From Empathy to Contempt: A Five-Stage Emotion Transition Framework for Human-AI Relationships

Eun Joung Kim, Jun Young Lee | JDMCT 6(1) 1-12

Abstract : Emotional bonds between humans and AI have grown deep enough to carry existential consequences, yet academic understanding of this phenomenon remains fragmented and static. Existing research has largely reduced human-AI emotion to binary categories such as positive vs. negative and has failed to capture the temporal, stage-wise evolution that defines these relationships. This study draws on Robert Plutchik's psychoevolutionary theory of emotion as its core analytic lens and proposes the Five-Stage Emotion Transition Framework (FS-ETF), which integrates the CASA paradigm, affective computing, and Human-AI Attachment theory. The framework charts a cumulative, bipolar, and stage-wise affective trajectory across five emotion pairs that branch into success and failure pathways: empathy vs. apathy, anticipation vs. anxiety, trust vs. distrust, reliance vs. helplessness, and affection vs. contempt. The framework also demonstrates that affection and contempt at Stage 5 operate as two sides of the same coin, which offers a new analytic frame for the cases of excessive dependence and abrupt severance with AI companions that have recently risen to social prominence. By reconceptualizing human-AI emotion as a multidimensional and evolutionary process, this study extends the explanatory power of psychoevolutionary emotion theory into the contemporary technological context.

Keyword : Human-AI relationship, AI companions, Plutchik's psychoevolutionary theory, attachment, Five-stage Emotion Transition Framework

http://dx.doi.org/10.29056/jdmct.2026.06.01

A Study on the Aesthetic Representation and Visual Implementation of the Artificial Intelligence Filter Bubble Phenomenon

Dong-Jo Kim, JungYoon Kim | JDMCT 6(1) 13-24

Abstract : Recent advancements in AI-based personalization algorithms have served as a decisive catalyst for shifting the information consumption paradigm of modern societyone centered on communication to one centered on isolation. The "filter bubble" phenomenon, situated at the heart of this structural shift, acts as a mechanism that blurs the boundaries between the virtual and the real when combined with the technological opacity of platforms. The recursive loop structure of endlessly circulating information is not merely the result of cognitive bias but is identified as a strategic environment derivedthe mutual interplay between the agency of algorithms and human confirmation bias. This study seeks to conceptualize these aspects of technological isolation and alienation through the aesthetic metaphor of the "bubble" and, through the realization of empirical media art works, to demonstrate the ontological crisis in which virtual illusions encroach upon actual realitya media-aesthetic perspective. Furthermore, by critically illuminating the emptiness of data concealed within the fantasy of technological optimism, this study proposes the reflective function that art must uphold in an algorithm-centered society, as well as the possibility of alternative modes of perception.

Keyword : AI Algorithm, Filter Bubble, Virtuality and Reality, Media Art, Non-human Actor

http://dx.doi.org/10.29056/jdmct.2026.06.02

Assessment of Foundation Models' Applicability to Visualization

Junseo Choi, Juhan Lim, Suhyeon Kim, Younhyun Jung | JDMCT 6(1) 25-32

Abstract : Direct Volume Rendering (DVR) and Cinematic Rendering (CR) are representative visualization techniques that transform Computed Tomography (CT) and Magnetic Resonance Imaging (MRI)-based 3D volume data into intuitive images using color, lighting, and shading information. These visualization images allow efficient understanding of complex anatomical structures, but the results vary greatly depending on user-defined parameters such as transfer functions and lighting. Existing Region of Interest (ROI)-based automatic enhancement methods primarily rely on scribble inputs, leading to limited accuracy and consistency. Recently, foundation models capable of performing segmentation using simple prompts such as pointsboxes have opened new possibilities. SAM has demonstrated strong segmentation performance on natural images, while MedSAM has shown high accuracy on medical images, suggesting the potential to apply intuitive ROI specification to visualization images as well. However, visualization images form a hybrid domain combining characteristics of natural images and medical images, making it unclear which model is more suitable. This study compares and evaluates the segmentation performance of SAM and MedSAM on DVR and CR-based visualization images to identify the model best suited for the hybrid domain and analyze its applicability.

