General practitioners are often the first point of contact for patients with rare or unclear conditions. While clinical decision support systems (CDSS) based on artificial intelligence can provide valuable assistance, effective communication between physicians remains crucial. Therefore, integrating digital tools to facilitate communication between physicians is considered an important additional pillar of a CDSS for primary care. This study examined the design of a portal to facilitate collaboration between physicians within a CDSS for primary care. The objective was to identify usability issues in the inter-physician exchange portal at an early stage to optimise the design of future portals within CDSSs. A first prototype of the inter-physician exchange portal was evaluated and further developed in two stages. Heuristic and user evaluations were conducted to optimise usability and better understand challenges. The design process resulted in a first interactive prototype of an inter-physician exchange portal. The heuristic evaluation identified 36 usability issues, which informed subsequent refinements to the prototype. User evaluations indicated positive overall perceptions of usability, along with specific recommendations for enhancing the display of status information. The portal achieved an average System Usability Scale score of 81.5, which corresponds to an 'excellent' rating. Developing an inter-physician exchange portal within a CDSS for primary care facilitates the exchange of information between physicians and can thus improve the care for patients with rare and unclear diseases. General practitioners' motivation to engage with the portal is influenced by several factors, notably the existence of an active user community. Future developments should address usability issues and evaluate l acceptance levels, impact on patient care, and long-term user retention in a real-world clinical setting. The integration of existing solutions could also be considered.

Optimal path reconstruction of plant chromosome evolution.

Evaluating accessibility in telehealth platforms using heuristic review: A case study of the pathways application

The online services have grown incredibly fast, and with such growth, also increases the fraudulent online services, such as phishing websites, email spoofing, internet domain, and over-the-phone frauds. These attacks take advantage of the vulnerability in the structure of Uniform Resource Locators (URLs), identity formats of the sender, domain registration system and numbering system in telecommunications. Such dynamic and emerging attacks are not usually that easily detected using traditional rule-based security mechanisms, which make use of fixed signatures and fixed patterns. The study suggests an Artificial Intelligence (AI)-based system to identify fraudulent web platforms based on structural analysis and behavioral analysis of phone numbers, email address, and URLs. The system combines telecommunication metadata analysis to detect suspicious phone numbers, Domain Name System (DNS) and Mail Exchange (MX) record authentication to determine sender integrity and a Random Forest machine learning classifier to profile URL and email-based threats. An interface based in a Flask allows meeting the task of real-time threat scanning and prediction. The model uses data preprocessing, feature engineering, model training, heuristic evaluation, and deployment. Results of trials performed on sample phishing data show high accuracy, precision, recall and F1-score, which are indicators of strong detection results. The framework is designed to be flexible to adapt to the new categories of cyber threats. The results indicate that the suggested system may be effectively used to reinforce traditional cybersecurity defenses through aid of the strengths revealed in automated detection and minimization of the use of rule-based approaches that are considered to be quite static.

High-quality professional development can help teachers build the skills and confidence needed to implement evidence-based practices, such as classroom physical activity breaks. While in-person training is often preferred, virtual asynchronous training offers a flexible alternative for teachers. However, its effectiveness may be limited by design and usability challenges. The aim of this study was to conduct a usability assessment of the Interrupting Prolonged Sitting with Activity (InPACT) virtual teacher training modules, using a human-centered design (HCD) approach to align the training with end-user preferences and needs. The InPACT professional development program includes nine modules delivered through an online platform (Qualtrics XM). A usability assessment was conducted using (1) structured online surveys with elementary school teachers who had completed the modules, (2) a heuristic evaluation based on the Jakob Nielsen 10 usability heuristics, and (3) a competitive analysis of three learning management systems (Moodle, Teachable, and Thinkific) to identify platform strengths, limitations, and insights. Findings and recommendations were compiled to inform module improvements. Eleven teachers completed the survey. They reported that the modules were easy to use, the content was informative and relevant, and they valued the interactive and practical components. Areas for improvement included enhancing content engagement and increasing technical flexibility. The heuristic evaluation identified 14 usability issues across nine of the Nielsen heuristics (eg, navigation difficulties and text-heavy pages). The competitive analysis highlighted features that enhance user experience, such as progress tracking, estimated completion times, interactive elements, and feedback on quiz answers. Usability assessments grounded in an HCD approach can enhance virtual training for educators, improving the uptake and implementation of evidence-based practices, such as classroom physical activity breaks. Five overarching recommendations emerged as follows: (1) removing video time constraints, (2) implementing accurate progress bars, (3) incorporating active learning or retention activities, (4) creating consistent and actionable end pages, and (5) ensuring consistency in titles and references to physical materials. Building on preliminary positive data from revised modules, future research should evaluate the impact of HCD revisions on teacher self-efficacy, training completion, and fidelity of program implementation.

