Human–Computer Interaction

Human-computer interaction for virtual-real fusion

Determination of the effectiveness of two methods for usability evaluation using a CPOE medication ordering system

Introduction Usability evaluation is essential to make sure that software products newly released are easy to use, efficient, and effective to reach goals, and satisfactory to users. For example, when a software company wants to develop and sell a new product, the company needs to evaluate usability of the new product before launching it at a market to avoid the possibility that the new product may contain usability problems, which span from cosmetic problems to severe functional problems. Three widely used methods for usability evaluation are Think Aloud (TA), Heuristic Evaluation (HE) and Cognitive Walkthrough (CW). TA method is commonly employed with a lab-based user testing, while there are variants of TA methods, including thinking out aloud at user's workplace instead of at labs. What we discuss here is the TA method that is combined with a lab-based user testing, in which test users use products while simultaneously and continuously thinking out aloud, and experimenters record users' behaviors and verbal protocols in the laboratory. HE is a usability inspection method, in which a small number of evaluators find usability problems in a user interface design by examining an interface and judging its compliance with well-known usability principles, called heuristics. CW is a theory-based method, in which evaluators evaluate every step necessary to perform a scenario-based task, and look for usability problems that would interfere with learning by exploration. These three methods have their own advantages and disadvantages. For instance, TA method provides good qualitative data from a small number of test users, but laboratory environment may influence test user's behaviors. HE is a cheap, fast and easy-to-use method, while it often finds too specific and low-priority usability problems, including even not real problems. CW helps find mismatches between users' and designers' conceptualization of a task, but it needs extensive knowledge of cognitive psychology and technical details to apply. However, even though these advantages and disadvantages show overall characteristics of three major usability evaluation methods, we cannot compare them quantitatively and see their efficiency clearly. Because one of reasons why so-called discounted methods, such as HE and CW, were developed is to save costs of usability evaluation, cost-related criteria for comparing usability evaluation are meaningful to usability practitioners as well as usability researchers. One of the most disputable issues related to cost of usability evaluation is sample size. That is, how many users or evaluators are needed to achieve a targeted usability evaluation performance, for example, 80% of overall discovery rate? The sample size of usability evaluation is known to depend on an estimate of problem discovery rate across participants. The overall discovery rate is a common quantitative measure that is used to show the effectiveness of a specific usability evaluation method in most of usability evaluation studies. It is also called overall detection rate or thoroughness measure, which is the ratio of 'the sum of unique usability problems detected by all experiment participants' against 'the number of usability problems that exist in the evaluated systems', ranging between 0 and 1. The overall discovery rates were reported more than any other criterion measure in the usability evaluation experiments and also a key component for projecting required sample size for usability evaluation study. Thus, how many test users or evaluators participate in the usability evaluation is a critical issue, considering its cost-effectiveness.

Background: Unprecedented growth in artificial intelligence (AI) is changing the way people interact with computers- the concept known as human-computer interaction (HCI), several AI approaches like machine learning or natural language processing have boosted the user experience (UX). Nevertheless, there are some ethical concerns and differences in users’ awareness of AI systems that make it difficult to consider the general advantages of AI in HCI. Objective: Therefore, this research seeks to explore the impact of enhanced AI techniques on the level of UX in HCI with a focus on the facet of user satisfaction, trust, and usefulness of AI-based personalization techniques. It also solves the ethical problem associated with the AI systems. Methods: A quantitative research method was used with the use of structured questionnaires with 250 participants. It also captured the user engagement with AI systems, as well as their satisfaction and trust levels as well as their concerns relating to AI ethics. Descriptive analysis was used to examine the collected data through statistical tests such as; the Shapiro-Wilk test for normality and Cronbach’s Alpha for reliability. Results: It is seen that most of the users are happy with the AI technologies integrated into their lives, with special emphasis on the aspect of personalization. However, the data was nonparametric (Nomography = 0 05) and so was the internal consistency (Cronbach alpha -0. 033 on the Likert scale items were needed for survey tools of higher specific focus. The results of the survey showed that ethical considerations running across the respondents as the most significant factor of discrepancy between satisfaction and trust in areas of tension. Conclusion: In the context of AI methodologies, most are perceived to have positive effects in enhancing the UX regarding individualized engagements. However, ethical issues should be met and addressed while determining the reliability of the tools used in ascertaining the impact of AI. There is a need for future studies to address these issues and improve the techniques used for evaluating AI’s diverse position in HCI.

