Human-computer Interaction Design Research Articles

Empirical methods for enhancing user experience in human-computer interaction design with digital media integration

Abstract. This study rigorously examines the application of empirical methodologies aimed at augmenting the user experience (UX) within the domain of human-computer interaction (HCI) design, with a pronounced emphasis on the seamless integration of digital media. In an era where digital media intricately intertwines with our daily lives, crafting interfaces that are both intuitive and engaging is becoming increasingly essential. This paper embarks on an in-depth analysis of a variety of empirical research techniques, including but not limited to, user studies, A/B testing, and comprehensive analytics. These methodologies are pivotal in providing critical insights and feedback that inform and refine the HCI design process. By judiciously incorporating these empirical methods throughout the design and development phases of digital media applications, designers and developers are equipped to forge more effective, accessible, and immersive user experiences. This approach ensures that digital media interfaces are not only functional but also highly engaging and responsive to user needs and preferences. The research findings underscore the critical role of user-centered design practices in significantly enhancing user engagement, satisfaction, and usability. It emphasizes that understanding the end-user's perspective and integrating their feedback into the design process is fundamental in creating digital media interfaces that resonate with users. Through a detailed exploration of these empirical methods, the study provides a comprehensive framework for improving digital media experiences, highlighting the necessity for a synergistic approach to HCI design that prioritizes user satisfaction and usability. This body of work contributes valuable insights into the ongoing discourse on the optimization of digital media interfaces through empirically informed design strategies, advocating for a user-centric approach in the rapidly evolving landscape of digital media technology..

Intentional or Designed? The Impact of Stance Attribution on Cognitive Processing of Generative AI Service Failures.

With the rapid expansion of the generative AI market, conducting in-depth research on cognitive conflicts in human-computer interaction is crucial for optimizing user experience and improving the quality of interactions with AI systems. However, existing studies insufficiently explore the role of user cognitive conflicts and the explanation of stance attribution in the design of human-computer interactions. This research, grounded in mental models theory and employing an improved version of the oddball paradigm, utilizes Event-Related Spectral Perturbations (ERSP) and functional connectivity analysis to reveal how task types and stance attribution explanations in generative AI influence users' unconscious cognitive processing mechanisms during service failures. The results indicate that under design stance explanations, the ERSP and Phase Locking Value (PLV) in the theta frequency band were significantly lower for emotional task failures than mechanical task failures. In the case of emotional task failures, the ERSP and PLV in the theta frequency band induced by intentional stance explanations were significantly higher than those induced by design stance explanations. This study found that stance attribution explanations profoundly affect users' mental models of AI, which determine their responses to service failure.

Working Memory Capacity for Mid-Air Gestures in Human–Computer Interaction

Mid-air gesture interactions have emerged as one of the predominant modalities used in human-computer interaction (HCI). However, extant research on gestures predominantly concentrates on the design of gestures for singular functionalities, which requires a comprehensive investigation into gesture interaction design from an integrative HCI system perspective. Working memory capacity plays a pivotal role in constraining the number of gestures accommodated within an HCI system. This study adopts the change detection paradigm to conduct an empirical investigation into the working memory capacities associated with mid-air gestures, aiming to elucidate the working memory capacity for such gestures. The experimental design included two independent variables: five distinct categories of mid-air gestures (physical, symbolic, metaphorical, abstract, and mixed) and memory set sizes ranging from two to nine items. The findings revealed that working memory capacities for different types of gestures fluctuate between three and five items, with symbolic gestures presenting the most negligible cognitive load for recall and abstract gestures proving to be the most challenging. The outcomes of this research have significant implications for both the design of individual gestures and the overarching design of gesture interaction systems, serving as a valuable reference for future endeavors in HCI design optimization.

Depth-based interactive assistive system for dementia care

Dementia often leads to reduced autonomy and the need for help with activities of daily living, such as the use of the lavatory. In response, we present ToiletHelp, a novel system to provide automated, step-by-step guidance to individuals with dementia when using the lavatory. In such an intimate environment, it is crucial to maintain the user’s privacy, which ToiletHelp achieves by sensing only depth maps. ToiletHelp uses a depth sensor to detect when the user is in need of assistance and tailors the guidance accordingly, using a combination of verbal and visual prompts. Our main contribution is the development of the first assistance system for people with dementia in such a sensitive environment as the lavatory. In addition, what sets ToiletHelp apart is its comprehensive involvement of 60 participants, 33 end-users and 27 healthcare workers, throughout its development process. A user-centered evaluation validates the interaction design, demonstrating enhanced feelings of security and independence among older adults, as well as successful comprehension of instructions by participants with dementia. Caregivers report high satisfaction with the system’s modalities and confirm its effectiveness in reducing their workload while assisting end-users. These findings not only validate the success of ToiletHelp and underscore its potential benefits but also contribute to the field of human-computer interaction design. Our comprehensive analysis of the results provides valuable information for researchers developing solutions for individuals with dementia, which is especially valuable given the limited existing literature on the subject.