ABSTRACT Universal digital accessibility is relevant to equity and sustainability, yet its pedagogical integration in computing curricula and the AI's educational value of AI lacks empirical evidence. This study investigates the pedagogical effectiveness of multi-modal LLMs in enhancing software development students’ understanding and implementation of accessible User Interface (UI) design. A 12-week teaching experiment involving 42 senior computer science students in an HCI course examined the impact of structured engagement with an LLM on their accessibility knowledge and design skills using a mixed-methods approach. Findings suggest observed improvements (51.7%) in the heuristic evaluation scores of students’ post-intervention UI designs and their conceptual understanding of accessibility principles. Qualitative analysis highlights the LLM’s role as a collaborative design partner and information resource. Quantitatively, significant positive effects (p < .001) were observed in students’ implementation of accessible UI and self-efficacy, though gains in foundational knowledge, competence, and motivation were limited. Epistemic network analysis (ENA) indicated that high-achieving students strategically used the LLM for augmenting design expertise, particularly in accessibility standards, while lower-achieving students used it for reactive correction. The research provides preliminary evidence for the judicious integration of intelligent language models into design curricula, suggesting a reconceptualization of pedagogical frameworks in HCI and software engineering education.

Validation of Video-LLM-based Heuristic Evaluation: Focusing on Dynamic Task Flow

The article presents the author's approach to solving the problem of supporting management decision-making in complex IT projects characterized by a multi-level task structure, resource constraints, and a high level of uncertainty. It is substantiated that the use of exclusively heuristic or only formalized methods is insufficient to ensure consistency, reproducibility, and transparency of task prioritization within iterative planning. The purpose of the study is to develop scientific and applied foundations for management decision support systems based on a combination of heuristic task prioritization using the Eisenhower Matrix and formalized multi-criteria evaluation using the Analytic Hierarchy Process (AHP). The paper proposes an integrated approach for two-layer task ranking: primary assignment to quadrants based on importance and urgency features, followed by the determination of an integrated score based on criteria weighting (impact, priority, deadline proximity, dependencies, effort). A full AHP calculation cycle with a consistency check of the pairwise comparison matrix is provided. The effectiveness of the approach is demonstrated using the example of a financial application support project backlog. The results confirm that the proposed hybrid integration allows for maintaining the speed of heuristic evaluation while simultaneously increasing the validity and transparency of task prioritization. The practical significance of the work lies in the possibility of integrating the proposed approach into the iterative planning procedures of Agile projects. Prospects for further research are related to expanding the experimental sample and analyzing the model's sensitivity to changes in criteria weights.

Given the proliferation of artificial intelligence-based voice assistant in vehicles, multimodal interaction can be performed in multi-task situation which can affect the primary task quality. Therefore, the usability of multimodal interface design in In-Vehicle Infotainment Systems (IVISs) need to be considered to improve the quality of the primary task and lower the cognitive load. The objective of this study is to develop usability principles for multimodal interface (voice and graphical) design of artificial intelligence-based voice assistant in multitasking environments and provide design guidelines in IVISs. We first developed multimodal interface usability evaluation principles and a checklist. To validate the usability principles and identify undefined usability problems, we conducted heuristic evaluation AI based voice assistant in simulated multitasking environment. We then performed usability evaluation of the newly prototyped assistant that was developed based on the guideline. The result clearly demonstrated that renewed voice assistant multimodal interface that was conducted based on the guidelines had positive impact on usability evaluation compared to conventional voice assistant multimodal interface. We expect that our principles will provide effective guidelines for the AI based voice assistant in the multitasking environment.

