefore, while the composition of the sample may limit generalizability, the data do not support the interpretation of a systematic professional protection bias within this data set. 7.2 The Digital Divide and Technological Exclusion It is necessary to emphasize a digital divide bias inherent in the research format. The exclusively online nature of the data collection instrument systematically excluded profiles with low digital literacy or without regular access to virtual environments. This limitation is particularly acute in older cohorts (such as the senior population over 75 years of age), who, despite possessing historically and culturally valuable perspectives on art, remain outside the debate due to technical barriers. This exclusion suggests that the results regarding senior openness may be biased toward those who are already technologically integrated. 7.3 Geographic Scope and Cultural Bias Although the sample included international participants, the majority of responses were collected within Catalonia and Spain. Public perception of AI is deeply influenced by local cultural narratives and by Western conceptions of authorship. As Han (2017) suggests in his critique of the notion of “copy,” in East Asia the concept of originality differs significantly from the European canon, with the perfect replica often valued as a form of respect and technical mastery. Therefore, the “semi-aura” construct defined here could be perceived very differently in other cultural contexts where the human “trace” is not as closely tied to the individual identity of the author. 7.4 Subjectivity of Terminology and Temporal Scope The concepts of “aura” and “authenticity” are inherently abstract. Although the high Cronbach’s alpha (α = .84) indicates that participants shared a coherent internal logic, individual interpretations may vary according to cultural background. Moreover, this research represents a cross-sectional snapshot from February 2026; given that technology evolves at an unprecedented pace, the perception of “disruptive novelty” may normalize within a few years, potentially modifying the acceptance thresholds presented here. 7.5 Future Research Directions Based on the identified constraints, the following avenues for future research are proposed: Qualitative validation with experts: It would be of particular interest to complement these quantitative data with a study based on in-depth interviews with experts (curators, art critics, and technologists). This phase would allow the validity of “semi-aura” to be examined from a professional perspective and to analyze how this construct might influence future valuation and certification protocols for hybrid artworks. Experimental studies with visual stimuli: Conduct “aesthetic Turing tests” in which participants evaluate the aura of artworks without prior knowledge of authorship, in order to observe whether semi-aura perception is maintained intuitively when confronted with the artistic object. Cross-cultural research: Replicate the study in East Asian countries to validate the universality or cultural specificity of the semi-aura construct. Inclusion of analog methods: Design data collection strategies that overcome the digital divide in order to capture perceptions from senior populations without digital literacy. Conclusions This research has made it possible to map the complex transformation of auratic experience in AI-generated art in early 2026. Based on the analysis of the collected data and its contrast with the theoretical framework of postmodernity, the following fundamental conclusions are established: 8.1 The Emergence of Semi-Aura as a Spectrum The study provides empirical support for the articulation of a perceptual construct termed semi-aura. The results indicate that aura no longer operates as a binary value of presence or absence, but as a graded spectrum. With a mean rating of M = 6.66, hybrid art surpasses the authenticity void of autonomous AI production (M = 2.67), occupying an intermediate space of legitimation. These findings suggest that audiences are capable of valuing the “human trace” embedded in prompt intentionality and curatorial mediation, rescuing the work from the condition of pure simulacrum. 8.2 Generational Differentiation and the Limits of Symbolic Capital Explanation The findings invite a nuanced reconsideration of symbolic capital theory (Bourdieu, 1979 ) in the context of AI-generated art. While one might expect professionally invested actors to exhibit stronger resistance toward algorithmic authorship as a strategy of capital protection, no statistically significant differences were observed across levels of artistic experience in this study. This absence of professional differentiation suggests that authenticity skepticism toward AI cannot be reduced to field-specific defensive mechanisms. At the same time, significant generational differences were identified, with participants aged 18–29 assigning lower authenticity ratings to AI-generated works (M = 1.97), compared to older cohorts (M = 3.34 for participants aged 41 and above). Although the effect size was small (ε² = .06), this pattern challenges the assumption that technological familiarity necessarily entails aesthetic acceptance. Rather than reflecting a strictly professional struggle for symbolic capital, the observed generational skepticism may indicate a broader anthropocentric orientation in which human intentionality remains central to authenticity judgments. In this light, symbolic capital remains a relevant theoretical lens, but its explanatory scope appears limited in hybrid creative ecologies. The data suggest that resistance to AI-authorship may operate less through institutional field competition and more through culturally embedded evaluative schemas concerning human agency, origin, and authorship. Further research with probabilistic samples will be required to determine whether these dynamics persist at the population level. 8.3 Semi-Aura as a Perceptual Mediator of Technological Legitimacy The empirical validation of semi-aura constitutes one of the central contributions of this study. Operationalized as a four-item perceptual construct (Cronbach’s α = .84), semi-aura captures the degree to which individuals attribute residual human intentionality—or a transformed modality of aura—to AI-mediated artistic production. Rather than measuring acceptance of technology per se, the construct indexes a perceptual orientation toward recognizing human trace within algorithmic systems. A moderate positive association was found between semi-aura perception and the belief that AI-generated art will become more important than human art in the future (ρ = .46, p < .001; N = 172). A secondary correlation indicated that higher semi-aura scores were also associated with stronger agreement that artists will need to adapt to AI tools in order to remain relevant (ρ = .39, p < .001). These associations suggest that perceived continuity of human agency within generative processes appears to operate as a perceptual bridge in future-oriented legitimacy attribution. Additionally, a moderate negative correlation was observed between labeling sensitivity and perceived authenticity of AI-generated art (ρ = −.45, p < .001), indicating that origin-awareness mechanisms substantially shape evaluative judgments. Participants who reported that knowledge of an artwork’s origin would alter their perception were precisely those assigning lower authenticity scores to AI-generated works. This reinforces the interpretation that authenticity judgments are structured not solely by visual properties, but by perceived ontological status and authorial trace. Given the cross-sectional and non-probabilistic nature of the sample, these relationships must be interpreted as associative rather than causal. No multivariate modeling was conducted; therefore, potential interactions between generational effects, professional experience, and perceptual orientation remain open to future investigation. Rather than implying a definitive metaphysical transformation of aura, the findings point toward a perceptual reconfiguration of artistic legitimacy in hybrid contexts. Legitimacy appears increasingly linked to the perceived continuity of human intentional mediation within algorithmic systems, rather than to manual execution alone. In this sense, semi-aura functions as a transitional perceptual category through which audiences negotiate authenticity under conditions of distributed and technologically mediated creativity. 8.4 Persistence of Human-Centered Authenticity Despite the emergence of hybrid legitimization patterns, the data indicate that perceived artistic authenticity remains strongly associated with exclusively human production (M = 9.14). AI-generated works continue to receive substantially lower authenticity ratings (M = 2.67), while hybrid productions occupy an intermediate evaluative space (M = 6.66). These findings suggest that, at the time of data collection (early 2026), human authorship remains the dominant reference point in authenticity attribution. Rather than signaling the disappearance of aura, the results point toward its perceptual redistribution. Semi-aura appears to function as a mediating category that allows participants to negotiate authenticity within hybrid creative systems without fully abandoning the human-centered evaluative framework. The artist, therefore, is not displaced, but neither is their position empirically guaranteed; legitimacy in generative contexts appears contingent upon perceived intentional mediation rather than ontological exclusivity. 