Present Issue

Despite over two decades of commercialization, conversational AI continues to produce functional and communicative errors. This study examines how users’ cultural and linguistic backgrounds influence their experience with conversational AI as well as their error tolerance and adoption rates. We hypothesize that users’ cultural and linguistic backgrounds affect both error tolerance and user experience and that the number of languages a user speaks may amplify this effect. To evaluate this, we conducted in-depth qualitative, 1-hour interviews with eight multilingual users based on a standardized set of questions. All sessions were conducted online on Zoom and were audio-recorded. Results revealed that multilingual users face more diverse linguistic and cultural challenges yet demonstrate greater error tolerance, often continuing to use AI for basic tasks despite inconveniences. Furthermore, monocultural and language users are more likely to discontinue use when errors persisted. By adapting to diverse users, conversational AI can enhance user experience, reduce disparities, and promote equitable access. This study provides insights for developing inclusive, sustainable, and socially responsible conversational AI systems accessible to a global user base. However, limitations include the narrow diversity of participants’ countries and languages and a small sample size. Future research should expand participant diversity to provide a more comprehensive and deeper understanding of conversational AI systems.

keyword : Conversational AI Agent, Voice User Interface, Human-Computer Interaction (HCI), AI, User Studies

This study examined the emotional structure of identically toned two-color combinations in naturally dyed silk fabrics and developed an artificial intelligence-based prediction model. A total of 100 two-color combinations were produced using various natural dyes under different dyeing conditions, and their color emotions were evaluated through expert-based subjective assessments. Basic statistical analyses were conducted to examine the effects of identical-tone types, chromatic versus achromatic tones, physical color characteristics, and quantitative color-combination variables affected factors related to color emotions. Four color emotional factors―Pleasant, Classical, Soft, and Modern―were identified; these factors varied by tone types and chromaticity and were correlated with objective parameters including color variables of single colors and two-color combinations respectively. Random Forest-based prediction model were established for each color emotional factor, with higher performance observed for “Pleasant” and “Soft.” Moreover, feature importance based on SHapley Additive exPlanations (SHAP) values and surrogate linear models to identify affective color variables for emotion factor. Significant correlations between observed and predicted values indicate that the proposed artificial intelligence approach of using Random Forest models can be effectively applied to predicting color emotions in naturally dyed textiles.

keyword : Artificial Intelligence Model, Machine Learning, Color Emotion, Two-Color Combination, Natural Dyeing, 인공지능모델, 기계학습, 색채감성, 2-배색, 천연염색

This study aimed to derive regression equations describing the relationship between clothing pressure and the applied stretch ratio of elastic fabrics, thereby providing quantitative design data for functional sportswear. Five stretchable fabrics composed of polyester or nylon blended with polyurethane were selected, and fabric stretch (%) was measured using the Ziegert and Keil (1988) method, which simulates clothing deformation under low-load, wide-area conditions. Experimental armbands were fabricated with six levels of applied stretch ratios (0%, 10%, 30%, 50%, 70%, and 90%), and clothing pressures (kPa) was measured on both cylindrical models and the human upper-arm models using an AMI-3037 air-pack sensor. Linear regression analysis showed a strong positive correlation between the applied stretch ratio and clothing pressure, with coefficients of determination (R²) ranging from 0.775 to 0.900 depending on the movement posture. The derived equations enabled the quantitative estimation of stretch ratio required to achieve specific pressure levels. Fabric structure significantly influenced the regression models; in particular, the woven Poly_85 fabric exhibited pressure characteristics distinct from those of knitted fabrics. These findings fabrics according to their structural and elastic properties can improve model accuracy. The proposed regression models offer practical guidelines for determining pattern reduction rates in elastic sportswear design and may be extended to compression garments, rehabilitation wear, and wearable healthcare systems.

keyword : Upper Arm Band, Reduction Ratio, Clothing Pressure, Fabric Properties, Sportswear, 상완밴드, 축소율, 의복압, 소재 물성, 스포츠웨어

To enhance the naturalness and emotional resonance of virtual characters in real-time human-computer dialogue, this study proposes a speech-driven framework for compound emotional digital-human interaction. The system first employs a speech emotion recognition module to extract affective features from the user’s voice, followed by a fine-grained compound emotion weight analysis using a GPT-based model. The results are structured in JSON format and transmitted via a local API to the Unreal Engine 5 rendering environment, enabling dynamic mapping from speech parameters to MetaHuman facial action units. To evaluate the system’s effectiveness, 40 participants rated four perceptual dimensions: naturalness and realism, effectiveness of compound emotion expression, emotional resonance, and overall interaction performance. Findings reveal that all four dimensions scored significantly higher than the neutral level (p < 0.001), with Cronbach’s α exceeding 0.70, indicating good internal consistency. Moreover, large effect sizes (Cohen’s d > 0.8) demonstrate the system’s considerable advantages in emotional expressiveness and interaction fluency. Overall, this framework achieves cross-modal emotional transmission through speech-driven compound emotion generation, providing an extensible technical pathway for future research in affective computing and digital-human interaction.

keyword : Affective Engineering, Affective Computing, Experimental Design, VR, Human Factors Engineering, 감성공학, 감성컴퓨팅, 실험설계, 가상현실, 인간공학

With the rapid rise in Korea’s aging population, several companies have started considering older adults as their key consumer segment. Advertisements that create appealing brand impressions (warmth vs. competence) through brand anthropomorphism are expected to be effective for older adults. This study aims to examine the difference in the hierarchical effects on older adults’ attitudes toward ads, brands, and brand purchase intentions between ads that create warm impressions and those that create competent impressions about the brand. In this study, an online experiment was conducted with 221 older adults aged ≥ 65 years. The results obtained from analyzing the data of 184 participants who correctly responded to the screening questions during the experiment are as follows. First, an ad that creates a warm impression of the brand improved consumers’ attitude toward the ad compared with one that emphasizes a competent impression. Second, the attitude toward the ad, influenced by its warm brand impression, influenced brand purchase intention (1) directly and (2) indirectly by improving brand attitude. The study confirmed that ads evoking warm impressions about brands positively affected the hierarchical relationship of “ad attitude → brand attitude → brand purchase intention” and “ad attitude → brand purchase intention.” These findings can broaden the understanding of the psychological mechanisms underlying the effects of ads conveying diverse brand impressions on older adults and help companies plan effective advertising strategies targeting older adults based on brand impressions.

keyword : Older Adults, Advertising Effectiveness, Brand Anthropomorphism, Warmth, Competence, 고령자, 광고 효과, 브랜드 의인화, 온정성, 유능성