Upper extremity (UE) impairment is a leading cause of decreased function in breast cancer (BC) survivors. Failure to recognize and properly manage these impairments can lead to significant and sustained functional limitations. Few supports exist to help survivors monitor UE impairments related to BC over time. The purpose of this project was to evaluate usability of the StrongArms-Cancer mHealth system from the patient and the health-provider perspective. This project had 2 parts to evaluate the usability of the StrongArms-Cancer system. Part 1: Participant usability testing; Part 2: Expert heuristics evaluation. In part 1, participants were adults with a previous diagnosis of BC. They were asked to use the think-aloud method while completing 6 tasks in the system to assess usability. Users completed the mHealth App Usability Questionnaire (MAUQ) and System Usability Scale (SUS) at the end of the session. Session transcripts were coded using deductive content analysis to identify specific types of usability issues. Quantitative data from the MAUQ and SUS were summarized using descriptive statistics. Participants in part 2 were health providers living in Canada. They were asked to review and evaluate the mHealth system based on established heuristic principles. Data from the heuristic evaluations were summarized descriptively into main usability issues identified. Think-aloud transcripts highlighted 10 categories of usability issues. Most users (n = 12; 75%) rated the system as "excellent" or better; the mean SUS score was 86.9 (SD 12.06), demonstrating a "superior" score. MAUQ total scores averaged 104.7 (SD: 14.91). The 2 heuristic criteria that resulted in the greatest number of violations (n = 5) were "help and documentation" and "consistency and standards." The findings from think-aloud sessions and heuristic evaluations revealed high user satisfaction and demonstrated effective usability. The StrongArms-Cancer mHealth system shows promise in aiding the proactive surveillance and monitoring of individuals with BC.

Patient portals, as extensions of electronic health records (EHRs), play a critical role in providing patients with access to diagnoses, test results, and clinical communication. Despite their potential benefits, portals often inherit usability issues from EHRs, which can make information difficult to navigate or interpret, and errors can compromise patient safety and affect clinical decision-making. This study aims to evaluate usability shortcomings in patient portals by identifying heuristic violations in portal functions from two leading EHR vendors, Oracle Cerner, and Epic and examine how these issues may limit patient understanding and engagement. Three human factors researchers independently conducted heuristic evaluations on 23 patient portal functions, focusing on ten key diagnostic-related areas (e.g., display of diagnoses, test results, etc.). Usability flaws were mapped to Nielsen-Schneiderman heuristics, assigned a four-level severity score, and reconciled through consensus review. A total of 80 heuristic violations were identified, 76% from Oracle Cerner (n = 61) and 24% from Epic (n = 19). The most frequent violations involved system feedback, inconsistencies, and excess information. The portal functions with the highest number of violations were display of diagnoses, test results, messaging, and clinical notes. Most violations were minor (65%), followed by moderate (20%), with no critical issues observed. Both portals displayed usability gaps, particularly in critical functions like diagnosis display and provider messaging, that may compromise patient understanding of diagnostic information and engagement in care. Although most issues were minor, cumulative effects in high-risk functions could compromise diagnostic safety. Addressing these gaps through human-centered design, standardized usability assessments, and collaboration among vendors, institutions, and policymakers is essential to improve patient understanding and reduce diagnostic error risk.

Adverse drug reactions pose a serious threat to health care, leading to patient harm and substantial economic burden. Dashboards for drug safety surveillance are a valuable tool to tackle it. This qualitative study aims to develop a dashboard for drug safety tracking with active and iterative involvement of end users. To support dashboard development, we formulate and iteratively refine design principles (DPs) for drug safety dashboards using the affordance theory. Following a design science research approach, we conducted 3 cycles of iterative design and evaluation involving the end users. Four professional end users (with expertise in drug screening, drug discovery, and data science) and 6 nonprofessional end users (drug consumers) were engaged in the requirements gathering sessions through co-design workshops, usability testing through think-aloud sessions, and heuristic evaluation. The analysis resulted in a set of 8 DPs refined using the prototype's affordances (ie, actionable properties of the dashboard that guide user interaction and interpretation of data). Following themes emerged in the formulation and refinement of DPs: addressing the bootstrap problem through designing for immediate use (DP1a), allowing identification of patterns through visualizing causality while signalizing uncertainty (DP1b), tracking trends for relevant variables (DP1c), implementing user-controlled views (DP2a) and customizable levels of data granularity (DP2b), guiding user's visual attention through spatial layouts (DP2c), designing for higher public value (DP3a) and providing features to support decision-making for varied stakeholders' groups (DP3b). A high-fidelity dashboard prototype for drug safety surveillance was proposed as a result of applying the final set of DPs. Heuristic evaluation of the prototype revealed an overall usability score of 84%. Applying DPs rooted in affordance theory led to a purposeful and user-relevant artifact that can improve understanding of drug safety data and potentially guide decision-making processes for professional users. Our theoretical contribution lies in providing refined DPs, while also demonstrating how affordances can aid dashboard development in pharmacovigilance. Our findings may be applicable to similar health information systems in related domains.