8.5 Practical Implications The findings carry potential implications for institutional and professional contexts in which AI-mediated artistic production is increasingly present. For museums and curatorial institutions, the results suggest that audience evaluations of authenticity are influenced not solely by visual output but by perceived transparency of human intentional mediation. Exhibition strategies that foreground process documentation, prompt intentionality, and curatorial framing may therefore play a central role in shaping legitimacy perceptions of hybrid works. In art education and design schools, the identified generational skepticism indicates that technological familiarity does not automatically translate into aesthetic acceptance. Pedagogical approaches that emphasize critical engagement with algorithmic systems—rather than merely technical proficiency—may help students articulate their position within hybrid creative ecologies without framing AI as a simple threat to authorship. For the art market and authorship certification systems, the emergence of semi-aura as a perceptual mediator suggests that valuation practices may increasingly depend on documenting procedural authorship and decision-making agency. Transparent disclosure of human–machine interaction processes could mitigate labeling bias effects and contribute to more stable legitimacy frameworks for hybrid production. These implications do not prescribe normative solutions but indicate domains in which perceptual dynamics identified in this study may have practical consequences. Declarations Ethical Approval This research was conducted in accordance with the ethical guidelines for research involving human participants at the Universitat Rovira i Virgili (URV). The project was registered within the institutional ethics management system (CEIPSA), reference number CEIPSA-2023-TDO-0080. Given that the study involved voluntary adult participants, ensured anonymity, and did not collect sensitive personal data, it was conducted under the institutional framework for minimal-risk research in the social sciences. All procedures complied with the Declaration of Helsinki and Regulation (EU) 2016/679 (GDPR). Informed Consent Informed consent was obtained electronically from all participants prior to their participation. Participants were recruited through an academic invitation and provided with an information sheet detailing the study's doctoral nature, the objectives of the research, and the anonymous treatment of data. The full text of the information and consent statement presented to participants is available in Appendix A. Author Contribution D.S.E. conceptualized the study, designed the methodology, conducted the statistical analyses, and wrote the main manuscript text. M.C. contributed to the theoretical framework and reviewed the manuscript. All authors approved the final version of the manuscript. Data Availability The de-identified dataset supporting the findings of this study is available from the corresponding author upon reasonable request. Funding Declaration The authors declare that no specific funding, grants, or financial support were received from any public or private agencies for the research, authorship, or publication of this article. References Allen, I. E., & Seaman, C. A. (2007). Likert scales and data analyses. Quality Progress, 40 (7), 64–65. Baudrillard, J. (1981). Simulacres et simulation . Galilée. Bellaiche, L., Shahi, R., Turpin, M. H., Ragnhildstveit, A., Sprockett, S., Barr, N., & Bloom, P. (2023). Humans versus AI: Whether and why we prefer human-created compared to AI-created artwork. Cognitive Research: Principles and Implications, 8 , 42. https://doi.org/10.1186/s41235-023-00499-6 Benjamin, W. (1936). The work of art in the age of mechanical reproduction. In H. Arendt (Ed.), Illuminations (pp. 217–251). Schocken Books. Boden, M. A. (2016). AI: Its Nature and Future . Oxford: Oxford University Press. Bourdieu, P. (1979). La distinction: Critique sociale du jugement . Les Éditions de Minuit. Braidotti, R. (2013). The posthuman . Polity Press. Bryman, A. (2016). Social research methods (5th ed.). Oxford University Press. Chamberlain, R., & Pepperell, R. (2021). The aesthetic experience of artworks produced by humans and machines. AI & Society, 36 , 809–821. https://doi.org/10.1007/s00146-020-01060-6 Couldry, N., & Hepp, A. (2017). The Mediated Construction of Reality . Cambridge: Polity Press. Crawford, K. (2021). Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence . New Haven & London: Yale University Press. Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications. Derrida, J. (1967). De la grammatologie . Les Éditions de Minuit. Field, A. (2017). Discovering statistics using IBM SPSS Statistics (5th ed.). SAGE Publications. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage. Han, B.-C. (2017). Shanzhai: Deconstruction in Chinese . MIT Press. Hayles, N. K. (1999). How we became posthuman: Virtual bodies in cybernetics, literature, and informatics . University of Chicago Press. Manovich, L. (2018). AI Aesthetics . Moscow: Strelka Press. McKinney, W. (2010). Data structures for statistical computing in Python. Proceedings of the 9th Python in Science Conference , 51–56. Millet, K., Buehler, F., Du, G., & Kokkoris, M. D. (2023). Defending humankind: Anthropocentric bias in the appreciation of AI art. Computers in Human Behavior, 143 , 107707. https://doi.org/10.1016/j.chb.2023.107707 Newman, G. E., & Bloom, P. (2012). Art and authenticity: The importance of originals in judgments of value. Journal of Experimental Psychology: General, 141 (3), 558–569. Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill. Park, S. (2024). The work of art in the age of generative AI: Aura liberation and democratization. AI & Society . https://doi.org/10.1007/s00146-024-01913-3 Pelowski, M., Markey, P. S., Lauring, J. O., & Leder, H. (2017). Visualizing the impact of art: An update and comparison of current psychological models of art experience. Frontiers in Human Neuroscience, 11 , 160. Prensky, M. (2001). Digital natives, digital immigrants part 1. On the Horizon, 9 (5), 1–6. Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 (General Data Protection Regulation). Revilla, M., Saris, W. E., & Krosnick, J. A. (2014). Choosing the number of categories in agree–disagree scales. Quality & Quantity, 48 (6), 2603–2627. Salas Espasa, D., & Camacho, M. (2025). From aura to semi-aura: Reframing authenticity in AI-generated art—A systematic literature review. AI & Society . Advance online publication. https://doi.org/10.1007/s00146-025-02361-3 Taber, K. S. (2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 48 (6), 1273–1296. World Medical Association. (2013). World Medical Association Declaration of Helsinki: Ethical principles for medical research involving human subjects. JAMA, 310 (20), 2191–2194. Additional Declarations No competing interests reported. Supplementary Files AppendixA.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 16 May, 2026 Reviewers agreed at journal 10 Apr, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviewers invited by journal 09 Apr, 2026 Editor assigned by journal 31 Mar, 2026 Submission checks completed at journal 12 Mar, 2026 First submitted to journal 11 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8994993","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":622887040,"identity":"ecc7a05b-2fbd-4caa-9ecf-2892737bef90","order_by":0,"name":"David Salas Espasa","email":"data:image/png;base64,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","orcid":"","institution":"Rovira i Virgili University","correspondingAuthor":true,"prefix":"","firstName":"David","middleName":"Salas","lastName":"Espasa","suffix":""},{"id":622887042,"identity":"2b8dc88d-cfde-4f6c-aa54-031a9c327b62","order_by":1,"name":"Maria del Mar Camacho Martí","email":"","orcid":"","institution":"Rovira i Virgili University","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"del Mar Camacho","lastName":"Martí","suffix":""}],"badges":[],"createdAt":"2026-02-28 11:40:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8994993/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8994993/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107482455,"identity":"9fd204bb-cede-4ba1-80ee-817895730b0b","added_by":"auto","created_at":"2026-04-22 02:23:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":143585,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of perceived authenticity ratings across artwork conditions (human-generated, hybrid, and fully AI-generated). The Friedman test revealed significant differences among conditions, χ²(2) = 246.43, p \u0026lt; .001, Kendall’s W = .73 (N = 168).\u003c/p\u003e","description":"","filename":"Figure1BoxplotConditions.png","url":"https://assets-eu.researchsquare.com/files/rs-8994993/v1/212a790392473841e1ee0c4a.png"},{"id":107254429,"identity":"f6a9b4ec-fe37-418b-b900-b9ab48e9605c","added_by":"auto","created_at":"2026-04-19 12:01:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":172188,"visible":true,"origin":"","legend":"\u003cp\u003eAI authenticity ratings according to artistic experience (formal artistic training vs. no formal experience). A Mann–Whitney U test indicated no statistically significant differences between groups (U = 2983, p = .286).\u003c/p\u003e","description":"","filename":"Figure2AIAuthenticitybyExperience.png","url":"https://assets-eu.researchsquare.com/files/rs-8994993/v1/a529ea6aeda3eb73610321d7.png"},{"id":107485004,"identity":"f0107c80-d2af-4357-a589-12af1413c1f5","added_by":"auto","created_at":"2026-04-22 02:33:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":139626,"visible":true,"origin":"","legend":"\u003cp\u003eAI authenticity ratings by age cohort (18–29, 30–40, 41+). A Kruskal–Wallis test indicated statistically significant differences across groups, H(2) = 11.41, p = .003, ε² = .06.\u003c/p\u003e","description":"","filename":"Figure3BoxplotAge.png","url":"https://assets-eu.researchsquare.com/files/rs-8994993/v1/ea8d37a8b83d7f3d9c54ae91.png"},{"id":107483084,"identity":"b88475bf-04a9-4537-9498-bc00fe4db1c6","added_by":"auto","created_at":"2026-04-22 02:26:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":94608,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between semi-aura perception and belief in the future importance of AI-generated art. A moderate positive Spearman correlation was observed (ρ = .46, p \u0026lt; .001; N = 172).\u003c/p\u003e","description":"","filename":"Figure4SemiAuraFuture300dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-8994993/v1/8611acba247900562fb508ad.png"},{"id":107254431,"identity":"2b590c6b-356e-4170-96e7-d464f3f20a86","added_by":"auto","created_at":"2026-04-19 12:01:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":106941,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between semi-aura perception and perceived need for professional adaptation to AI tools. A moderate positive Spearman correlation was observed (ρ = .39, p \u0026lt; .001; N = 172).