In recent years, Artificial Intelligence Generated Content (AIGC) technology has experienced explosive growth. Early image generation technologies, such as Generative Adversarial Networks (GANs), primarily focused on image inpainting or simple style transfer. Their core objective was to make generated images physically realistic, addressing the issue of whether a drawing resembles the real object [1]. Liu [2] (2024) points out that cutting-edge painting tools (such as Midjourney), video production models (such as Sora), and even brain-computer interfaces can now acquire skills such as playing Go, composing music, calligraphy, dancing, and piloting airplanes. This will profoundly reshape people’s concepts of learning and knowledge. However, with the emergence of new-generation diffusion models represented by Stable Diffusion, AI has begun to develop robust semantic understanding capabilities. By simply inputting a text prompt, users can enable AI to generate highly complex images.

This technological advancement marks a transition of AIGC from simple pixel imitation to a higher-level semantic generation. Through continuous dialogue with users, AI incessantly identifies, learns, and integrates personalized user elements, and structurally processes the output elements to present them organically in a user-centric manner. This essentially accomplishes the establishment of a relationship with the user, serving as a deep simulation of human communication patterns [3]. Despite the exquisite quality of AI-generated artwork, a critical question persists: Does AI truly “understand” what loneliness and warmth are? Or does it merely calculate through massive datasets that the word “loneliness” has a high probability of co-occurring with black and silhouette? Gan and Wang [4] (2024) argue that emotion, as a vital adhesive, plays an essential role in driving human-computer interaction and constructing tightly woven social formations. Bender et al. [5] (2021) once compared large models to stochastic parrots, suggesting that they merely mimic the probabilistic combinations of human language without generating genuine consciousness. Emotion, as something unique to humans, was once considered the final barrier preventing artificial intelligence from replacing humans. However, on the one hand, driven by the trend of AI shifting toward emotional generation, humans seek barrier-free interaction when communicating with machines and anticipate establishing human-like feelings and intimate relationships with them. On the other hand, a growing body of scientific research indicates that artificial intelligence is capable of mimicking human emotions. Driven by this dual dynamic, human-computer emotional interaction is gradually becoming routinized [6].

The significance of this study lies in testing AI’s reactions to abstract emotional words such as “loneliness” and “warmth” to observe the boundaries of its comprehension. This not only allows us to develop a more objective understanding of AI’s capabilities but also holds practical value for future applications. If AI can accurately translate emotions into images, it will become a more empathetic human assistant in future artistic creations; additionally, in the field of psychological healing, it can help individuals visualize inexpressible emotions, thereby assisting in psychological counseling [7].

In recent years, the rapid advancement of Text-to-Image generative AI models has profoundly transformed visual communication, prompting a burgeoning body of critical scholarship across media studies, discourse analysis, and human-computer interaction. Existing research on AI-generated images can be broadly categorized into three main areas. First, scholars conceptualize AI image generators as semiotic technologies that encode and reproduce social norms and ideologies. Rather than acting as neutral tools, models like Stable Diffusion and DALL-E function as cultural aggregators deeply inscribed with pre-existing societal values, meaning that their visual outputs inherently reflect the biases embedded within their training corpora [8]. Second, a significant strand of research focuses on the systematic amplification of stereotypes and social biases in AI visuals, extending from demographic identities to occupational stigma. While early audits revealed gender and racial disparities in professional representations [9], recent scholarship has begun to unpack how AI constructs specific occupational meanings. For instance, in an investigation of Chinese text-to-image models, Ye [10] (2026) introduced the concept of “visual prototype lock-in” to demonstrate how algorithms transform historical media discourses and social stigmas into rigid, recurring visual sign systems (such as default dark-uniformed, serious-expression middle-aged males for urban management officers). Similarly, studies in health and social domains show that AI models recycle reductionist cultural tropes, such as biomedical framings of “dementia” [11] or homogenized consumer-driven aesthetics of “teenagers” [8]. Third, recent methodological interventions treat AI image generation as a diagnostic tool for critical discourse analysis. Scholars like Hart [12] (2026) utilize AI-generated images to examine multimodal constructions, demonstrating how specific textual prompts systematically trigger recurring visual schemas—such as large, unbounded crowds of migrants—that evoke specific ideological framings and power relations.

