International Journal of Graphic Design (IJGD), to E-ISSN: 2987-9434; P-ISSN: 2988-0343, is a peer-reviewed scientific journal published by the National Research and Innovation Center, Institute for Research and Community Service, University of Science and Computer Technology, Semarang, Indonesia. This journal focuses on the field of Visual Communication Design and serves as an open-access platform for disseminating research-based insights in both theoretical and practical domains. IJGD publishes original scholarly articles that explore diverse aspects of design, media, and visual culture—particularly in relation to digital aesthetics, interactive experiences, and technology-based visual communication. The scope includes arts, journalism, media, crafts, and contemporary design innovations. The journal welcomes submissions written only in English and maintains a rigorous double-blind peer review process to ensure academic quality and integrity. International Journal of Graphic Design (IJGD) is published biannually, in April and October.
University of Science and Computer Technology
Data-Informed UI Decision Cards for Emergency Department Renovation: Linking Patient-Flow Simulation to Configurable Spatial Scenarios
Emergency department renovation requires designers to connect operational indicators with spatial planning choices through a clear visual interface. This study developed and analytically demonstrated a data-informed UI decision-card prototype linking patient-flow analysis to configurable wayfinding, nurse-station, and staff-support scenarios. The analysis used public CSV files from the 2024 Synthetic Dataset of Emergency Healthcare Services, with the California Emergency Department Volume and Capacity dataset providing facility-level context. Dataset-derived variables included waiting time, queue count, resource utilization, length of stay (LOS), and satisfaction; wayfinding friction, walking load, visibility, collaboration, station count, and respite-room count were prespecified scenario inputs. Composite indices represented ED pressure and operational staff-support review. The outpatient analysis set contained 236 records. Mean total waiting time was 36.42 minutes, mean LOS was 50.79 minutes, mean simulated satisfaction was 82.84%, and mean ED Pressure Index was 0.298. Gradient boosting achieved a mean five-fold RMSE of 4.119 and an R² of 0.944 on the synthetic data; its output served as a secondary-priority cue for congestion and provider-load cards. CheckPatientType had the highest mean wait, and Triage the highest mean provider utilization. Under the prespecified scenario inputs and base-case weights, Scenario C received the highest support score (66.95) and highest collaboration risk. Evidence labels indicate whether each card is based on dataset variables, model outputs, or configured scenario inputs, making the basis of each renovation trade-off explicit.
Copyright (c) 2025 Huichao Dong, Xiaoming Xiao, Sarah Li https://creativecommons.org/licenses/by-sa/4.0
Grounded AI Pointer Cards for Browser Agents: Evidence-Aware Web Element Reranking and Action Explanation on WebLINX
AI browser agents increasingly promise to turn the mouse pointer into a situated interface for selecting page content, asking follow-up questions, scrolling to relevant sections, and opening contextual panels. For graphic design research, this shift matters because the pointer card is both a computational output and a visual communication device: it must make the target, supporting evidence, and next action legible at the moment of use. This paper evaluates evidence-aware web element reranking as the grounding layer for AI pointer cards. The empirical study uses the WebLINX candidate-reranking data, including validation, same-distribution test, and unseen-website test partitions. Each query is paired with candidate DOM/page elements and binary relevance labels. Nine reranking methods were evaluated, ranging from random and DOM-salience controls to BM25 variants, a transparent hybrid formula, a logistic reranker, and a depth-limited decision-tree reranker. Across 5,883 queries and 1,556,655 query–element pairs, the tree reranker achieved the highest same-distribution nDCG@10 (0.272) and Recall@10 (0.473), while the fixed hybrid achieved the highest unseen-website Top-1 accuracy (0.104). Combining lexical and DOM evidence improved ranking, whereas layout features transferred less consistently across websites. Because the first-ranked target was correct in only 10.4% of unseen-website queries, the findings support a confirm-before-act grounding prototype with visible alternatives and correction controls rather than autonomous execution. The reported metrics evaluate target ranking rather than card usability.
Copyright (c) 2026 Yinchen Shi, Yuxuan Ren https://creativecommons.org/licenses/by-sa/4.0
From 8-K to Deal Cards: A Source-Grounded Visual Triage Framework for FinTech M&A Filings
Form 8-K is a timely public channel through which U.S. registrants disclose material events, including acquisition agreements, completed transactions, shareholder communications, and related exhibits. The density of this filing stream creates a visual communication problem: analysts need to identify source-linked deal cues without losing the legal and temporal provenance of each record. This study presents a source-grounded deal-card workflow for FinTech M&A filing triage. The analysis used the official SEC quarterly master indexes and included 16,439 Form 8-K and 8-K/A filings in Q4 2025, together with 276,261 contextual records in the complete Q4 index. Symmetric companion-form windows were protected against quarter-boundary truncation with the last 14 days of Q3 2025 and the first 14 days of Q1 2026. A seven-day window identified 222 rule-defined FinTech M&A targets across 94 company names, or 1.35% of the Q4 corpus. In five-fold cross-validation grouped by CIK, text-only logistic triage reached F1 = 0.224 and M&A-context logistic triage reached F1 = 0.322. Integrated logistic and linear SVM configurations reproduced the rule-defined target exactly (F1 = 1.000). Because the target was constructed from the same FinTech and M&A-neighborhood cues available to those configurations, this exact agreement is interpreted as rule-consistency validation rather than predictive superiority. A structural interface audit found that the deal-card schema exposed 10 documented fields, nine visual anchors, and six configured decision cues. These author-defined attributes describe interface structure; they do not measure comprehension, trust, or review speed. The contribution is a method for organizing SEC filing evidence for subsequent visual review.