\u003c/p\u003e","description":"","filename":"Figure5SemiAuraAdaptation300dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-8994993/v1/9a2cba8aff36866ee73acba7.png"},{"id":107484614,"identity":"7fffec73-aac3-40b2-a8f2-f71b6a6315be","added_by":"auto","created_at":"2026-04-22 02:32:31","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":221272,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between labeling sensitivity and perceived authenticity of AI-generated art. A moderate negative correlation was observed (Spearman’s ρ = −.45, p \u0026lt; .001; N = 172), indicating that higher sensitivity to origin disclosure is associated with lower authenticity ratings.\u003c/p\u003e","description":"","filename":"Figure6LabelingvsAIAuthenticity.png","url":"https://assets-eu.researchsquare.com/files/rs-8994993/v1/b86afca4276e77bf0dbee19b.png"},{"id":107487196,"identity":"d90c01cc-a350-4c36-8981-d92f10470cd2","added_by":"auto","created_at":"2026-04-22 02:40:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1551519,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8994993/v1/a349646a-5198-4811-9251-1c5037156d66.pdf"},{"id":107254427,"identity":"64d7b626-cb21-485a-8f7a-1a514cab88b9","added_by":"auto","created_at":"2026-04-19 12:01:55","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":18247,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixA.docx","url":"https://assets-eu.researchsquare.com/files/rs-8994993/v1/ca78b4e2bb40e0b47b874906.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Semi-Aura and the Perception of Authenticity in AI-Generated Art: An Empirical Study of Hybrid Authorship","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe rapid diffusion of generative AI systems has not only expanded the scale of cultural production but also reshaped the epistemological and evaluative frameworks through which creativity, authorship, and authenticity are interpreted. Contemporary debates on algorithmic aesthetics emphasize that AI-mediated creation reconfigures long-standing assumptions about artistic intentionality and originality (Manovich, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Boden, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). At the same time, critical scholarship has highlighted that AI systems function as sociotechnical infrastructures embedded in broader cultural and political dynamics rather than neutral creative tools (Crawford, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). From the perspective of digital mediation theory, these transformations affect how cultural meaning and legitimacy are socially constructed within increasingly hybrid human\u0026ndash;machine environments (Couldry \u0026amp; Hepp, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Within this shifting landscape, questions of authenticity and value attribution acquire renewed urgency.\u003c/p\u003e \u003cp\u003eThe emergence of diffusion models such as Midjourney or DALL-E has shifted the debate on automation from the sphere of industrial productivity to the core of human creativity, intensifying tensions surrounding authenticity, authorship, and aesthetic value in contemporary art. What Benjamin (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1936\u003c/span\u003e) diagnosed as the loss of aura in the age of mechanical reproducibility now reaches a new dimension: unlike the technologies originally analyzed, current generative models do not merely multiply copies of an original, but produce formally new images through algorithmic processes that pose an ontological challenge: can a machine generate \u0026ldquo;singular\u0026rdquo; works without a direct human author?\u003c/p\u003e \u003cp\u003eThis transformation reopens the question of aura in a context where singularity no longer depends exclusively on materiality or irreproducible physical presence. Benjamin\u0026rsquo;s theory situated aura in distance, uniqueness, and historical inscription. In the contemporary digital environment, however, the relationship between originality and reproduction is altered by the statistical synthesis capacity of AI systems. From a critical perspective, this shift resonates with Baudrillard\u0026rsquo;s (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1981\u003c/span\u003e) notion of the simulacrum, in which images progressively detach from stable referents. However, contemporary AI-mediated production cannot be reduced to a simple opposition between human authenticity and algorithmic simulation, as hybrid creative configurations introduce more nuanced forms of authorship and intentionality.\u003c/p\u003e \u003cp\u003eEmpirical research in the psychology of art has consistently shown that knowledge of the production process significantly influences aesthetic evaluation (Newman \u0026amp; Bloom, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). More recent studies comparing human and AI-generated artworks further indicate that authorship attribution and perceived intentionality substantially shape evaluative judgments (Chamberlain \u0026amp; Pepperell, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The perception of effort, agency, and creative intention emerges as a central variable in authenticity attribution. Meanwhile, the sociology of culture has emphasized that artistic legitimacy is not merely a formal property but a socially constructed value embedded within structures of symbolic capital (Bourdieu, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1979\u003c/span\u003e). Recent work has also identified an anthropocentric bias in the evaluation of AI-generated art, particularly when human authorship is perceived as absent (Millet et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this transitional scenario, this article proposes that authenticity should no longer be understood as a binary value, but as a graded perceptual spectrum, which we term \u0026ldquo;semi-aura.\u0026rdquo; Rather than replacing the Benjaminian notion, semi-aura seeks to empirically examine whether the presence of a human trace within generative processes\u0026mdash;especially in hybrid or co-creative configurations\u0026mdash;is perceptually recognized as a qualitative distinction that rescues the artwork from algorithmic inauthenticity. The study aims to validate this construct empirically and to analyze how age, artistic experience, and digital literacy modulate authenticity perception in a sample of 172 participants, with particular attention to professionals and students in the artistic field. In doing so, the study contributes to digital humanities research by empirically investigating how algorithmic mediation reshapes authenticity frameworks in contemporary cultural production.\u003c/p\u003e \u003cp\u003eThis debate, while ontological in scope, is addressed here from a strictly perceptual and empirical perspective. The objective is not to redefine the metaphysical status of aura, but to examine how audiences attribute differentiated degrees of authenticity according to the perceived level of human intervention.\u003c/p\u003e"},{"header":"Theoretical Framework","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Aura and Reproducibility: From the Benjaminian Paradigm to Algorithmic Generation\u003c/h2\u003e \u003cp\u003eThe notion of aura formulated by Walter Benjamin (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1936\u003c/span\u003e) constitutes one of the central axes of the debate on authenticity in modern art. For Benjamin, aura refers to the irreproducible presence of the artwork in time and space, linked to its material singularity and historical embeddedness. In the contemporary context, algorithmic generation no longer reproduces a pre-existing original, but produces new images based on statistically trained models. This capacity for synthesis raises the question of whether singularity may derive not from materiality, but from the generative process itself (Park, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). From a critical perspective, if autonomous AI generates what Baudrillard (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1981\u003c/span\u003e) calls a \u0026ldquo;pure simulacrum,\u0026rdquo; hybridization could disrupt this logic by reintroducing authorial will.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Trace, Intentionality, and Perception\u003c/h2\u003e \u003cp\u003eThe notion of \u0026ldquo;trace\u0026rdquo; formulated by Derrida (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1967\u003c/span\u003e) allows the issue to be addressed from a fundamental post-structuralist perspective: the trace is not merely a material mark, but a vestige that refers to a deferred presence. In algorithmic co-creation, audiences no longer seek only a physical mark, but the \u0026ldquo;intellectual trace\u0026rdquo; of the artist who has guided the algorithm through prompting, iterative selection, and curatorial decision-making. As Salas Espasa and Camacho (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) argue, semi-aura emerges precisely in this dialogue where knowledge of the process influences belief in an underlying human intentionality, thereby modulating emotional and evaluative responses (Newman \u0026amp; Bloom, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Pelowski et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Symbolic Capital and Legitimacy in the Artistic Field\u003c/h2\u003e \u003cp\u003eFrom the sociology of culture, Bourdieu (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1979\u003c/span\u003e) argues that artistic value is embedded in structures of symbolic capital and mechanisms of professional distinction. Legitimacy depends on position within the field and institutional recognition. The emergence of generative systems may activate tensions related to traditional mechanisms of distinction, with anthropocentric bias interpreted as a symbolic protection mechanism of the field in response to technologies that lower entry barriers (Millet et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Recognized human intervention in hybrid practices shifts the issue toward a terrain in which legitimacy depends on the perceived degree of human participation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Systematic Review on Authenticity and AI\u003c/h2\u003e \u003cp\u003eA recent systematic review on authenticity and AI-generated art (Salas Espasa \u0026amp; Camacho, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) notes that most studies tend to adopt conceptual approaches or binary comparisons between human and algorithmic production. According to this review, hybrid forms of co-creation have received limited empirical attention despite their increasing prevalence. This gap justifies the exploration of an intermediate construct capable of capturing perceptual variations according to the perceived level of human intervention, rather than asking whether art \u0026ldquo;has aura\u0026rdquo; in an absolute sense.