Current research has successfully established that AI-generated imagery is far from politically or culturally neutral; it actively participates in the construction and naturalization of social realities. However, existing research exhibits two notable limitations. On the one hand, most studies predominantly concentrate on Western models (e.g., DALL-E, Midjourney), leaving the performance, cultural localization, and bias mechanisms of domestic or cross-lingual AI platforms underexplored. On the other hand, while extensive attention has been paid to demographic markers (race, gender) and macro-social topics (migration, health), there remains a scarcity of research investigating how generative AI translates abstract emotional concepts into visual narratives. Addressing this gap is crucial for understanding the boundaries of AI’s semantic comprehension and its capacity for nuanced, cross-cultural visual storytelling.

To investigate the semantic comprehension boundaries and visual narrative capabilities of Text-to-Image models regarding abstract emotions, this study adopts a comparative experimental design combined with visual semiotic analysis. The research design is structured around three core dimensions: model selection, experimental variables, and analytical framework.

First, in terms of model selection, this study establishes a cross-cultural comparative sample by deploying one international leading model (Leonardo.Ai) and two Chinese domestic models (Doubao and Kling). This selection allows for a rigorous examination of how different cultural backgrounds and training datasets influence AI’s decoding of abstract concepts. Second, the experimental procedure employs a controlled-variable approach centered on prompt engineering and iterative optimization. The study investigates two contrasting emotional themes—“loneliness” (representing alienation and psychological vacancy) and “warmth” (representing interpersonal connection and intimacy). For each theme, a two-stage iteration is conducted:

Stage 1 (Baseline Generation, V1): Basic prompts containing abstract emotional keywords (e.g., extreme loneliness or family reunion dinner) are inputted to observe the models’ baseline cultural decoding and default visual signifiers.

Stage 2 (Optimized Generation, V2): Advanced prompts incorporating technical parameters (e.g., cinematic lighting, shallow depth of field, negative space ratio), specific cultural signifiers (e.g., round tables, bamboo steamers), and negative constraints (e.g., no turkey, no fireplace) are introduced to test the models’ capacity for psychological atmosphere conveyance and cross-cultural correction.

Finally, for data analysis, the study draws upon Roland Barthes [13]’ theory of visual semiotics to decode the generated images beyond mere aesthetic evaluations. A comprehensive evaluation framework is established based on two core dimensions: (1) The Signifier-Signified Matching Degree, which quantifies the precision of emotional mapping by evaluating the number of effective signifiers (e.g., color tones, lighting, spatial composition) versus interfering noise; (2) Cultural Adaptability and Innovation, which assesses the models’ ability to avoid cultural bias (such as Western-centric symbols in Chinese contexts) and break free from rigid emotion-signifier lock-ins. Through this multi-tiered design, the study systematically maps the transition of models from basic physical scene presentation to sophisticated, context-aware emotional storytelling.

Following the preliminary comparative experiments and iterative optimizations, this study integrates perspectives from semiotics and cognitive psychology to conduct an in-depth decoding of the visual outputs generated by the international model (Leonardo.Ai) and the domestic models (Doubao and Kling). Moving beyond superficial evaluations centered solely on aesthetic appeal, this research establishes a systematic evaluation framework to measure the emotional expression capabilities of artificial intelligence. To ensure the empirical rigor and objectivity of the evaluation, the assessment was conducted using a standardized quantitative semiotic coding procedure administered by two independent evaluators (two postgraduates in visual semiotics). The coding scheme operated under explicit decision rules: effective signifiers were defined as concrete visual elements (e.g., color tones, lighting, negative space, cultural props, and postures) that directly map to the core emotional signified without contradiction, whereas interfering signifiers were elements that disrupted the target emotional atmosphere. Inter-Coder Reliability (ICR) was calculated using Cohen’s Kappa and intraclass correlation coefficients (ICC), yielding high consistency (κ = 0.86, ICC = 0.91), with discrepancies resolved through consensus. Based on this methodology, the evaluation framework encompasses two core dimensions and subsequent comparative (empirical) analyses:

Core Dimension II: Cultural Adaptability and Innovation in Emotional Expression. Established to address the complexities of cross-cultural emotional expression, this dimension contains two sub-indicators: cultural symbol adaptability and symbol innovation freedom. Cultural adaptability examines whether artificial intelligence can select appropriate symbols based on the specific cultural context of the emotion, such as avoiding Western symbols like turkeys or fireplaces within a Chinese contextual framework for the theme of warmth. Symbol innovation freedom measures whether models can transcend rigid emotion-symbol linkages, such as attempting to portray loneliness through a static individual amidst a bustling crowd rather than merely relying on a monochromatic cold-toned scene. This evaluative dimension avoids technological determinism while highlighting the humanistic attributes inherent in AI emotional expression.