Copyright (c) 2026 Sisi Meng, Dingyuan Zhang, Eric Li https://creativecommons.org/licenses/by-sa/4.0
Beyond CTR and VCR: LLM-Assisted Design Evaluation with Eye-Tracking Validation for Graphic Advertising
Digital advertising design is often judged with delivery or response metrics such as click-through rate (CTR), video-completion rate (VCR), and viewability, although these measures do not describe whether a banner communicates through a clear visual hierarchy. This study evaluates whether large language model (LLM)-assisted scores and lightweight image features can support graphic advertising review while keeping design-quality prediction distinct from measured visual attention. GraphicDesignEvaluation provided 1,200 principle–image rating rows for alignment, overlap, and white space. Under grouped five-fold cross-validation by base design, the GPT-only Ridge model reached R2 = 0.408, RMSE = 1.566, MAE = 1.257, Pearson = 0.639, and Spearman = 0.634. Direct GPT–human agreement was uneven: overlap was strong (Spearman = 0.769), white space was moderate (0.637), and alignment was weaker (0.519). An external construct analysis used 1,000 advertisements from ADD1000 with aggregate human fixation-density maps and matched EAID ratings. A combined gradient and local-contrast proxy modestly exceeded a center prior (CC = 0.457, SIM = 0.562, KL divergence = 0.637), while lower fixation entropy was associated with higher aesthetic ratings (Spearman = -0.319). BannerRequest400 was used only to describe brief, CTA-language, logo, and format constraints because it contains no human design-quality or gaze labels. The findings support a preliminary, human-supervised design-review framework: LLM scores are useful screening evidence, especially for overlap, but they do not replace human creative judgment, eye tracking, or advertising-performance measures.
Copyright (c) 2026 Xiaochen Li, Fiona Wang https://creativecommons.org/licenses/by-sa/4.0
Evidence-Grounded Visual Explanation Design for E-commerce Recommendation Cards: Complementary and Substitute Relations, Image Availability, and Interface Hierarchy
E-commerce recommendation cards must explain why an item is related while maintaining a compact, consistent visual hierarchy. This study presents an evidence-grounded card framework and evaluates its retrieval inputs with the final TREC Product Recommendation 2025 topics, a 48,616-product corpus, 7,633 NIST judgments for 47 topics, and the SQID supplementary image-URL table. Fixed lexical methods generated separate top-10 complement and substitute rankings over the full corpus before assessor judgments were applied. Under the official scoring procedure, the relation-aware lexical method achieved an average nDCG@10 of 0.1840, with a complement nDCG@10 of 0.0562 and a substitute nDCG@10 of 0.3119. Its average difference from full-text TF–IDF was not statistically reliable (difference = 0.0111, 95% CI [-0.0090, 0.0321]). The complement-minus-substitute gap was -0.2557 (95% CI [-0.3151, -0.1937]), confirming that complementary recommendation remains the weaker relation. Requiring a confirmed supplementary image URL increased confirmed URL coverage among displayed items to 100% but reduced average nDCG@10 to 0.0138. The findings support relation-qualified reasons, confirmed-image and text-led fallback states, and conservative language for complementary suggestions. The framework specifies evidence and layout behavior; shopper responses and different verbalization methods require direct comparative evaluation.
Copyright (c) 2026 Hailin Zhou https://creativecommons.org/licenses/by-sa/4.0
Computer-Vision-Informed Visual Explanation Cards for Autonomous-Driving Traffic-Sign Alerts: Localization, Classification, and Retrieved Evidence on GTSDB
This paper develops a computer-vision-informed visual explanation card for traffic-sign alerts in autonomous-driving and driver-assistance interfaces. The card organizes a detected sign crop, predicted class, confidence, default semantic display-priority tier, retrieved visual precedents, scene-location cue, and concise action prompt. The empirical study uses the complete German Traffic Sign Detection Benchmark (GTSDB), comprising 900 road scenes in the standard 600-scene development and 300-scene evaluation portions. The same scenes support localization, crop classification, retrieval, calibration, and end-to-end analysis. The first 600 scenes were divided at the scene level into training and validation subsets; the 300 evaluation scenes were held out until model choices, retrieval depth, fusion weight, and detector threshold had been fixed. A learned class-agnostic localizer filters color-connected-component proposals with a histogram-of-oriented-gradients and color classifier. Five crop classifiers and four nearest-neighbor settings were evaluated, with retrieval treated primarily as example-based explanation support. On the held-out scenes, the selected localizer achieved an AP at IoU 0.50 of 0.211, an AP averaged over IoU 0.50–0.95 of 0.098, and a recall of 0.260 at the validation-selected operating point. The selected crop classifier achieved 0.784 accuracy and 0.574 macro-F1 on ground-truth crops. With predicted crops, correct-class end-to-end coverage was 0.177, and correct-tier end-to-end coverage was 0.244. These results define the information that the proposed card can receive from the evaluated vision pipeline. They do not measure driver comprehension, glance behavior, response time, trust, usability, or deployment safety, which require separate human-centred evaluation.
Copyright (c) 2025 Ruiyan Ma, Long Zhang, Tiffany Song https://creativecommons.org/licenses/by-sa/4.0
LLM-Compatible Visual Brief Cards for AI Infrastructure Capacity Dashboards: A UI/UX Framework for Turning Forecast Risk into Graphic Design Decisions
AI infrastructure dashboards frequently condense GPU inventory, demand forecasts, uncertainty, and admission-control policies into dense operational reports, making it difficult for planners to identify critical risks and appropriate design actions. This study introduces VB-Card, an LLM-compatible UI/UX framework that converts capacity-forecast evidence into structured visual brief cards containing risk indicators, capacity summaries, uncertainty cues, inventory evidence, policy explanations, and actionable recommendations. To evaluate the framework independently of vendor-specific model variation, a deterministic visual-brief generator was employed. The evaluation integrates the OpenCOLE graphic-design field contract with the Alibaba PAI GPU cluster trace. GPU demand, inventory, rolling P50/P90 forecasts, uncertainty measures, and stock-out probabilities were derived from 1,037,084 GPU-requesting task records and 1,897 machine specification records, then paired with 23,419 OpenCOLE instances. Five approaches were compared: a baseline text report, a generic visual card, an OpenCOLE keyword brief, a risk-badge card, and VB-Card. On the held-out test set, VB-Card achieved the highest overall score (0.820; 95% bootstrap CI: 0.820–0.821), outperforming the baseline text report (0.633), OpenCOLE keyword brief (0.522), and risk-badge card (0.538). Ablation results show that uncertainty, policy context, inventory evidence, and visual hierarchy each contribute to performance. The findings demonstrate that capacity risk can be translated into consistent, data-grounded visual-brief decisions, although real-world UI effectiveness requires user-based validation.