Human-Computer Interactive Design of Education Information Recommendation System for Private Colleges Based on AI-Powered CAD Technology

Human-Computer Interactive Design of Education Information Recommendation System for Private Colleges Based on AI-Powered CAD Technology

Effect of a Male-Targeted Digital Decision Support Application Aimed at Increasing Linkage to HIV Care Among Men: Findings from the HITS Cluster Randomized Clinical Trial in Rural South Africa.

Linkage to HIV care remains suboptimal among men. We investigated the effectiveness of a male-targeted HIV-specific decision support app, Empowering People through Informed Choices for HIV (EPIC-HIV), on increasing linkage to HIV care among men in rural South Africa. Home-Based Intervention to Test and Start (HITS) was a multi-component cluster-randomized controlled trial conducted among 45 communities in uMkhanyakude, KwaZulu-Natal. The development of EPIC-HIV was guided by self-determination theory and human-computer interaction design to increase intrinsic motivation to seek HIV testing and care among men. EPIC-HIV was offered in two stages: EPIC-HIV 1 at the time of home-based HIV counseling and testing (HBHCT), and EPIC-HIV 2 at 1 month after a positive HIV diagnosis if not linked to care. Sixteen communities were randomly assigned to the arms to receive EPIC-HIV, and 29 communities to the arms without EPIC-HIV. Among all eligible men, we compared linkage to care (initiation or resumption of antiretroviral therapy after > 3 months of care interruption) at local clinics within 1 year of a home visit, ascertained from individual clinical records. Intention-to-treat analysis was performed using modified Poisson regression with adjustment for receiving another intervention (i.e., financial incentives) and clustering at the community level. We also conducted a satisfaction survey for EPIC-HIV 2. A total of 13,894 men were eligible (i.e., aged ≥ 15 years and resident in the 45 communities). The mean age was 34.6 (±16.8) years, and 65% were married or in an informal union. Overall, 20.7% received HBHCT, resulting in 122 HIV-positive and 6 discordant tests. Among these, 54 men linked to care within 1 year after HBHCT. Additionally, of the 13,765 eligible participants who did not receive HBHCT or received HIV-negative results, 301 men linked to care within 1 year. Overall, only 13 men received EPIC-HIV 2. The proportion of linkage to care did not differ between the arms randomized to EPIC-HIV and those without EPIC-HIV (adjusted risk ratio = 1.05; 95% CI:0.86-1.29). All 13 men who used EPIC-HIV 2 reported the app was acceptable, user-friendly, and useful for getting information on HIV testing and treatment. The reach was low, although the acceptability and usability of the app were very high among those who engaged with it. Enhanced digital support applications could form part of interventions to increase knowledge of HIV treatment among men. Clinical Trial Number: ClinicalTrials.gov # NCT03757104.

Website Design and Network Marketing in the Printing Service Industry

Websites become a necessity in helping users to first see the information and content of a product/service offered. Therefore, the development of a website to help small businesses/industries becomes a necessity to help customers get information related to the products/services offered. Marketing product websites has become a necessity today, given that technological advancements have led users to focus more on online purchases. Website design plays a crucial role in enhancing user experience and product marketability more effectively. The aim of this study is to find the optimal combination of design elements for website design according to different user perceptions, so that a reasonable optimization reference based on user perceptions can be provided. Through Kansei Engineering methods and neural network modeling, this study measures and predicts users' perceptions of various website design forms for product marketing. Overall, this study provides conceptual and methodological support for research on the emotional design of human-computer interaction interfaces, as well as supplements existing theories of website design for product marketing. The findings of this study can serve as feedback to website developers for product marketing and related design practitioners to help them improve website design.