\u003c/p\u003e \u003cp\u003eIn this article, semi-aura is understood exclusively as an operative perceptual category. It is not proposed as an ontological reformulation of the Benjaminian concept, but as an analytical tool to describe variations in authenticity attribution within generative environments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Posthumanism and Distributed Creativity\u003c/h2\u003e \u003cp\u003ePosthumanist approaches (Hayles, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Braidotti, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) propose a relational conception of subjectivity in which the boundary between human and technology becomes porous. From this perspective, creativity is understood as a distributed process between human agents and technical systems, in which the machine does not replace the human but amplifies expressive capacity. Semi-aura functions here as a descriptive category to analyze whether participants perceptually differentiate between autonomous algorithmic production and guided production, capturing recognition of this shared authorship.\u003c/p\u003e \u003c/div\u003e"},{"header":"Objectives","content":"\u003cp\u003e\u003cstrong\u003e3.1. Validation of the \u0026ldquo;semi-aura\u0026rdquo; construct:\u003c/strong\u003e To determine whether \u0026ldquo;semi-aura\u0026rdquo; constitutes a perceptual dimension with sufficient internal consistency to be considered a valid analytical category within contemporary art theory.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. Comparison of authenticity and aura levels according to the origin of the work:\u003c/strong\u003e To establish a quantitative comparison between works created exclusively by humans, works generated autonomously by AI, and hybrid works (co-creation), in order to identify whether human intervention in prompting and curation significantly alters the reception of the artwork.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3. Analysis of the \u0026ldquo;symbolic capital\u0026rdquo; bias in the professional sector:\u003c/strong\u003e To investigate how prior artistic experience and academic training in fine arts and design condition the evaluation of algorithmic art, exploring the possible existence of a defensive or protectionist stance toward the automation of creative processes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4. Analysis of generational differences in perception:\u003c/strong\u003e To examine the contrast between so-called \u0026ldquo;digital natives\u0026rdquo; and senior generations, testing the widespread belief that greater technological familiarity necessarily entails greater acceptance of artificial authenticity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5. Identification of variables associated with AI acceptance in the future of art:\u003c/strong\u003e To determine which variables (theoretical knowledge, frequency of use, or perception of semi-aura) act as key indicators in predicting the integration and acceptance of these tools in the artistic and professional market in the coming years.\u003c/p\u003e"},{"header":"Hypotheses","content":"\u003cp\u003eThe following working hypotheses are established:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH1: The influence of theoretical knowledge.\u003c/strong\u003e It is postulated that greater self-reported theoretical knowledge about the functioning of Artificial Intelligence will positively correlate with the ability to perceive \u0026ldquo;semi-aura.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH2: The protective bias of artistic experience.\u003c/strong\u003e It is hypothesized that participants with a consolidated professional or academic background in fine arts and design will assign significantly lower authenticity ratings to AI-generated art compared to novices.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH3: The superiority of hybrid works.\u003c/strong\u003e It is predicted that works perceived as \u0026ldquo;hybrid\u0026rdquo; (human\u0026ndash;machine co-creation) will obtain significantly higher ratings of artistic value and aura than works generated entirely autonomously by AI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH4: The age paradigm (Digital Natives).\u003c/strong\u003e It is hypothesized that participants in the youngest cohort (18\u0026ndash;29 years), having grown up in a ubiquitous digital environment, will demonstrate greater openness and assign higher levels of authenticity to AI-generated art than senior generations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH5: Semi-aura as the variable most strongly associated with future acceptance.\u0026nbsp;\u003c/strong\u003eIt is postulated that the perception of semi-aura (rather than technical knowledge or age) will show the strongest associative relationship with the acceptance of AI as a legitimate creative tool and with the perceived need for professional adaptation in the future.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003e\u003cstrong\u003e4.1 Research Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study adopts a cross-sectional descriptive-correlational design with a quantitative approach. The objective is not to establish causal relationships, but to identify associative patterns in the perception of authenticity attributed to different types of artwork in a context of algorithmic generation.\u003c/p\u003e\n\u003cp\u003eThis design is appropriate for exploratory studies on aesthetic perception, particularly when working with complex constructs such as authenticity or perceived human trace, which require comparative measurement across defined conditions (Creswell \u0026amp; Creswell, 2018).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Procedure and Data Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData collection was conducted between February 5 and February 12, 2026, using the Google Forms platform. The questionnaire was distributed through academic networks, educational environments partially linked to the artistic field, and personal contacts, using convenience sampling (Bryman, 2016).\u003c/p\u003e\n\u003cp\u003eThree equivalent linguistic versions (Catalan, Spanish, and English) were developed, maintaining conceptually aligned structure, order, and wording. The use of multiple languages aimed to reduce potential semantic comprehension biases in theoretical terms such as \u0026ldquo;aura,\u0026rdquo; \u0026ldquo;authenticity,\u0026rdquo; or \u0026ldquo;human intervention.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003eThe three resulting datasets were consolidated into a single analytical matrix, standardizing nomenclature and categories prior to statistical analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe initial dataset included 182 responses. Before analysis, predefined exclusion criteria were applied:\u003c/p\u003e\n\u003cp\u003eRemoval of participants under 18 years of age (n = 9).\u003c/p\u003e\n\u003cp\u003eRemoval of one record with a manifestly erroneous age (397 years).\u003c/p\u003e\n\u003cp\u003eThe final analyzed sample consisted of N = 172 participants, aged between 18 and 78 years (M = 35.61; SD = 13.77).\u003cbr\u003e\u0026nbsp;A detailed overview of sociodemographic and professional characteristics is presented in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e\u003cbr\u003e\u003cem\u003eSociodemographic and professional characteristics of the sample\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\" width=\"359\" class=\"fr-table-selection-hover\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAge cohorts\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e18\u0026ndash;29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e41.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e30\u0026ndash;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e41+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eArtistic experience\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eProfessional / Student\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAmateur\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo formal experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e34.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eDescriptive statistics for age:\u003cbr\u003e\u003cem\u003eM\u003c/em\u003e = 35.61, \u003cem\u003eSD\u003c/em\u003e = 13.77, range = 18\u0026ndash;78.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRegarding artistic experience:\u003c/p\u003e\n\u003cp\u003e58.1% professionals or students in fine arts, design, or related fields.\u003c/p\u003e\n\u003cp\u003e7.6% amateurs.\u003c/p\u003e\n\u003cp\u003e34.3% without formal artistic experience.\u003c/p\u003e\n\u003cp\u003eThe 18\u0026ndash;29 age cohort represents 41.3% of the sample, reflecting an overrepresentation of younger participants relative to the general demographic distribution.\u003c/p\u003e\n\u003cp\u003eFull distribution of sociodemographic and professional characteristics.\u003c/p\u003e\n\u003cp\u003eIt is important to note that, given the non-probabilistic nature of the sampling, the results should be interpreted as internal patterns within the analyzed sample composition rather than as representative estimates of the general population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4 Instrument\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe questionnaire was structured into four sections:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eSociodemographic variables (age, gender, level of education, country).\u003c/li\u003e\n \u003cli\u003eArtistic experience and educational background.\u003c/li\u003e\n \u003cli\u003eDegree of use and knowledge of generative AI tools.\u003c/li\u003e\n \u003cli\u003eAuthenticity and semi-aura perception scale.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eRatings were collected using 0\u0026ndash;10 scales. The use of 11-point scales is justified by their greater discriminative capacity compared to narrower scales (Revilla et al., 2014) and is consistent with common practices in social science research (Allen \u0026amp; Seaman, 2007).