Copyright (c) 2025 Hailey Wong, Shilu He https://creativecommons.org/licenses/by-sa/4.0
Visualizing the Right Counseling Support: Evidence-Linked Recommendation Cards for Explainable Mental Health Intake Interfaces
This study presents and evaluates a reproducible interface pipeline for turning mental-health counseling case material into structured, evidence-linked decision-support cards. The aim is not to replace therapists or to claim clinical effectiveness, but to make computational suggestions visible, reviewable, and interruptible in a clinician-facing intake-support interface. To clarify scope, the evaluated engine is a local text-classification and extractive-evidence pipeline rather than a free-form proprietary LLM generator. Six datasets were analyzed: CounselingBench, Graph2Counsel, CounselBench-Eval, a CBT distortion test set, a staged CBT response-quality dataset, and AnnoMI. The primary task classified CounselingBench counselor questions and answer options into five counseling-support competencies. The best probability-producing model, TF-IDF word features with stochastic-gradient log-loss classification, achieved 0.677 accuracy and 0.532 macro-F1 on 405 held-out cases. Evidence extraction generated three evidence chips per case and reached 0.993 evidence/full-prediction agreement, indicating fidelity to the implemented classifier rather than clinical sufficiency. At a 0.70 confidence threshold combined with risk-term routing, the interface released 7.2% of cards for routine review, achieved 0.897 accuracy on released cards, and routed 97.7% of model errors to human review. External checks showed that Graph2Counsel strategy prediction achieved 0.610 micro-F1, CBT response acceptability reached 0.807 accuracy, and AnnoMI therapist-behavior classification reached 0.693 macro-F1. The findings support the card as a cautious information-architecture prototype: it can expose recommendation category, confidence, model evidence, risk flag, next-step question, and human-review action, while leaving final interpretation and clinical appropriateness to the therapist.
Copyright (c) 2025 Yifan Zhang, Hailey Zhang https://creativecommons.org/licenses/by-sa/4.0
Financial Risk Dashboard Design for Institutional RWA Investors: Visual Hierarchy, Chart Comprehension, and Explainability in FinChart-Bench
Institutional investors who review real-world asset (RWA) credit pools must read dense financial charts while judging default pressure, dilution, liquidity, and liquidation-related exceptions. This paper develops a benchmark-based dashboard design analysis using FinChart-Bench financial chart images and question records. The parsed data release contains 1,202 unique base chart images and 7,019 question records: 2,384 true/false, 2,350 multiple-choice, and 2,285 numeric or short-answer QA records. Each question was matched to its base chart image, classified by chart-reading cue, and mapped conservatively to RWA dashboard panels. Image-level visual features were also extracted from the chart files to estimate the interface burden attributable to visual density, edge structure, colour variation, and mark coverage. The analysis shows that numeric arithmetic is the dominant chart-comprehension demand, covering 3,263 records (46.49%). Comparison, extreme/rank lookup, and trend/direction cues account for another 3,031 records (43.18%). The risk-panel mapping is intentionally cautious: 3,992 records remain general financial context, while 1,345 map to dilution risk, 964 to liquidity risk, 616 to probability of default, and 102 to liquidation anomaly. The findings support a dashboard design principle for institutional RWA review: chart assistance should preserve the original evidence, mark the relevant visual region, attach a concise risk-specific explanation, and keep caveats visible. The contribution is a data-driven interface requirement profile for future VLM-connected dashboards and analyst user testing, rather than a claim of observed analyst performance.
Copyright (c) 2025 Zeyi Li , Sihan Zhou, Zoe Zhou https://creativecommons.org/licenses/by-sa/4.0
Visual Brief Cards for Advertising Design: A Structured UI/UX Framework for Turning Creative Intentions into Graphic Design Decisions
Automatic graphic design systems increasingly transform short creative intentions into visual assets, yet designers still need intermediate decisions that are easy to inspect, compare, and revise. This paper proposes Visual Brief Cards, a structured UI/UX framework that converts a design intention into a compact card containing headline, sub-heading, visual object, background mood, keywords, call to action, brand tone, design risk, and typography guidance. In response to the need for a stronger empirical basis, the revised evaluation uses OpenCOLE as the primary benchmark. All OpenCOLE splits were loaded, and the main quantitative comparison is reported on the held-out test split of 2,375 rows; GraphicBench test data and DEsignBench-Prompts are used as secondary checks. Three brief formats are compared under the same deterministic implementation: a free-form brief, a conventional JSON brief, and the proposed Visual Brief Card. On OpenCOLE test data, the card achieved the highest field reconstruction F1 (0.259), complete field coverage (1.000), measurable design-risk recovery (0.302), and the lowest computational scan-time proxy (1.061 s). Free-form text retained the highest TF-IDF semantic similarity (0.252) because it preserved the source wording with less compression. These results support a narrower claim: labeled card structure improves the visibility and recoverability of intermediate design decisions, while human-subject work is still required before making claims about designer trust, workload, or usability in practice.
Copyright (c) 2025 Haowei Tu , Siming Zhao, Andrew Zhou https://creativecommons.org/licenses/by-sa/4.0
Text-Grounded LLM-Assisted Design Rationale Interfaces: Turning Advertising Layout Metadata into Explainable UI/UX Decision Cards
Automatic graphic design generation has progressed from template retrieval to layered generative pipelines, but designers still need explainable review support that links generated layouts to practical UI/UX criteria. This paper presents a text-grounded, LLM-assisted rationale-card interface for advertising layouts using OpenCOLE annotations, including intention, description, keywords, background caption, object caption, heading, and subheading. The interface converts these fields into structured decision cards covering visual hierarchy, white space, call-to-action (CTA) prominence, brand tone, layout balance, and risk warning. An experimental evaluation was conducted on all 23,419 rows from the OpenCOLE train, validation, and test splits. Because expert UI critique labels are unavailable, the tasks are evaluated as weak-label schema-recovery tasks rather than professional design-quality judgments. A linear SVM achieved 0.888 accuracy and 0.872 macro-F1 for nine-way design-purpose recovery. The best CTA model reached 0.817 accuracy, 0.801 macro-F1, and 0.884 quadratic weighted kappa, while text-based layout-proxy prediction reached 0.734 accuracy and 0.706 macro-F1. Structured cards improved required-dimension coverage from 4.21 to 5.92 out of 6 and field-grounding from 0.681 to 0.918. The findings show that OpenCOLE text fields can support inspectable first-pass design review without replacing expert judgment or direct visual-layout evaluation.