A training and assessment system for human-computer interaction combining fNIRS and eye-tracking data

In the domain of engineering interactive product design, the optimization of Human-Computer Interaction (HCI) is central to enhancing user experience and system efficiency. Notably, the application of cognitive load theory holds significant prominence in this field. Efficient evaluation of cognitive load not only facilitates the optimization of resource management and task allocation, but is also the key to creating a fluent interactive experience. With the advancement of Artificial Intelligence (AI) and physiological measurement technologies, new methodologies have emerged to measure and understand cognitive load. This research introduces a system for HCI training and assessment. The system, through the development of digital memory paradigms, induces and verifies varying levels of cognitive load with precision, thereby providing data-driven insights for HCI design. Specifically, this study constructs an AI-assisted multimodal cognitive load assessment framework, integrating physiological data collected from Functional Near-Infrared Spectroscopy (fNIRS) and eye-tracking technologies. By extracting and analyzing 29 physiological features, including channel features, graph-theoretic features, and features pertaining to eyelid and iris movements, we propose an innovative multimodal recognition approach to classify cognitive load across individuals. The experimental results not only verified the validity of the method, but also revealed changes in physiological patterns at different levels of cognitive load. In particular, the significant potential of the fNIRS feature in band I. This discovery suggests that we can monitor and predict cognitive load with greater precision through physiological signals, hence optimizing the design of HCI systems to alleviate user strain, prevent information overload, and enhance the naturalness and intuitiveness of interaction.

Research on Information Fusion Method for Human Computer Interaction Interface of Command and Control System in MR Environment

In response to the challenge of high cognitive pressure and difficulty for commanders in cross-layer operations during the transition from traditional two-dimensional command and control system interfaces to mixed reality human-computer interaction interfaces, this paper conducts research on the information fusion display method for the human-computer interaction interface of the mixed reality command and control system. First, based on the findings of previous research, we developed a five-layer human-computer interaction interface for a mixed reality command and control system in typical combat scenarios. An evaluation experiment was designed and the resulting data analyzed. This analysis revealed that there are problems with low work efficiency and high cognitive load in the information acquisition process in cross-layer interaction operations. Consequently, we conducted research into the methods of integrating and displaying interactive interface information, integrating and displaying interactive and display interface information, and integrating and displaying complex situation interface information. The research findings indicate that: (1) The optimal display ratio for mixed reality 2D interface integration is 3:2. (2) The optimal display scheme for mixed reality 2D interface fusion is a combination of the main and secondary interfaces, with the main interface table displaying global information and the secondary interface displaying accurate information. (3) The optimal display scheme for mixed reality 2D and 3D interface fusion is the display of overall class information in 2D and the accurate identification of class information in 3D. The findings of this research can be applied to the design of human-computer interaction interfaces for mixed reality command and control systems in complex battlefield environments.

Digital interventions using mobile technologies for life skills development of learners with autism spectrum disorder: a scoping review

Providing essential information for digital health human-computer interaction designers, this scoping review identified the most utilized features of digital evidence-based interventions in prototyped mobile technologies for autism spectrum disorder (ASD), to lay the groundwork for designing user-focused mobile assistive technologies for learners with ASD. The review systematically categorized the features and capabilities of mobile applications that teach, support, and maintain life skills. Synthesizing and analysing 42 studies using thematic analysis, it was found that mobile applications hold the potential to support the uniqueness and varying needs of learners with ASD. This is achieved through personalization in the user interface and user experience, coupled with customizations on learning content. The scoping review also found that mobile applications can assist learners with ASD by breaking down tasks into smaller steps, incorporating pictures, illustrations, videos, sounds, pre-recorded instructions, and just-in-time prompting. In a broader context, mobile technologies can not only enhance life skills learning but also contribute to routine-building for individuals with ASD. This is possible if interventions taught in school are continued at home and integrated with daily home activities, aiming to enhance learning, daily practice, and maintenance of life skills. Future work focuses on making evidence-based interventions readily available and accessible to learners, parents, caregivers, and educators to support the overall well-being of learners with ASD.

Metal-organic framework based self-powered devices for human body energy harvesting.

The shift from traditional bulky electronics to smart wearable devices represents a crucial trend in technological advancement. In recent years, the focus has intensified on harnessing thermal and mechanical energy from human activities to power small wearable electronics. This vision has attracted considerable attention from researchers, with an emphasis on the development of suitable materials that can efficiently convert human body energy into usable electrical form. Metal-organic frameworks (MOFs), with their unique tunable structures, large surface areas, and high porosity, emerge as a promising material category for human body energy harvesting due to their ability to be precisely engineered at the molecular level, which allows for the optimization of their properties to suit specific energy harvesting needs. This article explores the progressive development of MOF materials, highlighting their potential in the realm of self-power devices for wearable applications. It first introduces the typical energy harvesting routes that are particularly suitable for harvesting human body energy, including thermoelectric, triboelectric, and piezoelectric techniques. Then, it delves into various research advances that have demonstrated the efficacy of MOFs in capturing and converting body-generated energy into electrical energy, emphasizing on the conceptual design, device fabrication, and applications in medical health monitoring, human-computer interaction, and motion monitoring. Furthermore, it discusses potential future directions for research in MOF-based self-powered devices and outlines perspectives that could drive breakthroughs in the efficiency and practicality of these devices.