\u003c/p\u003e\n\u003cp\u003eLabeling sensitivity was operationalized as a single-item measure (Item 5.3 in Appendix A), assessing the extent to which participants reported that knowledge of AI authorship would alter their perception of authenticity. This variable was treated as an indicator of origin-awareness effects in authenticity attribution.\u003c/p\u003e\n\u003cp\u003eThe internal consistency of the semi-aura block was assessed using Cronbach\u0026rsquo;s alpha coefficient. The semi-aura construct was operationalized using four items (6.3\u0026ndash;6.6), which specifically capture the perceived retention, transformation, or procedural mediation of aura in AI and hybrid artworks. Items 6.1 and 6.2 were excluded from this composite measure, as they assess baseline perceptions of human and AI aura independently rather than the graded or hybrid dimension conceptualized as semi-aura.\u003c/p\u003e\n\u003cp\u003eGiven the exploratory nature of the study and the available sample size, confirmatory factor analysis was not conducted. At this stage, construct validation was limited to internal consistency assessment. Given the limited number of theoretically aligned items (four), exploratory factor analysis was not deemed necessary. The obtained value (\u0026alpha; = .84) exceeds the recommended threshold of 0.70 for social science research (Nunnally \u0026amp; Bernstein, 1994) and is interpreted as an indicator of high reliability (Taber, 2018).\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u0026nbsp;Item-level statistics and corrected item\u0026ndash;total correlations are reported in Table 2.\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDescriptive statistics and reliability indices for the semi-aura scale (N = 172)\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\" width=\"616\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eItem (abridged)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCorrected Item\u0026ndash;Total r\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAI works retain a certain degree of aura\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHuman prompt intention increases authenticity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHybrid works may have distinct aura/value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAI transforms rather than eliminates aura\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCronbach\u0026rsquo;s \u0026alpha; = .84\u003c/p\u003e\n\u003cp\u003eThe full questionnaire is provided in Appendix A.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.5 Statistical Analysis Strategy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrior to inferential analysis, normality of distributions was examined using the Shapiro\u0026ndash;Wilk test. Results indicated significant deviations from normality (p \u0026lt; 0.05), which motivated the use of non-parametric tests, following methodological recommendations by Field (2017) and Hair et al. (2019).\u003c/p\u003e\n\u003cp\u003eThe following analyses were conducted:\u003c/p\u003e\n\u003cp\u003eFor within-subject comparisons between the three artwork conditions, a Friedman test was conducted.\u003c/p\u003e\n\u003cp\u003eSpearman correlations (\u0026rho;) were used to examine associative relationships.\u003c/p\u003e\n\u003cp\u003eAnalyses were performed using Python 3.x, employing the Pandas library for data management (McKinney, 2010) and SciPy for inferential statistics.\u003c/p\u003e\n\u003cp\u003eGiven the cross-sectional design, all identified relationships are interpreted in associative rather than causal terms.\u003c/p\u003e\n\u003cp\u003ePost hoc pairwise comparisons following the Friedman test were performed using Dunn\u0026rsquo;s test with Bonferroni correction.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e5.1 General Evaluation Pattern According to Artwork Type\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe descriptive analysis shows clear differences in perceived authenticity according to artwork origin (see Table 3 and Figure 1).\u003c/p\u003e\n\u003cp\u003eWorks created exclusively by a human artist obtain the highest mean score (M = 9.14), with a relatively low standard deviation, indicating a high level of consensus among participants. In contrast, works generated autonomously by an AI system receive the lowest mean score (M = 2.67), with moderate dispersion and concentration in the lower values of the scale.\u003c/p\u003e\n\u003cp\u003eHybrid works\u0026mdash;defined as productions generated with explicit human intervention in the algorithmic process\u0026mdash;occupy an intermediate position (M = 6.66), with greater dispersion than human-produced art, suggesting higher heterogeneity in their perception.\u003c/p\u003e\n\u003cp\u003eA Friedman test revealed statistically significant differences in perceived authenticity across the three conditions, \u0026chi;\u0026sup2;(2) = 246.43, p \u0026lt; .001, Kendall\u0026rsquo;s W = .73, indicating a large effect size according to conventional benchmarks for non-parametric repeated-measures designs. The Friedman test was conducted on complete cases (N = 168), as four participants had missing responses in at least one of the three artwork conditions. The overall valid sample consisted of N = 172 participants after applying predefined exclusion criteria. Post hoc Dunn\u0026ndash;Bonferroni comparisons confirmed that all pairwise differences were statistically significant (see Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 3\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison of perceived authenticity across artwork conditions\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCondition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHuman-generated artwork\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHybrid (human\u0026ndash;AI co-creation)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFully AI-generated artwork\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eFriedman Test Results\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;\u0026chi;\u0026sup2;(2) = 246.43, p \u0026lt; .001\u003cbr\u003e\u0026nbsp;Kendall\u0026rsquo;s W = .73\u003cbr\u003e\u0026nbsp;N = 168\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e The Friedman test was conducted on participants with complete responses across the three conditions (N = 168), within a final sample of 172 valid participants. Post hoc pairwise comparisons using Dunn\u0026rsquo;s test with Bonferroni correction indicated that all condition contrasts were statistically significant (human vs. hybrid: p \u0026lt; .001; human vs. AI: p \u0026lt; .001; hybrid vs. AI: p \u0026lt; .001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.2 Differences According to Artistic Experience\u003cbr\u003e\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Participants were initially classified into three categories of artistic experience: professionals/students in the arts, amateurs, and individuals without formal artistic experience. However, due to the very small number of valid responses within the amateur subgroup for the AI-authenticity variable, statistical comparisons were conducted between participants with formal artistic experience (professionals/students) and those without formal experience. This decision ensured sufficient statistical power and robustness of non-parametric group comparisons.\u003c/p\u003e\n\u003cp\u003eA Mann\u0026ndash;Whitney U test indicated no statistically significant differences in perceived authenticity of AI-generated art between participants with formal artistic experience (professionals/students; n = 114) and those without formal experience (n = 58), U = 2983, p = .286 (see Figure 2). The distributions showed substantial overlap between the two groups.\u003c/p\u003e\n\u003cp\u003eRegarding hybrid works, all groups show a notable increase in valuation compared to autonomous AI production, with less pronounced internal differences than in the case of exclusively algorithmic production.\u003c/p\u003e\n\u003cp\u003eAdditionally, no statistically significant differences were observed in perceived authenticity as a function of participants\u0026rsquo; gender or geographic origin (p \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.3 Differences According to Age Cohort\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA Kruskal\u0026ndash;Wallis test revealed statistically significant differences in perceived authenticity of AI-generated art across age cohorts, H(2) = 11.41, p = .003, \u0026epsilon;\u0026sup2; = .06. Participants aged 18\u0026ndash;29 reported the lowest mean scores (M = 1.97), whereas those aged 41 and above assigned comparatively higher authenticity ratings (M = 3.34) (see Figure 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe intermediate cohort (30\u0026ndash;40 years) fell between these groups. The distributions suggest notable within-group variability, particularly among older participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.4 Associative Relationships\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpearman correlation analyses revealed a moderate positive association between semi-aura perception and the belief that AI-generated art will become more important than human art in the future, \u0026rho; = .46, p \u0026lt; .001 (N = 172) (see Figure 4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA secondary correlation indicated that higher semi-aura scores were also associated with stronger agreement that artists will need to adapt to AI tools to remain relevant, \u0026rho; = .39, p \u0026lt; .001 (see Figure 5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditionally, a moderate negative association was found between labeling sensitivity and perceived authenticity of AI-generated art, \u0026rho; = \u0026minus;.45, p \u0026lt; .001 (see Figure 6).\u003c/p\u003e\n\u003cp\u003eThese findings support the construct validity of semi-aura as a perceptual mediator closely associated with future-oriented attitudes toward AI in artistic contexts.\u003c/p\u003e\n\u003cp\u003eContrary to H1, no statistically significant association was found between self-reported theoretical knowledge of AI and semi-aura perception (Spearman\u0026rsquo;s \u0026rho; = \u0026minus;.085, p = .268; N = 172). This suggests that perceptual recognition of human trace in hybrid production is not directly linked to technical familiarity with AI systems.