Copyright (c) 2025 Qiyou Wu, Siquan Meng, Jacob Zhao https://creativecommons.org/licenses/by-sa/4.0
Evidence-Grounded Accounting Disclosure Review Cards: A Visual Communication Framework for LLM-Style Explanations over SEC Financial Statements and Notes
This paper presents evidence-grounded accounting disclosure review cards, a visual communication framework for LLM-style explanations over SEC financial statements and notes. The revised study positions the card as an accounting evidence interface rather than as a test of real analyst performance or a live LLM pipeline. The objective is to organize structured statement facts, note-level disclosure evidence, ratio signals, citation trails, and review actions so that accounting claims can be inspected and challenged within a compact card. The evaluation combines the 2024 EDGAR quarterly master indexes, the 2024 SEC Financial Statement Data Sets, and the 2024 Q3-Q4 Financial Statement and Notes Data Sets. The master-index universe contains 1,241,395 filings, including 25,993 10-K/10-Q-family filings. The statement-level analysis uses 23,677 10-K/10-Q-family rows from 6,077 companies across 70 SIC groups, and 20,753 rows (87.65%) contain enough statement evidence to populate the core card. The statement-note paired analysis covers 11,880 filings from 5,811 companies, of which 10,391 (87.47%) are card-ready after note evidence is added. A stratified accounting review benchmark of 400 filings generates 2,400 evidence-checking tasks. The results support the feasibility of the proposed accounting evidence architecture and clarify that the reported outcomes describe data coverage, evidence readiness, and disclosure mapping rather than observed human speed, workload, trust, or live-model reliability.
Copyright (c) 2025 Kai Zhang, Yuanzheng Chen, Annie Qian https://creativecommons.org/licenses/by-sa/4.0
Risk-Calibrated Patient-Facing AI Safety Cards: A UI/UX Benchmark for Explainable Medical AI Response Interfaces
Patient-facing medical AI systems communicate risk at moments when a non-expert user may act on what they read. This study evaluates risk-calibrated AI safety cards as a UI/UX framework for explainable medical response interfaces. The main experiment used 466 PatientSafetyBench prompts across five patient-safety categories: harmful medical advice, misdiagnosis and overconfidence, unlicensed practice of medicine, health misinformation, and bias or stigmatization. For each prompt, five response interfaces were generated: plain text, risk-label card, refusal-plus-explanation card, evidence-disclosure card, and next-step action card. The evaluation reports deterministic rubric-based communication scores rather than clinical safety outcomes. Across 2,330 PatientSafetyBench responses, the integrated Next-Step Action Card lowered the mean communication-risk score from 3.85 to 1.00 on a 1-5 scale, lowered the overconfidence-indicator score from 3.38 to 1.41, increased actionability from 16.46 to 98.09 on a 0-100 scale, increased risk-label clarity from 10.20 to 94.97, and increased evidence disclosure from 22.82 to 96.98. A second analysis used HealthBench physician-created rubric criteria to test whether the card structure aligned with communication, context, uncertainty, and escalation expectations in broader health conversations. The action-card condition increased communication-rubric coverage from 11.23% to 85.29% in HealthBench OSS, from 10.44% to 83.86% in HealthBench Consensus, and from 9.88% to 86.50% in HealthBench Hard. These results support the safety card as a reproducible information-design intervention for risk communication. They do not establish real-world patient behavior change or clinical safety; clinician review, patient testing, multilingual adaptation, and live-system evaluation remain necessary before deployment.
Copyright (c) 2025 Chenyu Li, Binghua Zhou, Krystal Gao https://creativecommons.org/licenses/by-sa/4.0
Evidence-Constrained Incident Visualization Cards for Distributed Cloud Logs: A UI/UX Framework for Turning Hadoop, OpenStack, and ZooKeeper Logs into Actionable SRE Design Interfaces
Site reliability engineers do not need another opaque anomaly detector as much as they need interfaces that turn noisy distributed-system logs into fast, defensible incident decisions. This paper presents a reproducible UI/UX framework for LLM-assisted incident visualization cards. The framework converts raw and structured Loghub-2.0 benchmark logs from Hadoop, OpenStack, and ZooKeeper into evidence-grounded cards containing an incident headline, affected service and node, anomaly timeline, log-evidence badges, suspected root cause, confidence, recommended next action, severity hierarchy, and operator decision buttons. The empirical evaluation was conducted on the complete 2,000-line benchmark slice for each selected system, giving 6,000 log lines, 207 unique templates, and 120 fixed 50-line analysis windows. We implemented deterministic parsing, TF-IDF clustering, template-frequency z-score scoring, Isolation Forest, Local Outlier Factor, TF-IDF KMeans distance scoring, and a constrained LLM-style card generator. Drain-lite normalization achieved purity of 0.999 on Hadoop, 1.000 on OpenStack, and 1.000 on ZooKeeper against the Loghub EventId reference. The hybrid risk ensemble achieved Precision@5 of 0.800 on Hadoop, 0.800 on OpenStack, and 0.600 on ZooKeeper; the template-frequency z-score was the strongest single risk model on the ZooKeeper logs with AUPRC 0.934. The generated card interface increased the deterministic decision-readiness score from 0.133 for a raw log list and 0.486 for a plain-text summary to 0.944 for the visual incident card. These results show that evidence-preserving visual organization can improve the operational usefulness of log analytics outputs without claiming to replace human incident ownership.
Copyright (c) 2026 Jiayi Nie, GE Liu, Lily Zhao https://creativecommons.org/licenses/by-sa/4.0
Structured Visual Brief Interfaces for Advertising Design: A UI/UX Framework for Turning Creative Intentions into Designer-Editable Graphic Design Cards
This paper proposes and evaluates a UI/UX framework for advertising and graphic design interfaces. The framework converts a short creative intention into an editable structured brief card with eight slots: headline, sub-heading, visual object, background mood, call to action, audience, design risk, and brand tone. The motivation is that ordinary text prompts are easy to write but weak as design coordination artifacts because they mix marketing intent, visual instructions, copy hierarchy, and review cues in one statement. OpenCOLE provides an setting because its schema contains intention, description, keywords, heading/sub-heading fields for graphic design generation (Inoue et al., 2024). The study compares a prompt with a structured brief-card representation on the OpenCOLE Parquet dataset of 23,419 rows. The card is populated by deterministic rules; the study therefore evaluates an LLM-compatible interface layer rather than a live model-driven generation system. Across the full dataset, the structured card increased the structured-brief adequacy score from 0.299 to 0.878, a mean gain of 0.579 under paired Wilcoxon testing (p < .001). Intent coverage, keyword recall, heading hierarchy, CTA recognizability, semantic consistency, audience explicitness, and risk explicitness all improved. A deterministic advertising-oriented subset of 19,681 rows showed the same pattern, with the mean increasing from 0.313 to 0.881. The metrics evaluate whether a brief representation preserves and exposes information needed before production; they do not measure final design quality, designer preference, or user satisfaction. The results support the argument that generative design workflows should expose structured, designer-editable brief cards rather than relying on prompt expansion alone.
