Human-computer Interaction Factors Research Articles

An explanatory study of factors influencing engagement in AI education at the K-12 Level: an extension of the classic TAM model

Artificial intelligence (AI) holds immense promise for K-12 education, yet understanding the factors influencing students’ engagement with AI courses remains a challenge. This study addresses this gap by extending the technology acceptance model (TAM) to incorporate cognitive factors such as AI intrinsic motivation (AIIM), AI readiness (AIRD), AI confidence (AICF), and AI anxiety (AIAX), alongside human–computer interaction (HCI) elements like user interface (UI), content (C), and learner-interface interactivity (LINT) in the context of using generative AI (GenAI) tools. By including these factors, an expanded model is presented to capture the complexity of student engagement with AI education. To validate the model, 210 Chinese students spanning grades K7 to K9 participated in a 1 month artificial intelligence course. Survey data and structural equation modeling reveal significant relationships between cognitive and HCI factors and perceived usefulness (PU) and ease of use (PEOU). Specifically, AIIM, AIRD, AICF, UI, C, and LINT positively influence PU and PEOU, while AIAX negatively affects both. Furthermore, PU and PEOU significantly predict students’ attitudes toward AI curriculum learning. These findings underscore the importance of considering cognitive and HCI factors in the design and implementation of AI education initiatives. By providing a theoretical foundation and practical insights, this study informs curriculum development and aids educational institutions and businesses in evaluating and optimizing AI4K12 curriculum design and implementation strategies.

Exploring non-linear relationships between perceived interactivity or interface design and acceptance of collaborative web-based learning.

The online version contains supplementary material available at 10.1007/s10639-023-11635-6.

Human–Computer Interaction (HCI) and Trust Factors for the Continuance Intention of Mobile Payment Services

The human–computer interaction (HCI) for mobile applications is becoming increasingly important as mobile applications have become an integral part of everyday life. Mobile app developers need to continuously improve the users’ experience to fulfill their conditions, which in turn, can trigger the purchase decision. Trust is another important factor, driving users to adopt mobile apps and use them to make online purchases. However, mobile app developers constantly face the dilemma between user trust and HCI experiences. This study’s primary purpose is to investigate the HCI and trust factors for the continued use of mobile payment apps and their services. Based on the human–computer interaction, task–technology fit, and IS success theories, an integrative framework is proposed to examine the relationships between continuance intention and its antecedents. An online survey was conducted to collect data from 544 mobile users. The results of the analyses indicate that trust plays a more significant role than HCI in increasing the users’ continuance usage of mobile payment services. As for improving HCI experiences for users, this study shows that system quality plays the most important role, followed by the skill of using mobile payment services and the perceived task–technology fit (TTF) for using the services. Information quality does not significantly impact the enhancement of positive HCI experiences. The results provide insights for the developers of mobile payment services to improve the user experience.

Students’ Web-Based Activities Moderate the Effect of Human-Computer-Interaction Factors on Their E-Learning Acceptance and Success During COVID-19 Pandemic

The purpose of this study is to investigate if students' activities on systems web-based moderate the effect of human-computer interaction (HCI) factors on ease of use and usefulness of e-learning and their success (SS) at the time of the COVID-19 pandemic. To answer the questions that addressed the relationship between HCI, e-learning acceptance, and SS, the researcher surveyed 103 students from Kadir Has University whose grade and activity logs were accessible. The survey was related to their perceived course webpage design, system and content quality, interactivity, usability and functionality, and self-assessment. The results show that most of the hypotheses of this study have been proven, a comprehensive conceptual model was developed, and the student grades in the online courses improved their GPA. The findings further reveal that students' activities moderate the effects of course environment and content quality on perceived usefulness and the effect of the course evaluation' system on perceived ease of use, where the changes in R 2 ranged between 0.041 and 0.074. That means including logs as a moderator would increase the explanatory power of the effect of HCI factors on e-learning acceptance which together explained 54.9% of the variance in perceived success (SS), where U is the strongest determinant of SS.

An HCI-Based Cognitive Architecture for Learning Process Observation

In this article, a cognitive framework for observing learning activities based on human-computer coupling is proposed. The observation is based on the vectorization of a learning situation along with human-computer interaction factors. An evolutionary high-dimensional topology of learning cognitive flow is introduced for human-computer interaction. In addition, the authors have selected a tree topology as the topological structure of a low-dimensional learning space to process the observations for online learning. Furthermore, the mechanism for the BSM (brain cognitive body-situation of coupling-manifold of information) the coupling morphism is presented. The principle for the coupled observation of objects in a cognitive or learning manifold is proposed. Finally, a special system for teaching and learning is programmed to observe and evaluate learning and mental arithmetic training processes. This system not only provides students with a new ergonomic learning model but also records the students' learning processes. Thus, the teachers can summarize the knowledge points automatically rather than manually.