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.5 Scale Reliability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe semi-aura scale demonstrates high internal consistency (\u0026alpha; = .84), substantially exceeding the recommended threshold for social science research. Item\u0026ndash;total correlations remain within adequate ranges, with no indication of dysfunctional items (see Table 2).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBeyond the statistical description presented in the previous section, the results allow for a theoretical reading of how perceptions of authenticity are reconfigured in environments of algorithmic generation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.1 Semi-Aura as a Transcendence of the Simulacrum\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOne of the central contributions of this study is the empirical articulation of \u0026ldquo;semi-aura\u0026rdquo; as a perceptual construct. The data indicate that exclusively AI-generated works receive comparatively low authenticity ratings (M = 2.67), in contrast with human-produced art (M = 9.14). This marked evaluative distance, confirmed through non-parametric testing (see Table 3), suggests that algorithmic production is not yet perceived as equivalent to human authorship in terms of authenticity attribution.\u003c/p\u003e\n\u003cp\u003eRather than interpreting this pattern as a definitive ontological rupture, the findings invite a reconsideration of classical aesthetic frameworks. In Benjaminian terms (1936), aura does not appear to function as a binary category of presence or absence, but as a graded perceptual spectrum. Hybrid works, positioned at an intermediate level of authenticity (M = 6.66), indicate that audiences are capable of recognizing procedural human mediation within algorithmic systems.\u003c/p\u003e\n\u003cp\u003eThis intermediate position may be interpreted, in Derridean terms (1967), as the persistence of a \u0026ldquo;trace\u0026rdquo;: a residual sign of human intentionality that anchors the work to a subject, even when the material execution is algorithmically mediated. The audience does not appear to require exclusive manual authorship, but rather perceptible evidence of decision, direction, or conceptual agency. In this sense, AI-generated art may be read not strictly as a simulacrum in the Baudrillardian sense (1981), but as a production whose legitimacy remains contingent upon the visibility of such trace. This suggests that aura, rather than dissolving under algorithmic reproducibility, undergoes a perceptual displacement toward procedural mediation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOne of the most theoretically suggestive findings of this study concerns the non-confirmation of Hypothesis 4 (H4). While significant generational differences were observed in perceived authenticity of AI-generated art, these differences were small in magnitude (\u0026epsilon;\u0026sup2; = .06) and do not support a deterministic association between technological familiarity and greater acceptance of algorithmic authorship. The youngest cohort (18\u0026ndash;29 years) reported the lowest authenticity scores (M = 1.97), challenging the conventional assumption that digital nativity necessarily entails aesthetic endorsement of artificial production. The delimitation of this age range follows the conceptualization of \u0026ldquo;digital natives\u0026rdquo; (Prensky, 2001). The 18\u0026ndash;29 cohort is commonly used in contemporary survey research to capture early adult populations socialized in digital environments.\u003c/p\u003e\n\u003cp\u003eImportantly, this generational skepticism cannot be reduced to professional self-interest. The analysis of artistic experience revealed no statistically significant differences in authenticity attributions across levels of formal artistic training. This suggests that resistance toward AI-generated authorship is not confined to professionally invested actors but reflects a broader cultural negotiation of authorship, agency, and human intentionality in the age of generative systems.\u003c/p\u003e\n\u003cp\u003eA central explanatory factor for this skepticism appears to be the weight attributed to the genesis of the artwork, commonly described as labeling bias. As Newman and Bloom (2012) demonstrate, aesthetic evaluation is deeply intertwined with perceived origin history. In the present study, a moderate negative correlation was found between sensitivity to origin labeling and authenticity ratings of AI-generated art (\u0026rho; = \u0026minus;.45, p \u0026lt; .001). In other words, participants who reported that knowing an artwork\u0026rsquo;s origin would significantly alter their judgment were precisely those who assigned lower authenticity scores to algorithmic production.\u003c/p\u003e\n\u003cp\u003eThis pattern aligns with experimental findings showing that when origin information is concealed, AI-generated works may be evaluated more favorably, whereas explicit disclosure of algorithmic authorship significantly reduces perceived authenticity (Bellaiche et al., 2023). Together, these results suggest that skepticism is not merely a reaction to visual output, but to the ontological framing of non-human production itself; the label \u0026ldquo;AI\u0026rdquo; operates as a heuristic trigger that modulates perceived aura.\u003c/p\u003e\n\u003cp\u003eWhile this resistance is present across the sample, it manifests more clearly within the youngest cohort. Rather than interpreting this pattern exclusively as a defensive strategy of symbolic capital protection in the Bourdieusian sense (Bourdieu, 1979), the absence of significant professional differentiation points toward a more generalized anthropocentric orientation. As Millet et al. (2023) propose, contemporary responses to automation often involve a defensive reaffirmation of human creative exclusivity. In this context, skepticism toward AI-authenticity may reflect not merely institutional self-preservation but a generational rearticulation of human exceptionalism under conditions of algorithmic ubiquity.\u003c/p\u003e\n\u003cp\u003eThis interpretation remains provisional, given the non-probabilistic nature of the sample. Further research employing representative designs will be necessary to determine whether the observed generational pattern persists at the population level and to clarify the relative contribution of symbolic capital, anthropocentric bias, and perceptual semi-aura orientation in shaping authenticity judgments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.3 Auratic Literacy and the Future of the Profession\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe empirical findings do not indicate that mere frequency of AI-tool usage is sufficient to account for semi-aura perception. Rather than technological familiarity per se, the data suggest that perceptual orientation toward human trace is the more decisive factor. Semi-aura, operationalized as a perceptual construct (\u0026alpha; = .84), appears to capture a cognitive disposition toward recognizing intentional mediation within algorithmic production.\u003c/p\u003e\n\u003cp\u003eConceptually, this pattern indicates that the perception of \u0026ldquo;human trace\u0026rdquo; in hybrid environments shifts from manual gesture toward decision-making, selection, and conceptual refinement. Semi-aura does not imply a restoration of traditional material aura, but rather a recognition of distributed yet human-directed agency within generative systems.\u003c/p\u003e\n\u003cp\u003eThe data further indicate that semi-aura shows strong associative alignment with both future-oriented technological acceptance (\u0026rho; = .46, p \u0026lt; .001) and perceived necessity of professional adaptation (\u0026rho; = .39, p \u0026lt; .001). Rather than functioning as a statistical predictor in a causal sense, semi-aura emerges here as the variable most consistently associated with future-oriented legitimacy attitudes within the present sample. At the same time, a moderate negative association between labeling sensitivity and authenticity attribution (\u0026rho; = \u0026minus;.45, p \u0026lt; .001) suggests that origin-awareness mechanisms significantly modulate evaluative judgments.\u003c/p\u003e\n\u003cp\u003eGiven the cross-sectional and non-probabilistic nature of the sample, these relationships must be interpreted as associative rather than causal. No multivariate modeling was conducted; therefore, potential confounding interactions between age, professional experience, and perceptual orientation remain open to further investigation.\u003c/p\u003e\n\u003cp\u003eRather than asserting a definitive transformation of aura in the Benjaminian sense, the results point toward a perceptual reconfiguration of artistic value in hybrid contexts. Professional legitimacy may increasingly depend not on manual execution alone, but on curatorial agency, conceptual direction, and procedural authorship. In this light, the artist does not disappear but is repositioned within a shared creative ecology, consistent with the reframing proposed by Salas Espasa and Camacho (2025).\u003c/p\u003e"},{"header":"Limitations and Future Research","content":"\u003cp\u003eDespite the statistical significance of the findings and the high reliability of the analyzed constructs, this study is subject to several limitations that must be carefully considered when interpreting the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7.1 Sampling Bias and Professional Representativeness\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough the sample includes a high proportion of participants with formal artistic training, no statistically significant differences were observed across levels of artistic experience. Therefore, while the composition of the sample may limit generalizability, the data do not support the interpretation of a systematic professional protection bias within this data set.