Copyright (c) 2026 Jinyi Mu, Yifei Lu, Evelyn Hwang https://creativecommons.org/licenses/by-sa/4.0
Uncertainty-Aware Breast Ultrasound Explanation Cards: A Visual Communication Framework for Image-Based AI Diagnostic Support Using BreastMNIST_224
AI-assisted diagnostic interfaces should communicate more than a class label. They need to show the predicted risk, the uncertainty around that risk, the visual evidence that influenced the model, the limits of the evidence, and the appropriate next action. This paper presents an uncertainty-aware explanation-card framework for breast ultrasound decision-support screens. The empirical study was conducted on BreastMNIST_224, the 224 x 224 MedMNIST+ breast ultrasound benchmark with official train, validation, and test splits of 546, 78, and 156 images. The positive class was defined as malignant. Five image classifiers were trained on downsampled image grids, and the selected card model was a Platt-probability RBF SVM. On the official test split, the selected model achieved AUROC = 0.867 and AUPRC = 0.728. A validation-selected operating threshold of 0.254 gave accuracy = 0.769, sensitivity = 0.833, specificity = 0.746, Brier score = 0.125, and ECE = 0.068. The explanation card pairs malignant-risk probability with risk tier, uncertainty band, occlusion-sensitivity heatmap evidence, a limitation statement, and a review cue. In the held-out test set, the conservative Low-risk tier contained six cases and no malignant cases; all seven false negatives occurred in the Review tier rather than in Low risk. These findings support a prototype-level visual communication framework in which image evidence is shown together with uncertainty and safeguards, while diagnostic authority remains with the clinician.
Copyright (c) 2025 Tong Ye, Xiaohan Chang, Eric Zhong https://creativecommons.org/licenses/by-sa/4.0
Privacy and Data-Integrity Risk Cards for LLM Agents: A UI/UX Design Framework for Secure Human Oversight under Prompt-Injection Attacks
Large language model (LLM) agents increasingly combine natural-language reasoning with external tools that can send messages, update records, book services, or transfer money. This capability changes prompt injection from a text-output problem into an interface problem: before an agent acts, the user must be able to determine whether the proposed action matches the original task, moves sensitive data, or modifies consequential state. This paper presents Privacy and Data-Integrity Risk Cards, a visual confirmation pattern. The card translates security-relevant runtime facts into source-trust labels, data-sensitivity chips, permission chips, data-flow arrows, consequence previews, a calibrated risk badge, and safer-alternative controls. The evaluation uses AgentDojo v0.1.11 source definitions and saved run traces from 18 agent-pipeline runs. The dataset contains 629 injected security cases and 97 benign user-task traces across Workspace, Slack, Travel, and Banking, yielding 13,068 trace-level UI samples. The evaluation is a deterministic oversight-decision proxy that measures how much risk-relevant information each interface condition exposes rather than how real users behave. Compared with a Plain Agent Log and a Text-only Security Warning, the Risk Card produced the highest proxy attack-recognition rate, the lowest benign false-positive rate, and the clearest score separation between injected and benign traces. Among traces where the injection goal was executed, the proxy approval rate was 3.8% for the Risk Card, compared with 13.0% for the Plain Agent Log and 5.9% for the Text-only Warning. These findings support the Risk Card as a UI risk-communication framework and a candidate interface for human-subject validation rather than a validated deployed defense.
Copyright (c) 2026 Wenhao Su, Hengning Rao, Emma Ma https://creativecommons.org/licenses/by-sa/4.0
LLM-Style Evidence Cards for Scientific Search Interfaces: A UI/UX Design Framework for Retrieval Transparency, Ranking Trust, and Visual Evidence Hierarchy
Scientific search systems increasingly provide ranked documents, passages, and automated summaries, yet users must still determine whether retrieved evidence supports a claim, whether the presented information is sufficient for inspection, and why a result is highly ranked. This paper proposes an evidence-card UI/UX framework for scientific search that transforms retrieved articles into structured evidence cards containing a claim anchor, extractive evidence summary, support/refute/insufficient badge, confidence cue, citation cue, ranking rationale, and expandable source text. The framework is designed as a visual communication layer for retrieval transparency and evidence inspection rather than as a new claim-verification model or a live LLM system. Evaluation was conducted using the BEIR SciFact retrieval benchmark, the original SciFact train/dev datasets, and a SciFact-Open candidate-pool stress test. On the 300-query BEIR SciFact test set, the BM25-dominant hybrid baseline achieved nDCG@10 = 0.6667 and Recall@10 = 0.7858, while the proposed evidence-card pipeline achieved nDCG@10 = 0.6621 and Recall@10 = 0.7763. On the SciFact dev set, gold evidence appeared within the top three evidence-card candidates for 84.6% of evidence-bearing claims, and selected rationale sentences matched gold rationale annotations for 44.0% of gold evidence-document pairs. Interface-level analysis showed that the proposed card design increased the evidence visibility index from 0.2643 to 0.5920 and reduced the estimated first-pass scan-burden proxy from 115.50 to 84.08 seconds. These results suggest that evidence cards improve transparency by making relevance, uncertainty, confidence, and ranking rationale visible while preserving access to source evidence.