The development of computer technology has materialized into the term of human computer interaction. Research studies in human computer interaction studied young age groups that are educated and technically inclined. This paper will study the mental model in Human Computer Interaction that could be useful in some area. There are a number of types of this review paper on the Internet. One will lay out the current approach, result, trends, in the human computer interaction and the other approach will look into research construction that have been around long before but are now forgotten. In this paper, the author is also looking at the landscape in the emotional intelligence of a user so that they are more user like, fidelity prototyping. The development and design of an automated system that performs this task is still being undertaken. The notion of human-computer interaction is conceived as a result of the evolution of the development criteria of computer technology. The next generation of individuals (those at a younger age cohort) in which is termed an educated, technical, or at least knowledgeable individual will also be the focus of individual (tech) research experiments in human-computer interaction. Human-computer interaction (HCI) refers to both technical and human behaving aspects. The central tenets of practical studies in human-computer interaction are to reveal implicit knowledge about human behaviour and human interaction with technology. Resilience is set of behaving contextually taken collectively reacts to challenges. The term resilience has been applied to virtually everything from the economy, real estate, sports, psychology, business, and education, and the list could and can go on. Resilience is mainly comprised or made of a number of different abilities and skills as a way to be able to relate effectively to others, build relationships, self-efficacy, optimism, and self-awareness, creating meaning from other experiences form these are essential combination of competencies. In this manner people should use this in order to optimize the value of the resilience of an organization. This becomes important resourcefulness knowledge of the help available to increasing capacity to deal with the challenges facing change, overcoming (wasting time) relative to everything that could be done alternatively.

As a common human behavior, interaction is everywhere in human life. With the rise of big data and human–computer interaction in the 21st century, more and more researchers from different industries and disciplines pay great attention to the human interactive behavior research. From the perspective of computer science, scholars try to use computer technology to make research more meaningful. To the best of our knowledge, this paper is the first study to investigate the potential rules of human interactive behavior in the view of computer science, based on 16 top-tier journals of human interactive behavior from Microsoft Academic Graph dataset. We put forward a topic extraction and clustering model based on word2vec to infer key topics, which can be widely used in different fields of research. We find that the growth of human interactive behavior is in an uptrend on the whole. Besides, the cooperative relationship between authors and countries/regions is closer over time. We also make the mensurable evolution analysis of topics by a statistical method. Some topics are hot all the time, while some are unpopular as time goes by. Finally, we do rankings in the field of human behavior research from a new perspective. All these findings help researchers observe potential patterns and the topic evolution in half a century, which may shed dazzling light on the exploration of human interactive behavior.

Artificial intelligence (AI) and Human Computer Interaction (HCI) share common roots and early work on conversational agents has laid the foundation for both fields. However, in subsequent decades the initial tight connection between the fields has become less pronounced. The recent rise of deep learning has revolutionized AI and has led to a raft of practical methods and tools that significantly impact areas outside of core-AI. In particular, modern AI techniques now power new ways for machines and humans to interact. Thus it is timely to investigate how modern AI can propel HCI research in new ways and how HCI research can help direct AI developments. This workshop offers a forum for researchers to discuss new opportunities that lie in bringing modern AI methods into HCI research, identifying important problems to investigate, showcasing computational and scientific methods that can be applied, and sharing datasets and tools that are already available or proposing those that should be further developed. The topics we are interested in including deep learning methods for understanding and modeling human behaviors and enabling new interaction modalities, hybrid intelligence that combine human and machine intelligence to solve difficult tasks, and tools and methods for interaction data curation and large-scale data-driven design. At the core of these topics, we want to start the conversation on how data-driven and data-centric approaches of modern AI can impact HCI.