노령층의 대리 감각(Sense of Agency) 메커니즘과 지능형 장치의 인간-컴퓨터 상호작용 디자인에 관한 연구 - 인지 신경과학 기반으로 -

노령층의 대리 감각(Sense of Agency) 메커니즘과 지능형 장치의 인간-컴퓨터 상호작용 디자인에 관한 연구 - 인지 신경과학 기반으로 -

EcoSketch: Promoting Sustainable Design through Iterative Environmental Assessment during Early-Stage Product Development

Sustainability has long been a topic of substantial interest to the design and human-centered computing communities. With industries increasingly prioritizing climate targets, there is a growing demand for sustainable product design. This article addresses this need through EcoSketch, a digital tool designed to democratize environmental impact assessments for product designers. Shifting typically retrospective evaluations to the early stages of product development, EcoSketch enables proactive consideration and adoption of sustainable alternatives. Unlike software tailored to environmental scientists, it minimizes the need for specialized training or extensive data inputs. We delve into the development and evaluation of EcoSketch, highlighting its unique features and usability strengths. This article concludes by discussing design implications and proposing future research avenues to strengthen the intersection of human-computer interaction and sustainable product design, with the aim of advancing progress on environmental challenges at the systems level.

Enhancing Perceived Value in Human-Computer Interaction: The Mediating Role of User Participation and the Moderating Role of Task Complexity

The study investigates the interplay of human-computer interaction (HCI) quality, user participation, and perceived value in technology interactions. The authors aim to elucidate the mediating role of user participation and the moderating effect of task complexity. Using structural equation modeling, we analyzed a diverse dataset of user feedback. The measurement and structural models are employed to determine the structural paths and predictive capability of the proposed model. Our findings indicate that user participation significantly mediates the impact of HCI quality on perceived value. Further, task complexity moderates these relationships, highlighting its role in enhancing or attenuating the perceived value of digital experiences. These findings provide insights for HCI design, emphasizing the need for user-centered systems that adapt to task complexity. Enhancing perceived value in technology interactions is essential, impacting user satisfaction, loyalty, and ultimately, the success of digital services.

Broadening the mind: how emerging neurotechnology is reshaping HCI and interactive system design

Abstract People are increasingly eager to know more about themselves through technology. To date, technology has primarily provided information on our physiology. Yet, with advances in wearable technology and artificial intelligence, the current advent of consumer neurotechnology will enable users to measure their cognitive activity. We see an opportunity for research in Human-Computer Interaction (HCI) in the development of these devices. Neurotechnology offers new insights into user experiences and facilitates the development of novel methods in HCI. Researchers will be able to create innovative interactive systems based on the ability to measure cognitive activity at scale in real-world settings. In this paper, we contribute a vision of how neurotechnology will transform HCI research and practice. We discuss how neurotechnology prompts a discussion about ethics, privacy, and trust. This trend highlights HCI’s crucial role in ensuring that neurotechnology is developed and utilised in ways that truly benefit people.

A closed-loop human-computer interactive design method based on sequential human intention prediction and knowledge recommendation

Designers working on large-scale complex engineering projects may encounter design errors due to limited experience and expertise. Therefore, during the design process, accurately identifying the designer’s design intention and recommending relevant design knowledge can enhance design efficiency and reduce errors. In this paper, we propose a closed-loop human–computer interactive design method based on sequential human intention prediction and knowledge recommendation. This method leverages the Function-Behaviour-Structure (FBS) model to reduce the dimensionality of design action sequences, so as to facilitate the analysis of potential design patterns. The processed action sequences are used to train Transformer to predict design intentions. In different input sequences, Transformer achieved a highest prediction accuracy of 92.09%. We construct a Design Knowledge Recommendation Framework (DKRF) and its corresponding design knowledge matching algorithm. This framework accurately recommends design knowledge to designers, solving design stagnation and improving design efficiency. Finally, a case study of four types of mechanical model design is conducted to demonstrate the feasibility and wide applicability of the proposed closed-loop human–computer interactive design method.