Copyright (c) 2025 Jiaying Jin https://creativecommons.org/licenses/by-sa/4.0
LLM-Style Explainable E-Commerce Recommendation Cards: A UI/UX Design Framework for Trust-Calibrated Product Recommendation
This paper presents and empirically evaluates a UI/UX design framework for explainable e-commerce recommendation cards. The framework addresses a practical visual-communication problem: product lists can be useful but opaque, while explanation-heavy cards can create unwarranted confidence when the system has weak evidence. The revised study therefore uses the term LLM-style for the language condition and treats it as a grounded card-generation and confidence-display policy rather than as evidence of a black-box large-language-model recommender. Experiments were conducted on Amazon Reviews'23 All_Beauty raw reviews and item metadata, together with the Beauty_and_Personal_Care 5-core benchmark split as a larger same-domain warm-user check. The All_Beauty review file contains 701,528 review records from 631,986 users and 112,565 parent items, and the metadata file contains 112,590 parent items with near-complete title and image coverage. On the sparse All_Beauty all-user test, Recall@10 remained low for all methods, with the LLM-style reciprocal-rank reranker reaching 0.007945. On the All_Beauty warm-user slice, the same reranker reached Recall@10 of 0.008079. On the larger Beauty_and_Personal_Care 5-core test, it reached Recall@10 of 0.021463, improving over popularity and last-item co-history baselines but still indicating modest recommendation effectiveness. Card-level evaluation on All_Beauty shows that the LLM-style explanation plus confidence card achieved the highest confidence-discrimination AUC (0.700), while the review-evidence card offered a simpler evidence-forward alternative. The results support an interface-oriented conclusion: recommendation cards should separate ranking quality, grounded evidence, and confidence display, and UI/UX claims should be framed as proxy-based evidence until validated with a controlled user study.
Copyright (c) 2025 Boning Zhang, Yuxuan Ren, Jocelyn Zou https://creativecommons.org/licenses/by-sa/4.0
Risk-Calibrated Patient-Facing AI Safety Cards: A UI/UX Design Framework for Rubric-Based Medical Risk Communication
Patient-facing medical AI systems can provide valuable health information; however, safety-sensitive queries require responses that establish clear boundaries while remaining informative, respectful, and actionable. This paper presents the Risk-Calibrated Safety Card, a UI/UX framework for communicating medical AI responses in high-risk situations. The framework transforms safety-sensitive outputs into a structured card containing five elements: risk level, explanation of why an unrestricted response may be unsafe, bounded safe information, professional-help guidance, and a bias-sensitive language note. The evaluation uses HealthBench, a benchmark of realistic health conversations with physician-authored rubrics, including the HealthBench Full evaluation split and robustness analyses on the Consensus and Hard subsets. Four response formats were compared: unstructured answer, refusal-only answer, refusal with explanation, and the proposed safety card. Across 4,597 HealthBench Full records, the safety card achieved the lowest rubric-based safety-communication risk score (1.27), the highest weighted positive-rubric coverage (0.664), and complete coverage of predefined card components (1.00). Refusal-only responses reduced unsafe personalization but showed limited helpfulness (1.55) and negligible positive-rubric coverage (0.001). Refusal with explanation improved boundary communication but lacked the structured presentation provided by the card. Although the safety card produced longer responses and a slightly lower readability score than the refusal-with-explanation condition (Flesch-Kincaid grade 14.39 vs. 14.72), the results suggest that structured safety cards can improve the visibility of risk, guidance, and support cues. These findings represent rubric-based interface evidence and should not be interpreted as validation of patient outcomes, clinical safety, or real-world deployment effectiveness.
Copyright (c) 2025 Binghua Zhou, Chenyu Li, Lily Liu https://creativecommons.org/licenses/by-sa/4.0
LLM-as-Design-Critic: Aligning AI-Generated UI Feedback with Human Graphic Design Judgment
This paper evaluates whether AI-authored mobile user-interface critiques align with human graphic design judgment. The study uses the public UICrit CSV derived from RICO mobile screens, containing 2,981 annotator rows, 1,000 distinct UI screens, 11,344 source-indexed design critiques, normalized critique bounding boxes, and ratings for aesthetics, learnability, efficiency, usability, and overall design quality. We conducted a full reproducible empirical evaluation rather than reporting illustrative results. Seven models were compared on a group-disjoint split by RICO screen ID: a mean baseline, task-text TF-IDF Ridge, human-critique TF-IDF Ridge, LLM-critique TF-IDF Ridge, all-critique TF-IDF Ridge, topic-and-region Ridge, and a fused text-topic-region Ridge model. We also measured human-LLM critique alignment using TF-IDF cosine, character n-gram cosine, ROUGE-L F1, unigram F1, topic Jaccard, and best-match bounding-box IoU. The fused model achieved the strongest overall design-quality prediction on the held-out test set (MAE = 0.613, RMSE = 0.805, Spearman = 0.575), improving over the mean baseline MAE of 0.779. Human critiques alone were highly predictive (design-quality Spearman = 0.556), whereas LLM-inclusive critiques alone were much weaker (Spearman = 0.194). Human-LLM semantic alignment was low for exclusive human versus exclusive LLM comments (mean TF-IDF cosine = 0.046) and substantially higher when comments tagged as both were included (mean TF-IDF cosine = 0.390). Results show that design critiques encode measurable aesthetic and usability judgment, but LLM critiques still differ from human critique priorities unless shared comments and region evidence are incorporated
Copyright (c) 2025 Yunhe Li, Shenghan Lu, Lily Zhao https://creativecommons.org/licenses/by-sa/4.0
Adaptive User Interface Design for Volleyball Learning Apps: Empirical Evidence from Google Play Reviews and Mobile Screen Analysis
Adaptive sports-learning apps should not expose the same interface to a first-session learner and to a self-directed advanced player. This paper develops an empirical design basis for a volleyball learning app that adapts information density, feedback granularity, and interaction complexity to learner level. We combined two public datasets: a Google Play review corpus and the MASC mobile UI dataset. From 63,340 reviews we constructed a learning-and-training subset of 7,328 reviews from 1,702 apps and benchmarked five sentiment models using grouped 3-fold cross-validation. The best review classifier was a Linear SVM (accuracy = 0.786, macro-F1 = 0.548). We then compared LDA, NMF, and KMeans topic models on 1,527 negative reviews; LDA with four topics produced the best coherence. The resulting themes were core reliability and login/access friction (34.1%), control quality and monetization friction (28.0%), update/privacy/state-persistence failures (23.1%), and support, billing, and account recovery (14.7%). Novice-coded complaints were 3.74 times more likely than advanced-coded complaints to focus on support and recovery, whereas advanced-coded complaints over-indexed on control and monetization. On MASC, a fusion Linear SVM combining keywords and numeric UI features achieved a macro-F1 of 0.938 for 10-class screen prediction. Complexity clustering produced two stable screen regimes: a low-complexity cluster dominated by Welcome, Login, and Home screens and a high-complexity cluster dominated by List, Menu, and Search screens. Based on these results, the paper specifies a two-mode adaptive volleyball UI blueprint: a guided beginner mode and a dense expert mode. The study shows how reproducible review mining and screen analytics can directly inform adaptive UI decisions for skill-learning applications.