Cognitive Walkthrough: An Element in System Development and Evaluation – Experiences From The eWALL Telehealth System

Pokémon Go, go, go, gone?

This issue is the first of ISJ's 25th volume and, as is customary on such anniversaries, this editorial reflects on the life of the ISJ and on its future. The editors-in-chiefs have periodically analysed the nature of the papers that ISJ publishes, most recently in the valedictory editorial by David Avison and Guy Fitzgerald in volume 22 (Avison & Fitzgerald, 2012). This looked at papers in the first 21 volumes. Here we update this analysis by reflecting on the content in the last three volumes and consider how we need to develop the journal further. The last three volumes, 22-24, comprised 64 papers. ISJ has moved from four issues a year to six and the number of papers published in each volume has increased from around 17.3 to 21.3. The move to six issues a year was occasioned by increases in good quality submissions and a desire to ensure that the time between acceptance and publication was shorter. In these 64 papers, we see many of the publication trends identified in earlier papers exacerbated. The 64 papers were produced by 177 authors (some authors produced more than one paper, of course) with an average of 2.77 authors per paper. Remarkably, only 4 single authored papers were published (6.25%) cementing a decreasing trend of 32.4% single authored papers in volumes 1-17 and 13.6% in volumes 18-21. Papers with four authors are now quite common, and some have five or six. While single authored papers are often preferred by tenure and promotions committees, the trend in IS, or at least in the ISJ, is for multi-authored papers. ISJ is continuing to becoming more global, with the authors of papers more geographically diverse. Avison and Fitzgerald identified the reduction in UK papers in the ISJ, the plateauing of contributions from North America and the increases from Asia-Pacific. UK contributions in the most recent volumes have fallen from 16% to 14.2%. North American contributions drop from 40.8% to 36.7%, while Asia-Pacific authors now comprise 20.3% of the total (up from 18.9%) and Europe (excluding the UK) hosts 28.2% of contributors (up from 22.3%). Taken together, the increase in author numbers and their greater geographical spread means that more papers are co-authored by people from different geographical regions. Indeed, within regions there is a sense that co-authors work in different countries, and even within countries, co-authors are seldom co-located in the same institution – and where they are, they are often not in the same academic unit. Given the global nature of IS, and the multiple perspectives upon it that need to be brought to bear, this diversity appears healthy. In a different aspect of diversity, the gender balance of authors continues to become more even. 35% of the most recent paper authors are female, compared to 14.4% in volumes 1-6, 19.8% in volumes 13-17 and 28% in the volumes 18-21. As in the earlier analyses, virtually all the most recent authors are academics. Fewer than 4% of papers are co-authored by practitioners, down from 7.1% in the last analysis. While, we are improving diversity in many respects, we are not attracting (or publishing) papers by practitioners. If this reflects less research, or poorer research, being undertaken by or with practitioner co-authors then this is a concern for a discipline such as IS. The trend over time has been for more ISJ authors to be located in business and management schools or departments. Most recently, this percentage has increased again – from 43.3% to 46.3%. The percentage in IS departments has dropped again to 31.6%, while that from computer science has increased from a very low base to just over 5% and the number from ‘other’ has nearly doubled to 13%. However, caution needs to be exercised over the home academic units as the data is taken from the addresses of authors. Some authors may be in a department, within a school that is part of a faculty that is itself an element of a college which is a unit of an institution. Thus, someone may be in an IS department that is within a business school. The address they choose to use may refer to either (or both) – but perhaps reflects their preference for the unit they would most like to be associated with. Likewise, there is a diversity of titles for units that make classification problematic – departments of Tourism Information, Health Administration, Social Informatics, and Business Education for example. So, what does the ‘typical’ authorship of an ISJ paper look like? There will be multiple authors, of both genders who are unlikely to work in the same institution or even in the same country and whose departmental home disciplines are different. What these co-authors are researching remains diverse too. While ISJ has been known as an interpretivist journal around a third of papers are positivist. About half the papers are qualitative. In terms of the 10 questions and challenges that Avison & Fitzgerald (2012) posed in their final editorial, the ISJ has, recently, published more papers from under-represented regions, it has improved the gender balance of authors, and it has remained generalist in its outlook. We have published more papers, more issues and more special issues. We can continue to do better in these aspects, and other challenges remain. We would like to be more risk taking, we have plans for additional sections to reflect different aspects of IS and of research, and the journal needs to reflect the need that the IS community has identified for the impact of its research to be better understood and better communicated. We will take up these issues throughout this year. When we assumed the role of editors-in-chief in July 2012, we introduced a new era for the Information Systems Journal as management of the journal shifted to us from the founding editors. In our inaugural editorial we indicated our desire to retain the commitment to publishing high-quality research that is consistent with the founding mission of the journal. That is, the motivation for founding ISJ was a desire to publish work that pushed out the boundaries of acceptable methodology for conducting information systems research. Since the methodological norm in 1991 was quantitative and positivist research, ISJ was established to