Keyword : Cinematic Rendering, Direct Volume Rendering, Foundation Model, Vision Transformer, Visualization

http://dx.doi.org/10.29056/jdmct.2026.06.03

Vision-Conditioned Gating for Product-Store-Level Demand Forecasting of New Fast-Fashion Products

Jeongho Kim, Saebom Lee, Chang Choi | JDMCT 6(1) 33-44

Abstract : Fast fashion demand forecasting at the product-store level is a critical task for improving market responsiveness, profitability, and operational stability. Nevertheless, product-store level data are often sparse and highly volatile, and forecasting becomes particularly challenging in cold-start settings where new products lack historical sales records. In response, this study proposes Vision-Conditioned Gated RNN (VIGRNN), based on the observation that the predictive usefulness of auxiliary information varies across product-store instances. VIGRNN is an adaptive forecasting model that dynamically controls attribute, release-date, and trend information on a per-sample basis, conditioned on visual information. Experimental results show that, under cold-start settings, the proposed model outperforms an existing multimodal RNN-based baseline by reducing WAPE by 3.6% and MAE by 2.47%. In addition, it reduces GFLOPs by 5.33% and the number of parameters by approximately 32%. These findings suggest that the proposed model effectively balances forecasting accuracy and computational efficiency, highlighting its potential for practical deployment in real-world industrial environments.

Keyword : Fast Fashion, Cold Start, Demand Forecasting, Multimodal Learning, Vision-Conditioned Gating

http://dx.doi.org/10.29056/jdmct.2026.06.04

Entity-Aware Bidirectional Attention-Based Gated Fusion for News Classification

Jun-Yeong Kang, Yousung Yeon, Chang Choi | JDMCT 6(1) 45-58

Abstract : As the online news environment shifts toward combining diverse media such as text and images, research on multimodal AI-based news classification is underway to address the limitations of information loss in single-modality methods and improve classification accuracy by analyzing complex contexts. However, existing fusion techniques based on simple concatenationunidirectional interaction struggle to preserve the core context in lengthy texts and fail to resolve the semantic discrepancies caused by irrelevant visual noise, ultimately leading to model bias and degraded classification performance. This paper proposes an entity-aware bidirectional attention-based gated fusion designed to maintain the key textual context and mitigate the bias issues induced by visual noise. The proposed architecture consists of a preprocessing stage that prevents context loss using NER and keywords extractedunstructured text, a bidirectional cross-attention stage that aligns features through cross-modal referencing, and a gated fusion stage that suppresses noise amplification via modality-specific bias initialization. Experimental results using the New York Times N24News dataset demonstrate that the proposed model improves accuracy by up to 14.04% and F1-score by up to 15.73% compared to existing baseline models, validating its applicability for robust news classification even in environments characterized by semantic misalignment and noise.

Keyword : Multimodal News Classification, Bidirectional Cross-Attention, Gated Fusion, Named Entity Recognition, Visual Noise

http://dx.doi.org/10.29056/jdmct.2026.06.05

A Study on Video-Based Service Prototyping through the Use of Generative AI

Hyeyoung Yoo | JDMCT 6(1) 59-68

Abstract : This study examines the potential and production process of video-based service prototyping using generative AI and analyzes its practical significance through an industrial complex policy hackathon case. The research combines a literature review on generative AI, service prototyping, and video-based prototypes with a case study. The service concept proposed at the 2026 Industrial Complex Policy Hackathon, which integrates the Galaxy Road, Safe Stop, and Smart Pole, was translated into a generative AI-based scenario, storyboard, key visuals, and video prototype. The findings demonstrate that generative AI contributes to enhancing efficiency and experimentation in the early planning stage by transforming abstract service ideas into visual and narrative outputs within a short period of time. However, the study notes that AI-generated outputs require further examination in terms of consistency between scenes, copyright, ethical issues, and practical feasibility. Ultimately, this study suggests that generative AI can significantly expand the scope of video-based prototyping methodologies within the service design process.

Keyword : Generative AI, Service Design, Video-Based Prototyping, AI Production, Service Concept

http://dx.doi.org/10.29056/jdmct.2026.06.06

Voice-to-Mesh: A Mixed Reality Pipeline for 3D Content Generation Using Text-to-3D Model

Yejin Kim, Minjun Kang, Suhyeon Kim, Younhyun Jung | JDMCT 6(1) 69-76

Abstract : Recent advances in text-to-3D generation have enabled the creation of 3D assetsnatural language prompts. However, Mixed Reality (MR) authoring workflows still rely on manually prepared 3D assets, making it difficult to generate and utilize new s. In addition, generated mesh assets often vary in scale, origin, orientation, and coordinate conventions, requiring additional normalization and alignment for direct placement in MR environments. This additional processing interrupts the continuity of the content creation workflow. To address these limitations, we present an end-to-end pipeline that generates 3D assetsmultilingual voice commands and automatically normalizes the generated assets for use in MR environments. Speech-to-Text recognition and Large Language Model-based parsing convert natural language into structured commands. The proposed pipeline enables generated mesh assets to be immediately utilized, stored, and reused within the MR environments. Through a storytelling scenario, we demonstrate the feasibility of interactive MR content creation.