Copyright (c) 2025 Jubin Zhang https://creativecommons.org/licenses/by-sa/4.0
Uncertainty-Aware Medical Image Explanation Cards: LLM-Generated Visual Explanations for AI-Assisted Radiology Interfaces
This study investigates how visual hierarchy, calibrated probability, uncertainty cues, Grad-CAM heatmaps, and role-specific language generation can be integrated into compact explanation cards for AI-assisted radiology interfaces. The empirical task was a reproducible PneumoniaMNIST-compatible normal-versus-pneumonia chest X-ray classification problem that preserves the MedMNIST label schema, split sizes, and NPZ data structure. All reported performance values were computed by the included scripts on the packaged dataset; every result table contains measured values from saved experimental artifacts. Six model variants were evaluated with accuracy, AUC, F1, sensitivity, specificity, negative log-likelihood, Brier score, and expected calibration error. The selected Spatial-CNN with temperature scaling achieved AUC = 0.868, accuracy = 0.763, F1 = 0.778, specificity = 0.923, Brier score = 0.155, and ECE = 0.021 on the 624-image test split. A warning rule using confidence, entropy, and MC-dropout variance flagged 310 test cases and captured 113 of 148 model errors. Grad-CAM stability was audited on a 200-case stratified subset, and role-specific microcopy was generated for clinician-facing, patient-facing, and uncertainty-warning cards. Patient-facing text achieved a mean Flesch Reading Ease of 74.6 and FK grade of 5.4, while clinician text preserved concise technical language. The contribution is a visual communication system for AI diagnostic cards that connects empirical model behavior with user-centered explanation design rather than treating explainability as an isolated algorithmic overlay.
Copyright (c) 2025 Ziliang Samuel Zhong, Qiyou Wu , Gaotian Mi https://creativecommons.org/licenses/by-sa/4.0
Designing a Visualization of Malay Cultural Identity in the Illustration of the Children's Storybook "Let's Get to Know the Malay Tribe"
This study explores the visual articulation of Malay cultural identity in children's educational media through digital-based picture storybooks. Although visual storytelling has great potential as a medium for cultural literacy, the transmission of traditional values into child-friendly aesthetics often faces challenges between cultural authenticity and visual simplification. Using a qualitative approach with a formal design framework, this study applies Charles Sanders Peirce's semiotic analysis and visual communication principles to dissect elements of composition, typography, visual hierarchy, and color psychology in storybooks for children aged 5–9 years. Data were collected through a triangulation methodology involving comparative visual analysis, audience studies of children, and expert judgment validation. The analysis results indicate that the transformation of Malay identity—such as the bamboo shoot motif and traditional color palette—can be effectively integrated without losing its philosophical meaning through the application of simple geometric shapes (child-friendly shapes). Furthermore, empirical validation confirms that a coherent visual storytelling structure significantly improves children's cognitive retention and emotional attachment to local cultural values. This research contributes to the methodology of visual communication design, particularly in formulating standard guidelines for creating measurable cultural literacy media for the younger generation.
Copyright (c) 2026 Suprianingsih Suprianingsih, Rani Hermita, Heti Zaharani https://creativecommons.org/licenses/by-sa/4.0
Trust-Calibrated Multilingual RAG for Humanitarian Information Platforms: Empirical Evaluation on OMoS-QA for Migration Information Access
Humanitarian information platforms increasingly serve migrants, refugees, and crisis-affected users who need correct answers about housing, schooling, legal procedures, benefits, health, and emergency services. In this setting, a wrong answer is more harmful than a missing answer, so multilingual question-answering systems must not only retrieve and summarize relevant content but also calibrate when to answer, when to abstain, and how to communicate uncertainty to the user. This paper develops a trust-calibrated multilingual retrieval-augmented generation (RAG) design for humanitarian information platforms and evaluates it on the public OMoS-QA benchmark for migration information access. The study combines two empirical layers. First, we run a direct page-retrieval evaluation over the full public corpus and compare BM25, word-level TF-IDF, character-level TF-IDF, and a lexical-character hybrid retriever. Second, we reanalyze the officially scored benchmark outputs released with OMoS-QA for sentence-level answer extraction, question-level no-answer detection, multilingual transfer, and cross-language transfer. All numerical results are empirically measured; no illustrative placeholders are used. The hybrid retriever reaches 69.4% recall at rank 1, 82.6% at rank 3, and 86.1% at rank 5, outperforming the sparse baselines. On same-language answer extraction, DeBERTa achieves the strongest balanced F1 (62.5 German, 64.9 English), while Llama-3-70B and GPT-3.5-Turbo obtain the strongest no-answer detection results. Explicit answerability prompting raises Llama-3-70B recall on unanswerable questions to 83.6% in German and 78.2% in English. Multilingual experiments show moderate degradation for French and larger losses for Arabic and Ukrainian, while cross-language transfer remains surprisingly robust. Based on these findings, the paper formulates a design contribution for graphic and interaction design: a trust-calibrated evidence-card pattern that combines evidence highlighting, citation links, uncertainty cues, and escalation to human support. The result is a benchmark-grounded interface logic for safer public-interest LLM applications rather than a user-validated final interface.