be a publication outlet for qualitative interpretive and later critical research. Going forward into the next 25 years we are committed to the founding principles of ISJ, as we interpret them today. First, we will continue to publish high quality research that is informed by the interpretive tradition. This means that while positivist and quantitative research is welcome, and indeed, has been published in significant quantity, the production of numbers and the testing of hypotheses are not sufficient. The paper must also ‘tell a good story’ with the literature review, the analysis of data, and the discussion. Second, we will continue to publish work that pushes out the boundaries of acceptable information systems research. But in 2015 this is less about methodology than it is about content. Therefore, while we continue to welcome and publish research about information system development and management, we also welcome and want to publish research on emerging topics of relevance to the information systems field. This would include, for example: papers about societal/environmental influences on information systems; research about information systems professionals; and the consequences of information systems – intended and unintended, positive and negative. We also encourage research on the effect of new technologies – such as 3D printing, ubiquitous computing, wearable computing, and the Internet of things – on information systems development and practice. Finally, we welcome research that will explore a future in which information systems are an ever more important component. For instance, information systems are increasingly embedded in organisations, in societies and even in people. However, these embedded systems and their implications for privacy, convenience or control are rarely subjected to in-depth investigations. Throughout 2015, as part of the ISJ 25th anniversary, we will publish, in addition to our regular papers and special issues, a set of invited papers. We invited all those who published in our very first volume, and some others, to consider submitting a paper for volume 25. Many accepted our invitation. It is remarkable how many of the people who published in ISJ 1(1-4) are still active in the field. This may reflect the youth of the discipline, the youth of those working in it 25 years ago, that ISJ attracted submissions from very young academics, or that continual reductions in pension entitlements globally have meant many people have continued working for longer than they had anticipated. The first paper in this issue is by Lee, Thomas and Baskerville who argue for the restoration of the IS artifact in design science. They do so by unpacking the concept of the IT artifact. In doing so they remind us that what is being designed is not the IT artifact, but the IS artifact, which consists of three artifacts: the technology artifact (the hardware and software), the information artifact (the message) and the social artifact (human actions). They illustrate the primacy of the IS artifact in design science with material from three cases. The result is to restore the idea that the study of design in information systems needs to consider the design of the entire IS artifact, not just the technology artifact. This result encourages an expansion in the use of design science research methodology to study a broader set of artifacts. Three cases serve as a reminder that the component parts of the IS artifact - the information artifact, the technology artifact, and the social artifact - all interact and combine into a whole that is greater than the sum of its parts. In the second paper, Myers and Baskerville contribute new methodological insights in their argument for active engagement by IS researchers during the conduct of ethnographic work. While most ethnographic research in information systems has been based on the traditional anthropological model in which the ethnographer observes but does not actively seek to change the situation, they argue for the incorporation of action research perspectives into ethnographic research. They do so by describing two existing types of research that relate ethnography and design. The first type is ethnography for design, in which the results of the ethnographic study of some context informs the subsequent design process. The second type is ethnography to study design. Here, the ethnographer is studying those who are engaged in the design activity. They contrast these with the third type, design ethnography, in which the ethnographer is actively engaged in the activity of design, production or introduction of artifacts. Hence, the researcher is intervening in changing the context being researched. The researcher is actively engaged with others in a future-oriented way: designing, creating, innovating, and improvising artifacts that may affect the cultural and social values under study. Hence, rather than being a passive observer of changes in human behavior or organizations and human society more generally, the design ethnographer becomes an active participant in shaping these changes. In the third paper of this issue, Alter passionately engages with the concept of the “IT artifact” and trenchantly argues that it is no longer useful, it has outlived its usefulness. IT artifacts are ubiquitous in research, yet there is a huge amount of inconsistency in the way the term is used, with the result that it no longer means anything in particular at all. Alter traces the way the definition of IT artifact has morphed since it was first used by Orlikowski & Iacono (2001) to definitions in 2013 that are so loose and all-encompassing as to be essentially meaningless. Alter also suggests that cousins of the IT artifact, notably the IS artifact, the sociotechnical artifact, the social artifact and the technical artifact, simply confuse the issue further, each overlaps to some extent with other artifacts. In order to fill the yawning void that will be created when the IT artifact is retired, Alter proposes three ways forward, the most ambitious of which develops Alter's own notion of IT-enabled work systems (Alter, 2013). As Alter notes in his conclusion, retiring the IT artifact will help to enhance the rigour of our research because we will not use terminology that is meaningless. Indeed, we will also focus on more useful research that is meaningful.