This study presents a Genetic Algorithm (GA)-driven optimization framework for enhancing the heuristic evaluation function of a Tetris bot. The proposed approach combines offline evolutionary training with real-time dynamic weight adjustments to adapt the bot's strategy to evolving gameplay conditions. Key heuristic features—including hole minimization, line clearing, and surface bumpiness—are weighted dynamically based on board state metrics such as maximum column height. Evaluated through 100 independent simulations, the GA-optimized bot demonstrated significant performance improvements over a baseline bot with fixed heuristic weights: +61.79\% in average lines cleared (91.38 vs. 56.48) and +55.74\% in average game duration (3.17 vs. 2.04 minutes). While decision latency increased marginally (7.33 ms vs. 7.05 ms), this trade-off was justified by the bot's enhanced strategic adaptability, evidenced by reduced performance variance and outlier frequency. The results validate GA's efficacy in optimizing complex, multi-objective decision-making processes in dynamic environments. Future work will explore hybrid GA-reinforcement learning architectures and applications to other real-time strategy games.

Purpose Usability considerations for wheelchair users remain underexplored. This study evaluated usability requirements of a smartphone App (MvBii) for monitoring physical activity and sedentary behaviour in manual wheelchair users with spinal cord injury (SCI). Materials and methods A mixed-methods design was adopted. Manual wheelchair users with SCI completed System Usability Scale, e-loyalty and user experience questionnaires, think-aloud sessions and scenario-based workshops. Six design and research evaluators undertook think-aloud sessions. Qualitative data was analysed thematically and mapped against heuristics. Results Ten participants with SCI (C5-L1; three females) with a mean age of 51 ± 9 years took part. The App received positive ratings on e-loyalty (mean scores, 5.6 ± 1.51 to 6.10 ± 0.99 across items) and user experience (4.3 ± 1.03 to 5.93 ± 0.78) from participants with SCI. A novel heuristics principle was developed to explore “accessibility and inclusion” usability issues. Thematic analysis captured patterned meanings across tasks and heuristics including “Navigating with autonomy” (e.g., challenges with interface clarity and understanding terminology), “Language and representation” (e.g., simplifying using inclusive language and icons), and “Seeing progress not noise” (e.g., physical activity notifications that encouraged self-competition without external pressure). Conclusions This study demonstrates the value of a mixed-methods approach to usability and heuristic evaluation for identifying effective, accessible and inclusive tailoring of physical activity Apps universally and for wheelchair users specifically. These findings can inform refinements to the MvBii app and provide broader insights for designing inclusive and effective mobile health Apps across diverse populations.

The introduction of dark mode as a default option on all major mobile platforms has sparked considerable debate regarding its impact on user experience and visual comfort. We employed a mixed-method approach through an online survey (n=215), a controlled experiment (n=45), and heuristic evaluation of 12 popular mobile apps to assess user preferences and display mode impacts on visual comfort. Survey findings indicated that 68.4% preferred dark mode, mainly due to increased visual comfort and decreased eye strain in low-light environments. The experimental part used within-subject design to show significant differences in subjective comfort ratings (p&lt;0.05) and reading performance across different ambient lighting conditions for various display modes. Heuristic evaluation uncovered critical design issues including optimization of contrast ratio, consistency in the color palette as well as compliance with accessibility standards. Results indicate that the application of dark mode should be context-aware considering ambient light, type of content, and personal sensitivity to vision changes. Our results provide evidence-based recommendations for mobile application designers and contribute to literature regarding visual ergonomics within mobile HCI.