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7.2 The Digital Divide and Technological Exclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIt is necessary to emphasize a digital divide bias inherent in the research format. The exclusively online nature of the data collection instrument systematically excluded profiles with low digital literacy or without regular access to virtual environments. This limitation is particularly acute in older cohorts (such as the senior population over 75 years of age), who, despite possessing historically and culturally valuable perspectives on art, remain outside the debate due to technical barriers. This exclusion suggests that the results regarding senior openness may be biased toward those who are already technologically integrated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7.3 Geographic Scope and Cultural Bias\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough the sample included international participants, the majority of responses were collected within Catalonia and Spain. Public perception of AI is deeply influenced by local cultural narratives and by Western conceptions of authorship. As Han (2017) suggests in his critique of the notion of \u0026ldquo;copy,\u0026rdquo; in East Asia the concept of originality differs significantly from the European canon, with the perfect replica often valued as a form of respect and technical mastery. Therefore, the \u0026ldquo;semi-aura\u0026rdquo; construct defined here could be perceived very differently in other cultural contexts where the human \u0026ldquo;trace\u0026rdquo; is not as closely tied to the individual identity of the author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7.4 Subjectivity of Terminology and Temporal Scope\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe concepts of \u0026ldquo;aura\u0026rdquo; and \u0026ldquo;authenticity\u0026rdquo; are inherently abstract. Although the high Cronbach\u0026rsquo;s alpha (\u0026alpha; = .84) indicates that participants shared a coherent internal logic, individual interpretations may vary according to cultural background. Moreover, this research represents a cross-sectional snapshot from February 2026; given that technology evolves at an unprecedented pace, the perception of \u0026ldquo;disruptive novelty\u0026rdquo; may normalize within a few years, potentially modifying the acceptance thresholds presented here.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7.5 Future Research Directions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the identified constraints, the following avenues for future research are proposed:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQualitative validation with experts:\u003c/strong\u003e It would be of particular interest to complement these quantitative data with a study based on in-depth interviews with experts (curators, art critics, and technologists). This phase would allow the validity of \u0026ldquo;semi-aura\u0026rdquo; to be examined from a professional perspective and to analyze how this construct might influence future valuation and certification protocols for hybrid artworks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExperimental studies with visual stimuli:\u003c/strong\u003e Conduct \u0026ldquo;aesthetic Turing tests\u0026rdquo; in which participants evaluate the aura of artworks without prior knowledge of authorship, in order to observe whether semi-aura perception is maintained intuitively when confronted with the artistic object.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCross-cultural research:\u003c/strong\u003e Replicate the study in East Asian countries to validate the universality or cultural specificity of the semi-aura construct.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion of analog methods:\u003c/strong\u003e Design data collection strategies that overcome the digital divide in order to capture perceptions from senior populations without digital literacy.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis research has made it possible to map the complex transformation of auratic experience in AI-generated art in early 2026. Based on the analysis of the collected data and its contrast with the theoretical framework of postmodernity, the following fundamental conclusions are established:\u003c/p\u003e \u003cdiv id=\"Sec35\" class=\"Section2\"\u003e \u003ch2\u003e8.1 The Emergence of Semi-Aura as a Spectrum\u003c/h2\u003e \u003cp\u003eThe study provides empirical support for the articulation of a perceptual construct termed semi-aura. The results indicate that aura no longer operates as a binary value of presence or absence, but as a graded spectrum. With a mean rating of M\u0026thinsp;=\u0026thinsp;6.66, hybrid art surpasses the authenticity void of autonomous AI production (M\u0026thinsp;=\u0026thinsp;2.67), occupying an intermediate space of legitimation. These findings suggest that audiences are capable of valuing the \u0026ldquo;human trace\u0026rdquo; embedded in prompt intentionality and curatorial mediation, rescuing the work from the condition of pure simulacrum.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec36\" class=\"Section2\"\u003e \u003ch2\u003e8.2 Generational Differentiation and the Limits of Symbolic Capital Explanation\u003c/h2\u003e \u003cp\u003eThe findings invite a nuanced reconsideration of symbolic capital theory (Bourdieu, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1979\u003c/span\u003e) in the context of AI-generated art. While one might expect professionally invested actors to exhibit stronger resistance toward algorithmic authorship as a strategy of capital protection, no statistically significant differences were observed across levels of artistic experience in this study. This absence of professional differentiation suggests that authenticity skepticism toward AI cannot be reduced to field-specific defensive mechanisms.\u003c/p\u003e \u003cp\u003eAt the same time, significant generational differences were identified, with participants aged 18\u0026ndash;29 assigning lower authenticity ratings to AI-generated works (M\u0026thinsp;=\u0026thinsp;1.97), compared to older cohorts (M\u0026thinsp;=\u0026thinsp;3.34 for participants aged 41 and above). Although the effect size was small (ε\u0026sup2; = .06), this pattern challenges the assumption that technological familiarity necessarily entails aesthetic acceptance. Rather than reflecting a strictly professional struggle for symbolic capital, the observed generational skepticism may indicate a broader anthropocentric orientation in which human intentionality remains central to authenticity judgments.\u003c/p\u003e \u003cp\u003eIn this light, symbolic capital remains a relevant theoretical lens, but its explanatory scope appears limited in hybrid creative ecologies. The data suggest that resistance to AI-authorship may operate less through institutional field competition and more through culturally embedded evaluative schemas concerning human agency, origin, and authorship. Further research with probabilistic samples will be required to determine whether these dynamics persist at the population level.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec37\" class=\"Section2\"\u003e \u003ch2\u003e8.3 Semi-Aura as a Perceptual Mediator of Technological Legitimacy\u003c/h2\u003e \u003cp\u003eThe empirical validation of semi-aura constitutes one of the central contributions of this study. Operationalized as a four-item perceptual construct (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;.84), semi-aura captures the degree to which individuals attribute residual human intentionality\u0026mdash;or a transformed modality of aura\u0026mdash;to AI-mediated artistic production. Rather than measuring acceptance of technology per se, the construct indexes a perceptual orientation toward recognizing human trace within algorithmic systems.\u003c/p\u003e \u003cp\u003eA moderate positive association was found between semi-aura perception and the belief that AI-generated art will become more important than human art in the future (ρ\u0026thinsp;=\u0026thinsp;.46, p \u0026lt; .001; N\u0026thinsp;=\u0026thinsp;172). A secondary correlation indicated that higher semi-aura scores were also associated with stronger agreement that artists will need to adapt to AI tools in order to remain relevant (ρ\u0026thinsp;=\u0026thinsp;.39, p \u0026lt; .001). These associations suggest that perceived continuity of human agency within generative processes appears to operate as a perceptual bridge in future-oriented legitimacy attribution.\u003c/p\u003e \u003cp\u003eAdditionally, a moderate negative correlation was observed between labeling sensitivity and perceived authenticity of AI-generated art (ρ = \u0026minus;.45, p \u0026lt; .001), indicating that origin-awareness mechanisms substantially shape evaluative judgments. Participants who reported that knowledge of an artwork\u0026rsquo;s origin would alter their perception were precisely those assigning lower authenticity scores to AI-generated works. This reinforces the interpretation that authenticity judgments are structured not solely by visual properties, but by perceived ontological status and authorial trace.\u003c/p\u003e \u003cp\u003eGiven the cross-sectional and non-probabilistic nature of the sample, these relationships must be interpreted as associative rather than causal. No multivariate modeling was conducted; therefore, potential interactions between generational effects, professional experience, and perceptual orientation remain open to future investigation.\u003c/p\u003e \u003cp\u003eRather than implying a definitive metaphysical transformation of aura, the findings point toward a perceptual reconfiguration of artistic legitimacy in hybrid contexts. Legitimacy appears increasingly linked to the perceived continuity of human intentional mediation within algorithmic systems, rather than to manual execution alone. In this sense, semi-aura functions as a transitional perceptual category through which audiences negotiate authenticity under conditions of distributed and technologically mediated creativity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec38\" class=\"Section2\"\u003e \u003ch2\u003e8.4 Persistence of Human-Centered Authenticity\u003c/h2\u003e \u003cp\u003eDespite the emergence of hybrid legitimization patterns, the data indicate that perceived artistic authenticity remains strongly associated with exclusively human production (M\u0026thinsp;=\u0026thinsp;9.14). AI-generated works continue to receive substantially lower authenticity ratings (M\u0026thinsp;=\u0026thinsp;2.67), while hybrid productions occupy an intermediate evaluative space (M\u0026thinsp;=\u0026thinsp;6.66). These findings suggest that, at the time of data collection (early 2026), human authorship remains the dominant reference point in authenticity attribution.