Situation is a computational abstraction that encapsulates human-centric contexts, which can be human-oriented behavioral contexts as well as the relevant environmental contexts. Its potential applications in computer science range from artificial intelligence, service computing, human-computer interaction, pervasive computing to software engineering and software systems. In this paper, we introduce a user-centric situation-aware Intelligent Online Learning Platform (IOLP) that aims to provide a personalized learning experience for those conducting online-learning activities. It joins the engineering techniques from Machine Learning, Intelligent Systems (IS) and Human-Computing Interaction (HCI) to offer highly personalized services to the users. It serves as an experimental subject for, as well as a real-world implementation of situations. Under the strength of the abstraction of situation, various elements drawn from different areas in computer science are seamlessly integrated towards a user-centric Intelligent Online Learning Platform (IOLP). Our discussions on the IOLP are further supported by an online class instance it currently hosts featuring Python Programming.

Empathy plays an important role in social interactions, such an effective teaching-learning process in a teacher-student relationship, and company-client or employee-customer relationship to retain potential clients and provide them with greater satisfaction. Increasingly, people are using technology to support their interactions, especially when the interlocutors are geographically distant from one another. This has a negative impact on the empathic capacity of individuals. In the Computer Science, there are different approaches, techniques and mechanisms to promote empathy in social or human-computer interactions. Therefore, this article presents a systematic mapping to identify and systematize the approaches, techniques and mechanisms used in computing to promote empathy. As a result, we have identified existing approaches (e.g. collaborative learning environment, virtual and robotics agents, and collaborative/affective games) to promote empathy, the main areas involved (e.g. human-computer interaction, artificial intelligence, robotics, and collaborative systems), the top researchers and their affiliations who are potential contributors to future research and, finally, the growth status of this line of research.