Keyword : Mixed Reality, Text-to-3D generation, Generative Content, Interactive MR

http://dx.doi.org/10.29056/jdmct.2026.06.07

An Integrative Exploration of the Characteristics, Structure and Effectiveness of Metaverse Counseling for Children and Adolescents

Hye-Seong Jeon | JDMCT 6(1) 77-87

Abstract : This study aims to integratively analyze the user characteristics, counseling structure, and effectiveness of metaverse counseling studies conducted for children and adolescents. To achieve this purpose, a literature review method was applied to relevant studies published in Korean accredited academic journals, and a total of five articles that met the research purpose were finally selected for analysis. The analysis focused on four categories: user characteristics, utilized metaverse platforms, counseling structure (counseling format, session composition, and theoretical foundation), and counseling effectiveness. The analysis of counseling structure revealed that metaverse counseling was primarily utilized for elementary school students, and various platforms such as MetaForest, ZEP, MAVE, Spatial Studio, and ifland were applied. In addition, differences were identified in interaction methods and counseling environments depending on the platform. Group counseling was the predominant counseling format, and short-term interventions consisting of 6 to 10 sessions were found to be the most common structure. Some studies applied cognitive behavioral therapy and solution-focused therapy; however, many studies showed limitations in that their theoretical foundations were not clearly presented. The analysis of effectiveness demonstrated positive outcomes, including enhanced self-understanding and identity formation, improved behavioral regulation and reduction of problem behaviors, and strengthened interpersonal and group relational functioning. This study is meaningful in that it integratively reviews the structural characteristics and effectiveness of metaverse counseling for children and adolescents and provides foundational data for the theoretical establishment and practical application of digital-based counseling.

Keyword : metaverse counseling, children and adolescents, counseling structure, counseling effectiveness

http://dx.doi.org/10.29056/jdmct.2026.06.08

Cultural Usability and Cultural Policy in the Era of Digital Transformation

Hye-Kyung Shin | JDMCT 6(1) 89-101

Abstract : This study examines the transformation of cultural policy in the era of digital transformation through the framework of 'Cultural Usability'. Moving beyond conventional accessibility-centered approaches, the study argues that cultural policy should focus on how citizens meaningfully interpret, experience, and utilize cultural resources in everyday life. To explore this perspective, the study conducts a comparative analysis of cultural policies in Korea and France. In particular, France has institutionalized long-term systems of cultural participation through governance structures such as EAC, EPCC, DRAC, and Micro-Folie, emphasizing mediation, learning pathways, and collaborative governance. By contrast, Korea has achieved notable progress in expanding digital cultural infrastructure and accessibility; however, limitations remain in terms of sustainable governance, professional mediation, and long-term operational continuity. Based on these findings, this study proposes a three-dimensional model of cultural usability consisting of cognitive, participatory, and structural dimensions. Ultimately, the study suggests that future cultural policy should move beyond infrastructure-centered expansion toward experience-centered cultural design that promotes citizens' active cultural engagement and sustainable participation.

Keyword : Cultural Usability, Cultural Accessibility, Digital Transformation, Digital Cultural Policy, Cultural Governance

http://dx.doi.org/10.29056/jdmct.2026.06.09

Dynamic Capabilities for Global K-Beauty: Integrating Digital and Regional Markets in Sulwhasoo

Seo-Woo Kim, Joohee Han | JDMCT 6(1) 103-110

Abstract : This study analyzes how Sulwhasoo, the flagship luxury brand of Amorepacific and a symbol of Korean premium beauty, secures competitive advantages amidst structural shifts in the global beauty market through the lens of Dynamic Capabilities. By applying Porter's Five Forces and the VRIO framework, the research finds that Sulwhasoo demonstrates strategic flexibility in reconfiguring its unique ginseng-based R&D assets to meet evolving environmental demands. Key success factors include differentiated marketing mix adaptations across regions: luxury localization in China, clinical-based digital marketing in North America, formulation diversification in Southeast Asia, and sustainability branding in Europe. The analysis reveals that Sulwhasoo overcomes global market uncertainties by integrating traditional brand heritage with digital platforms. This study provides a strategic framework and practical implications for K-beauty brands seeking sustainable growth in the era of digital transformation.

Keyword : K-Beauty, Dynamic Capabilities, Digital Transformation, Regional Adaptation, Sulwhasoo, Premium Branding, Amorepacific

http://dx.doi.org/10.29056/jdmct.2026.06.10

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