\u003c/p\u003e \u003cp\u003eRather than signaling the disappearance of aura, the results point toward its perceptual redistribution. Semi-aura appears to function as a mediating category that allows participants to negotiate authenticity within hybrid creative systems without fully abandoning the human-centered evaluative framework. The artist, therefore, is not displaced, but neither is their position empirically guaranteed; legitimacy in generative contexts appears contingent upon perceived intentional mediation rather than ontological exclusivity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec39\" class=\"Section2\"\u003e \u003ch2\u003e8.5 Practical Implications\u003c/h2\u003e \u003cp\u003eThe findings carry potential implications for institutional and professional contexts in which AI-mediated artistic production is increasingly present.\u003c/p\u003e \u003cp\u003eFor museums and curatorial institutions, the results suggest that audience evaluations of authenticity are influenced not solely by visual output but by perceived transparency of human intentional mediation. Exhibition strategies that foreground process documentation, prompt intentionality, and curatorial framing may therefore play a central role in shaping legitimacy perceptions of hybrid works.\u003c/p\u003e \u003cp\u003eIn art education and design schools, the identified generational skepticism indicates that technological familiarity does not automatically translate into aesthetic acceptance. Pedagogical approaches that emphasize critical engagement with algorithmic systems\u0026mdash;rather than merely technical proficiency\u0026mdash;may help students articulate their position within hybrid creative ecologies without framing AI as a simple threat to authorship.\u003c/p\u003e \u003cp\u003eFor the art market and authorship certification systems, the emergence of semi-aura as a perceptual mediator suggests that valuation practices may increasingly depend on documenting procedural authorship and decision-making agency. Transparent disclosure of human\u0026ndash;machine interaction processes could mitigate labeling bias effects and contribute to more stable legitimacy frameworks for hybrid production.\u003c/p\u003e \u003cp\u003eThese implications do not prescribe normative solutions but indicate domains in which perceptual dynamics identified in this study may have practical consequences.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthical Approval\u003c/h2\u003e \u003cp\u003e This research was conducted in accordance with the ethical guidelines for research involving human participants at the Universitat Rovira i Virgili (URV). The project was registered within the institutional ethics management system (CEIPSA), reference number CEIPSA-2023-TDO-0080. Given that the study involved voluntary adult participants, ensured anonymity, and did not collect sensitive personal data, it was conducted under the institutional framework for minimal-risk research in the social sciences. All procedures complied with the Declaration of Helsinki and Regulation (EU) 2016/679 (GDPR).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInformed Consent\u003c/strong\u003e \u003cp\u003e Informed consent was obtained electronically from all participants prior to their participation. Participants were recruited through an academic invitation and provided with an information sheet detailing the study's doctoral nature, the objectives of the research, and the anonymous treatment of data. The full text of the information and consent statement presented to participants is available in Appendix A.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eD.S.E. conceptualized the study, designed the methodology, conducted the statistical analyses, and wrote the main manuscript text. M.C. contributed to the theoretical framework and reviewed the manuscript. All authors approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe de-identified dataset supporting the findings of this study is available from the corresponding author upon reasonable request.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFunding Declaration\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no specific funding, grants, or financial support were received from any public or private agencies for the research, authorship, or publication of this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAllen, I. E., \u0026amp; Seaman, C. A. (2007). Likert scales and data analyses. \u003cem\u003eQuality Progress, 40\u003c/em\u003e(7), 64\u0026ndash;65.\u003c/li\u003e\n\u003cli\u003eBaudrillard, J. (1981). \u003cem\u003eSimulacres et simulation\u003c/em\u003e. Galil\u0026eacute;e.\u003c/li\u003e\n\u003cli\u003eBellaiche, L., Shahi, R., Turpin, M. 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Choosing the number of categories in agree\u0026ndash;disagree scales. \u003cem\u003eQuality \u0026amp; Quantity, 48\u003c/em\u003e(6), 2603\u0026ndash;2627.\u003c/li\u003e\n\u003cli\u003eSalas Espasa, D., \u0026amp; Camacho, M. (2025). From aura to semi-aura: Reframing authenticity in AI-generated art\u0026mdash;A systematic literature review. \u003cem\u003eAI \u0026amp; Society\u003c/em\u003e. Advance online publication. https://doi.org/10.1007/s00146-025-02361-3\u003c/li\u003e\n\u003cli\u003eTaber, K. S. (2018). The use of Cronbach\u0026rsquo;s alpha when developing and reporting research instruments in science education. \u003cem\u003eResearch in Science Education, 48\u003c/em\u003e(6), 1273\u0026ndash;1296.\u003c/li\u003e\n\u003cli\u003eWorld Medical Association. (2013). World Medical Association Declaration of Helsinki: Ethical principles for medical research involving human subjects. \u003cem\u003eJAMA, 310\u003c/em\u003e(20), 2191\u0026ndash;2194.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Semi-aura, AI-generated art, Authenticity, Hybrid authorship, Algorithmic creativity, Digital humanities, Technological legitimacy","lastPublishedDoi":"10.21203/rs.3.rs-8994993/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8994993/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines public perceptions of authenticity in AI-generated art through the conceptual framework of \u003cem\u003esemi-aura\u003c/em\u003e, defined as the perceived retention of human intentional trace within algorithmic production. Drawing on a non-probabilistic sample of 172 participants, the research employs non-parametric comparative tests (Friedman and Kruskal\u0026ndash;Wallis) and Spearman correlational analyses to explore generational, professional, and perceptual determinants of authenticity attribution.\u003c/p\u003e \u003cp\u003eA Friedman test revealed significant differences in perceived authenticity across human, hybrid, and fully AI-generated works (χ\u0026sup2;(2)\u0026thinsp;=\u0026thinsp;246.43, p \u0026lt; .001), confirming a clear hierarchical evaluation pattern. Contrary to expectations derived from symbolic capital theory, no statistically significant differences were observed across levels of artistic experience. A small generational effect was identified (H(2)\u0026thinsp;=\u0026thinsp;11.41, p = .003, ε\u0026sup2; = .06), with younger participants (18\u0026ndash;29 years) assigning lower authenticity ratings to AI-generated works.\u003c/p\u003e \u003cp\u003eSemi-aura demonstrated good internal consistency (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;.84) and showed a moderate positive correlation with the belief that AI-generated art will become more important than human art in the future (ρ\u0026thinsp;=\u0026thinsp;.46, p \u0026lt; .001), as well as with perceived necessity of professional adaptation (ρ\u0026thinsp;=\u0026thinsp;.39, p \u0026lt; .001). Additionally, a moderate negative association between labeling sensitivity and authenticity attribution (ρ = \u0026minus;.45, p \u0026lt; .001) indicates that origin-awareness mechanisms substantially shape evaluative judgments. Contrary to expectations, theoretical AI knowledge did not significantly correlate with semi-aura perception (ρ = \u0026minus;.085, p = .268).\u003c/p\u003e \u003cp\u003eThe findings suggest that authenticity perceptions in hybrid creative ecologies are structured less by professional capital defense and more by generational orientation and perceived continuity of human agency within algorithmic systems. Semi-aura is proposed as a perceptual mediator of technological legitimacy in contemporary artistic contexts.\u003c/p\u003e","manuscriptTitle":"Semi-Aura and the Perception of Authenticity in AI-Generated Art: An Empirical Study of Hybrid Authorship","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-19 12:01:50","doi":"10.21203/rs.3.rs-8994993/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-16T18:47:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"156512397708594142389515103486706791527","date":"2026-04-10T06:34:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"149774909646353184896950269287755112029","date":"2026-04-09T11:19:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"94493324284660706055797503470544330294","date":"2026-04-09T10:12:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"158318177740301065901904241255125208500","date":"2026-04-09T08:42:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-09T08:20:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-31T08:51:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-12T07:45:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2026-03-11T17:27:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"5a32883e-dcab-4fa5-97ef-35007cc135ac","owner":[],"postedDate":"April 19th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-16T18:47:28+00:00","index":70,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":66285190,"name":"Humanities/Cultural and media studies"},{"id":66285191,"name":"Social science/Cultural and media studies"},{"id":66285192,"name":"Biological sciences/Psychology"},{"id":66285193,"name":"Social science/Psychology"},{"id":66285194,"name":"Social science/Science technology and society"}],"tags":[],"updatedAt":"2026-04-19T12:01:50+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-19 12:01:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8994993","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8994993","identity":"rs-8994993","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}