Evaluation, Description and Invention: Paradigms for Human-Computer Interaction

In recent years, gesture recognition and speech recognition, as important input methods in Human–Computer Interaction (HCI), have been widely used in the field of virtual reality. In particular, with the rapid development of deep learning, artificial intelligence, and other computer technologies, gesture recognition and speech recognition have achieved breakthrough research progress. The search platform used in this work is mainly the Google Academic and literature database Web of Science. According to the keywords related to HCI and deep learning, such as “intelligent HCI”, “speech recognition”, “gesture recognition”, and “natural language processing”, nearly 1000 studies were selected. Then, nearly 500 studies of research methods were selected and 100 studies were finally selected as the research content of this work after five years (2019–2022) of year screening. First, the current situation of the HCI intelligent system is analyzed, the realization of gesture interaction and voice interaction in HCI is summarized, and the advantages brought by deep learning are selected for research. Then, the core concepts of gesture interaction are introduced and the progress of gesture recognition and speech recognition interaction is analyzed. Furthermore, the representative applications of gesture recognition and speech recognition interaction are described. Finally, the current HCI in the direction of natural language processing is investigated. The results show that the combination of intelligent HCI and deep learning is deeply applied in gesture recognition, speech recognition, emotion recognition, and intelligent robot direction. A wide variety of recognition methods were proposed in related research fields and verified by experiments. Compared with interactive methods without deep learning, high recognition accuracy was achieved. In Human–Machine Interfaces (HMIs) with voice support, context plays an important role in improving user interfaces. Whether it is voice search, mobile communication, or children’s speech recognition, HCI combined with deep learning can maintain better robustness. The combination of convolutional neural networks and long short-term memory networks can greatly improve the accuracy and precision of action recognition. Therefore, in the future, the application field of HCI will involve more industries and greater prospects are expected.

Aim: This paper proposes a novel paradigm of Artificial Intelligence (AI) grounded in the epistemological process of converting tacit knowledge into explicit knowledge. Drawing on the foundational philosophies of science, particularly the works of Popper, Kuhn, Lakatos, and Gospodarek, the study conceptualizes AI not merely as a computational tool but as a systemic method for epistemic transformation. The paradigm is structured as a Lakatosian Research Programme, with a clearly defined hard core asserting that AI enables the symbolic representation of internalized, experiential knowledge. Surrounding this core is a protective belt of auxiliary hypotheses derived from general systems theory, cybernetics, machine learning, and symbolic processing. The programme's heuristics guide theoretical and technological advancements while preserving its epistemological foundation. By formalizing the tacit-to-explicit knowledge conversion, this paradigm repositions AI as a critical instrument for knowledge creation, management, and application in digital and socio-technical systems. This allows one to build measures and values of generative and language models, which is important from an economic point of view. This research tries to clarify the framework of use AI models for converting tacit knowledge inside a learning data of neural network systems to explicit information requested by the asking. It is important for economic evaluation of AI systems where accuracy considered utility as a criterion. Design / Research methods: Research programme in Lakatos’ sense and multidisciplinary heuristic related to the theory of systems. Conclusions / findings: Artificial Intelligence should be understood not only as a technological artefact but as a systemic method for transforming tacit knowledge into explicit knowledge. The proposed AI paradigm adheres to the structure of a Kuhnian paradigm and a Lakatosian research programme. Its hard core is defined by the thesis that AI operationalizes the conversion of experiential, intuitive, or unconscious knowledge into symbolic, formalized, and actionable representations. Lakatosian protective belt as a dynamic epistemic layer. This AI paradigm offers a progressive problem-shift capacity by enabling novel ways of organizing, analyzing, and applying knowledge in digital and socio-technical environments. It also provides a coherent framework for developing AI systems that are more aligned with human cognitive and organizational processes. Originality / value of the article: This paper introduces: new concepts of usefulness of AI systems, new definition of AI systems based on conversion of the knowledge, original conversion paradigm and research program in Lakatos sense. It is original conceptional heuristic based on philosophy of science in relation to economic usefulness of view AI systems. JEL: C67, C18.