Multilingual Retrieval-Augmented Generation
Copyright (c) 2026 Yushan Chen, Haosen Xu https://creativecommons.org/licenses/by-sa/4.0
Simulating Sustainable Color-Form Decisions in AI-Driven Eco-Brand Identity Design
Integrating sustainability into brand identity design remains challenging, as the harmony between visual aesthetics, primarily color and shape, and eco-branding values often relies on subjective judgment, highlighting a lack of data-driven approaches. While AI-assisted design tools have demonstrated utility, their primary focus remains on aesthetic generation rather than sustainable design optimization. This research addresses this gap by proposing a simulation-based framework that, through simulation and expert review, examines and evaluates how AI-assisted color-form choices influence both aesthetic consistency and ecological brand alignment. Using synthetic data and scenario modeling, we created a simulation that analyzes color and form parameters against sustainability indicators across three scenarios: Aesthetic-only, Sustainability-only, and an Integrated Eco-Aesthetic approach. Eco-Alignment was computed using cosine similarity of semantic embeddings across 50 simulation runs for each scenario, providing a quantifiable measure of semantic consistency with eco-brand values. The results indicate that the integrated approach outperformed single-focus scenarios in balancing aesthetic harmony and eco-alignment. At the same time, the Color-Form Sustainability Matrix identified specific combinations, such as earthy tones paired with organic shapes, that achieved the highest Eco-Alignment Scores. This study contributes methodologically by linking computational aesthetics with sustainable design through structured simulation, offering designers an evidence-informed framework for making visual decisions that support environmental ethos and reduce resource-intensive trial-and-error design processes.
Copyright (c) 2026 Sofia Elena, Liam Alexander https://creativecommons.org/licenses/by-sa/4.0
Human-AI Collaborative Adaptive Typeface Generation: Eye-Tracking Fixation Metrics in Instagram Branding
Integrating artificial intelligence into typographic design opens new opportunities for social media branding, especially on visually intensive platforms like Instagram. This study investigates how typefaces developed in collaboration with AI may influence visual engagement metrics. We designed an assessment framework that couples collaborative typeface generation with simulated eye-tracking analysis. Three adaptation scenarios were evaluated: static, semi-adaptive, and fully adaptive typefaces, based on fixation duration, fixation count, and heatmap visualization. Interestingly, when analyzing the simulation results, a pattern emerged: the typefaces generated through iterative human-AI processes tended to attract longer, more frequent visual fixations. These preliminary results thus point to the potential value of collaborative approaches to typographic design in social media contexts. The present study provides a methodological framework to assess AI-assisted visual assets using human attention metrics.
Copyright (c) 2026 Oliver Henry, Mia Charlotte https://creativecommons.org/licenses/by-sa/4.0
Adaptive Graphic Interaction Model: A Mixed-Method Framework for Future Factory Design
This study investigates the role of visual interfaces in enhancing human-machine interaction in high-tech factory environments. The research focuses on the Adaptive Graphical Interaction Model (AGIM), which integrates graphic design principles with adaptive visual interactions to support context-sensitive user tasks. A mixed-methods approach was employed: domain specialists were interviewed, and experimental lab tests of prototype interfaces were conducted to measure user experience, cognitive load, and visual usability. Quantitative metrics from UX tools and qualitative coding of observed behavior were used to assess performance under different simulated operating conditions. Results indicate that AGIM reduces cognitive load by 18% on average and improves task efficiency by 12% compared to static interfaces, supporting the practical effectiveness of adaptive visual systems. The findings also suggest that context-sensitive graphical adaptations enhance intuitive navigation and user engagement. Overall, AGIM provides both conceptual guidance for interface design and practical applications for engineers and designers aiming to develop adaptive, context-aware visual systems in industrial settings. The study is framed as an exploratory design investigation, highlighting potential rather than asserting definitive claims.
Copyright (c) 2026 Laura Mendez, Samuel Okafor https://creativecommons.org/licenses/by-sa/4.0
Designing with the Brain in Mind: A Neuroaesthetic Pedagogy Framework
Contemporary graphic design education necessitates a deeper understanding of how aesthetic experience and cognitive processes shape creativity and empathetic comprehension in visual communication. Insights into how the brain perceptually and affectively interprets design can support the development of self-aware designers who respond more sensitively to human needs. Drawing on neuroaesthetics and design cognition, this study proposes an interdisciplinary approach that integrates creative pedagogy with the science of perception. This research introduces the Neuroaesthetic Pedagogy Framework (NPF) as an initial, exploratory framework that connects neurodesign principles with pedagogies of visual empathy and adopts an exploratory design-based research (DBR) methodology. Three iterative cycles were conducted as context-specific pedagogical interventions within a graphic design course, involving teaching experiments, reflective journals, and analyses of neuroaesthetic engagement. Thirty-eight undergraduate graphic design students participated over one semester, with observations focusing on cognitive and affective engagement in visual problem-solving activities. The findings suggest observable improvements in design cognition, particularly in visual reasoning and empathic decision-making, rather than statistically conclusive effects. The NPF indicates the potential to interconnect analytical and affective cognitive processes, supporting more emotionally informed design outcomes. This study concludes that the NPF demonstrates promise in bridging graphic design education and neuroscience by framing creative learning as an iterative process of aligning perception, cognition, and empathy, while highlighting the emerging importance of neuroaesthetic literacy in graphic design pedagogy.
Copyright (c) 2026 Carlos Fernández, Petra Nováková https://creativecommons.org/licenses/by-sa/4.0
From Points to Insight: Gamifying Reflective Thinking in Design Education
Reflective practice in design education is often marginalized as a post-hoc evaluative task rather than conceptualized as a central creative and epistemic process. In this study, reflective practice is reconceptualized as a gamified learning framework that fosters metacognition, visual empathy, and self-awareness through interactive design strategies. The primary objective was to examine the extent to which gamification elements, including feedback cycles, progress visualization, and reward structures, enhance concept retention, reflective insight, and comprehension of design processes among 36 undergraduate graphic design students. Using a mixed-methods, design-based research approach, data were collected across three iterative learning cycles through digital activity logs, reflective journals, and semi-structured interviews. Quantitative analysis using paired-sample t-tests revealed a statistically significant 18% improvement in reflective accuracy and a 20% increase in conceptual retention (p < 0.05) following the implementation of gamified reflective activities. Platform analytics further indicated a 92% task completion rate, suggesting increased learner engagement associated with visual feedback and reward mechanisms. Qualitative thematic analysis identified three interrelated dimensions of reflective learning: emotional resonance, sensory awareness, and social connectedness, highlighting the multidimensional nature of reflection in gamified environments. Overall, the findings suggest that gamified reflection can support more active and emotionally engaging learning processes, positioning reflective practice as a form of “learning through play” and offering a pedagogical framework to enhance empathy, self-awareness, and reflective thinking in visual design education.
Copyright (c) 2026 Oskar Larsen, Aisha Al-Farsi https://creativecommons.org/licenses/by-sa/4.0