This study evaluates user experience (UX) on the South Sumatra Province Goods and Services Procurement Bureau (PBJ) website using Jakob Nielsen’s Heuristic Evaluation to identify usability barriers affecting e-government transparency and accountability. As public procurement increasingly relies on digital platforms, preliminary assessments revealed critical issues including unintuitive navigation, limited system feedback, and inadequate user guidance that risk undermining public trust. The evaluation employed a 30-item Likert-scale questionnaire aligned with ten heuristic principles and was conducted by expert evaluators. The results show an overall usability index of 81, classified as excellent, with strong performance in consistency and standards (92 percent) and aesthetic and minimalist design (88 percent). Nevertheless, lower scores emerged in help and documentation (69 percent) and error prevention (78 percent), indicating priority areas for improvement. The findings confirm that usability heuristics function as governance enablers rather than purely technical attributes. Improved visibility of system status and effective error recovery mechanisms enhance procurement oversight and are associated with a 20 to 30 percent reduction in vendor drop-off rates. Contextual tooltips and guidance further strengthen accountability in high-value public tenders. The study recommends simplifying navigation structures, strengthening form validation, and implementing searchable frequently asked questions for sustainable provincial e-government implementation initiatives.

Usability engineering is essential for ensuring the safety and effectiveness of medical software, as design-related issues are a leading cause of use errors in clinical settings. Heuristic evaluation provides a practical approach to identifying usability problems, but its outcomes depend heavily on expert interpretation. Large Language Models (LLMs), such as ChatGPT, offer a potential means to augment heuristic evaluation by generating structured, context-aware usability feedback. This study explored the use of ChatGPT to support heuristic assessment of the Radiation Planning Assistant (RPA), a web-based radiotherapy planning tool designed to support clinical teams in low- and middle-income countries. ChatGPT was provided with the RPA user and technical guides, training videos for each functional dashboard, and Zhang etal.'s 14 usability heuristics. The model was instructed to score each dashboard according to these heuristics, using Zhang's 0-4 severity scale, and to propose concrete interface improvements. The resulting feedback was reviewed and scored independently by the RPA developer team and by 13 users during a dedicated User Meeting. Comparative analysis was performed between ChatGPT, developer, and user ratings. ChatGPT identified 26 potential usability issues across six heuristic domains. The developer team considered nine of these actionable, though all were classified as minor (severity≤2). User ratings showed wide variability, with nine suggestions achieving mean scores≥1.5. Qualitative agreement between users and developers was limited, underscoring the importance of diverse perspectives in heuristic evaluation. Three suggestions-enhanced upload logs, reversible actions ("reopen request"), and stronger error prevention-were rated as potentially high priority by a minority of users. ChatGPT's ratings were consistent across dashboards. While ChatGPT did not reveal any critical usability failures, its heuristic assessment proved valuable in prompting discussion, identifying minor refinements, and enriching both developer and user engagement with the RPA's interface design. This study demonstrates that LLMs can serve as an effective, low-cost complement to conventional heuristic evaluation, supporting early-stage usability review and stakeholder dialogue in the development of medical software.

3D-MIHE-RRT-A*: multi-indicator heuristic evaluation hybrid path planning algorithm for UAV navigation in complex environments

This study analyzed the usability of the Korea Disaster Management Resource Integrated Management System (KRMS) using combined user- and expert-based evaluations to explain its low utilization in practice. Expert heuristic evaluation assessed effectiveness, efficiency, satisfaction, interface quality, and related factors, and found that none of the items were rated as “met,” with most judged “partially met” or “not met.” Severity ratings indicated that level 2 (moderate problems) accounted for the largest proportion. User evaluation was conducted with disaster management practitioners from a local public enterprise in Seoul, who completed representative task scenarios and the System Usability Scale (SUS). The average SUS score was 24.6, placing KRMS in the “Not Acceptable” range in international benchmarks. Key difficulties included complex procedures, overly segmented menu structures, and low intuitiveness, suggesting that structural and procedural complexity constrains usability in real work contexts and informing future system improvement and policy considerations.

User experience evaluation in mixed reality systems for education and training: a systematic literature review