Who Made That Decision and Why? Users’ Perceptions of Human Versus AI Decision-Making and the Power of Explainable-AI

With the advent of artificial intelligence (AI) based systems, a new era has begun. Decisions that were once made by humans are now increasingly being made by these advanced systems, with the inevitable consequence of our growing reliance on AI in many aspects of our lives. At the same time, the opaque nature of AI-based systems and the possibility of unintentional or hidden discriminatory practices and biases raises profound questions not only about the mechanics of AI, but also about how users perceive the fairness of these systems. We hypothesize that providing various explanations for AI decision-making processes and output may enhance users’ fairness perceptions and make them trust the system and adopt its decisions. Hence, we devised an online between-subject experiment that explores users’ fairness and comprehension perceptions of AI systems with respect to the explanations provided by the system, employing a case study of a managerial decision in the human resources (HR) domain. We manipulated (i) the decision-maker (AI or human); (ii) the input (candidate characteristics); (iii) the output (recommendation valence), and (iv) the explanation style. We examined the effect of the various manipulations (and individuals’ demographic and personality characteristics) using multivariate ordinal regression. We also performed a multi-level analysis of experiment components to examine the effects of the decision-maker type, explanation style, and their combination. The results suggest three main conclusions. The first conclusion is that there is a gap in users’ fairness and comprehension perception of AI-based decision making systems compared to human decision making. The second conclusion is that knowing that an AI-based system provided the decisions negatively affects users’ fairness and comprehension perceptions, compared to knowing that humans made the decision. Finally, the third conclusion is that providing case-based, certification-based, or sensitivity-based explanations can narrow this gap and may even eliminate it. Additionally, we found that users’ fairness and comprehension perceptions are influenced by a variety of factors such as the input, output, and explanation provided by the system, as well as by individuals’ age, education, computer skills, and personality. Our findings may help to understand when and how to use explanations to improve users’ perceptions regarding AI-based decision-making. CCS CONCEPTS • Human computer interaction (HCI) → HCI design and evaluation methods → User studies • Human-centered computing → Human computer interaction (HCI) → Empirical studies in HCI • Applied computing → Law, social and behavioral sciences → Sociology

Research on human-computer interaction design of intelligent vehicle based on situational awareness

With the rapid development of automotive intelligent technology, the human-computer interaction design of intelligent vehicles has been paid more and more attention. Good human-computer interaction design can improve driving experience and enhance driving safety. However, some existing human-computer interaction designs of intelligent vehicles have some problems, such as lagging concept, lack of adaptability of interactive interface and insufficient response ability of intelligent system, which lead to poor human-computer interaction effect. In response to these problems, this paper proposes countermeasures for human-computer interaction design of intelligent vehicles with situational awareness as the core, including strengthening the position of situational awareness in the design concept, optimizing the situational adaptability of the human-computer interaction interface, and enhancing the coordination between the driver and the intelligent system, so as to improve the naturalness, efficiency and safety of human-computer interaction.

Research on HCI and Interface Design in VR: Understanding Virtual Perceptions

The Virtual reality (VR) affords a promising route for revolutionizing online shopping (OS) experiences, imparting human-computer interaction (HCI) and more desirable product expertise. By integrating the VR into OS platforms, users can delve deeper into product details, along with substance, shade, and different vital characteristics, thereby improving choice-making and fulfillment. This investigation specializes in knowledge of the synergy among VR and OS, utilizing structural equation modeling (SEM) to evaluate user interaction (UI) and experience (UE). Through UI achievement surveys and professional panels, insights into VR-integrated OS are collective, informing the improvement of design standards. The research focused on generating evidence-primarily based layout requirements, validating their feasibility, and assessing their impact. By emphasizing sizeable design additives, VR creators can enhance consumer enjoyment and pleasure, ultimately contributing to greater effective OS interfaces. This research presents sensible pointers for VR improvement but underscores the potential for VR to elevate overall user satisfaction in online retail environments.

Virtual reality painting: A structured review of a decade of innovation

<p>Various studies have widely utilized virtual reality (VR) in painting creation. However, there has been few conclusive studies on VR painting. The present systematic literature review describes the current state of knowledge and practices using VR in painting and provides guidance for painters and scholars focusing on human computer interaction design by critically appraising and summarizing the existing research. It also outlines a wide range of results yielded by quantitative, qualitative and mixed-method studies to investigate potential benefits, difficulties, and effectiveness of VR painting. Overall, 44 studies published from 2013 until 2023 were analysed, with 16 articles introduced the hardware technology and interaction mode of VR painting, 17 articles introduced the beautification method of VR painting, and 11 articles focused on the application of VR painting in different fields. Implications for practice and research are discussed in detail, as the hardware selection, interaction, and physical simulation in VR painting scenes.<strong></strong></p>