In testing the loneliness theme, international models such as Leonardo.Ai tended to generate a theatricalized sense of isolation. They often relied on exaggerated Tyndall lighting effects to forcibly create visual impact, imparting a deliberate sense of performative detachment. In contrast, Kling demonstrated superior immersive narrative capabilities. Regarding emotional symbol matching, Kling performed most prominently: its generated images constructed a complete semiotic chain, linking a blue-teal color tone (cold signifier) to alienation (loneliness signified), cinematic side-lighting outlining the human silhouette (lighting signifier) to individual presence, and two-thirds dark negative space (compositional signifier) to psychological vacancy. Kling achieved an effective signifier count of eight with zero invalid symbol interference. Conversely, while Leonardo.Ai and Doubao incorporated the core symbol of a rainy window, the additionally introduced burning candles (warm light signifier) conflicted with the signified loneliness, resulting in higher invalid interference. Regarding cultural adaptability and innovation, none of the three models exhibited apparent cultural bias, though they all fell into the stereotypical association that “cold colors equal loneliness”. Nevertheless, Kling attempted to replace static cold tones with the dynamic blur of rain streaks on glass, showing slightly higher innovation.

In terms of warmth matching, Doubao and Kling demonstrated significant advantages. Doubao constructed a complete and precise semiotic chain that closely aligned with the emotional cognition of Chinese family reunions, linking a red tablecloth (color signifier) to festiveness (warmth signified), dynamic hot-pot steam (detail signifier) to the vibrancy of daily life (yanhuoqi烟火气), and an elder serving food (action signifier) to interpersonal bonding. Meanwhile, Leonardo.Ai, having corrected Western symbols such as turkeys and fireplaces, retained the long-table layout as a signifier while accurately conveying the signified Chinese-style barrier-free reunion. Regarding cultural adaptability and innovation, after prompt calibration, none of the three models displayed noticeable cultural bias. However, the domestic models Doubao and Kling generated more exquisite imagery that better resonated with domestic festive atmospheres, exhibiting higher innovation than the international model. These visual symbols are inherently high-context symbols deeply rooted in native cultural memory. Domestic models did not merely render physical warmth; rather, through the meticulous restoration of everyday details, they awakened the collective subconscious of the audience, thereby achieving instantaneous comprehension and emotional resonance in transmission efficiency.

Through visual narrative experiments focusing on the themes of loneliness and warmth, this study observes that within the limited scope of the three tested text-to-image systems (Leonardo.Ai, Doubao, and Kling) and the two prompted scenarios, Kling demonstrated high alignment between the input prompts and the generated visual symbols.

The empirical observations indicate that the tested domestic models effectively translated the specified Chinese cultural contexts into structured visual signifiers, exhibiting competitive performance in foundational rendering quality, lighting logic, and semantic-visual alignment under these specific conditions. These results suggest that advanced multimodal architectures can successfully instantiate abstract emotional concepts and cultural nuances through probabilistic pattern matching based on their training data, rather than indicating genuine emotional comprehension or subjective awareness.

In conclusion, while acknowledging the constraints of a limited sample size, a restricted set of prompts, and the absence of audience validation, this study empirically documents how Text-to-Image technology can achieve nuanced visual expression when guided by structured prompt engineering. Future research should incorporate broader datasets, diverse emotional categories, and empirical audience evaluations to further explore the intersection of global technological frameworks and localized aesthetic representations in AI-driven visual storytelling.

The authors would like to thank the 2027 Key Cultivation Project of Philosophy and Social Sciences of Hebei University: “A Study of the Construction and Recontextualized Dissemination Mechanisms of Intangible Cultural Heritage Emotional Memory Discourse in Digital Spaces Based on NLP” (HBU2026SS007) for the support in this research.

Yipu Kong: conceptualization, methodology, data collection original draft.

Zhengjie Guo: data analysis, review & editing, funding acquisition, project administration

All authors have read and agreed to the published version of the manuscript.