EEG neurofeedback research: A fertile ground for psychiatry?

The clinical efficacy of neurofeedback is still a matter of debate. This paper analyzes the factors that should be taken into account in a transdisciplinary approach to evaluate the use of EEG NFB as a therapeutic tool in psychiatry. Neurofeedback is a neurocognitive therapy based on human–computer interaction that enables subjects to train voluntarily and modify functional biomarkers that are related to a defined mental disorder. We investigate three kinds of factors related to this definition of neurofeedback. We focus this article on EEG NFB. The first part of the paper investigates neurophysiological factors underlying the brain mechanisms driving NFB training and learning to modify a functional biomarker voluntarily. Two kinds of neuroplasticity involved in neurofeedback are analyzed: Hebbian neuroplasticity, i.e. long-term modification of neural membrane excitability and/or synaptic potentiation, and homeostatic neuroplasticity, i.e. homeostasis attempts to stabilize network activity. The second part investigates psychophysiological factors related to the targeted biomarker. It is demonstrated that neurofeedback involves clearly defining which kind of relationship between EEG biomarkers and clinical dimensions (symptoms or cognitive processes) is to be targeted. A nomenclature of accurate EEG biomarkers is proposed in the form of a short EEG encyclopedia (EEGcopia). The third part investigates human–computer interaction factors for optimizing NFB training and learning during the closed loop interaction. A model is proposed to summarize the different features that should be controlled to optimize learning. The need for accurate and reliable metrics of training and learning in line with human–computer interaction is also emphasized, including targeted biomarkers and neuroplasticity. All these factors related to neurofeedback show that it can be considered as a fertile ground for innovative research in psychiatry.

A Review of Pupillometry for Human-computer Interaction Studies

The most commonly used methods in human-computer interaction (HCI) are based on behavioral data, interviews and questionnaires. While these practices are able to provide reliable results, they are influenced by subjective factors arising both from the experimenter’s and the user’s side. Many of these methods also have poor temporal resolution, limiting their range of use. Approaches based on physiological responses should not have these inherent shortcomings. Although there exist many methods based on different signals (EEG, ECG, EDA), pupillometry has its distinct advantages. The basic set of devices needed is cheap compared to most other methods and it is also the least intrusive option. It is well established that cognitive and affective processes can be monitored by recording changes in pupil dilation. While emotions are an important aspect of HCI and overall user experience, changes in mental effort levels are easier to interpret in case of software and webpage evaluation or interactions with automated systems. As a result, the present paper will focus on the measurement of mental effort, only mentioning other possible uses briefly. The goal of this review is to provide a brief insight on the issues that might arise during HCI studies and factors that should be controlled to gather valid data for mental effort calculations.

CKD Express © - A New IT-Software Proposed for a Paradigm Change in CKD Care

CKD Express © - A New IT-Software Proposed for a Paradigm Change in CKD Care

Teaching without the teacher? Building a learning environment through computer simulations

This article analyzes the impact of a computer simulation (business game) on the users’ perceived learning. The theoretical model developed in this paper is derived from collaborative learning and human–computer interaction assumptions. The hypotheses relating to group dynamics and the user–computer interface design with the users’ perceived learning are tested using the business game “FirmReality” on a sample of 173 respondents. Multiple regression and qualitative results show that, in a computer-based simulation context, perceived learning is influenced more by human–computer interaction factors than by group dynamics. Furthermore, results point out a negative effect of interaction with the instructor on individual perceived learning. These results support the possibility to automate the interaction between teacher and students.

Role of Human-Computer Interaction Factors as Moderators of Occupational Stress andWork Exhaustion

Software professionals perform boundary-spanning activities, and thus need strong interpersonal, technical, and organizational knowledge to be professionally competent. They have to perform in a demanding work environment characterized by strict deadlines, differing time zones, interdependency in teams, increased interaction with clients, and extended work hours. These characteristics lead to occupational stress and work exhaustion. Yet, the impact of stress is felt in different ways by different people, even if they perform the same functions. These differences in the perception of stress can be caused by varying confidence in their technical capabilities. People possess varying technical capabilities, based on their acquisition of technical skills, comfort level in using the technology, and intrinsic motivation. These attributes represent the human-computer interaction (HCI) personality of software professionals. This article examines whether these HCI factors moderate the relationship between occupational stress and work exhaustion. Data were collected from software professionals located in Chennai and Bangalore in India. The data revealed that HCI factors had a main effect but no significant moderating effects on work exhaustion. The control over the technology variable emerged as the key variable among the HCI factors that affected software professionals' ability to cope with stress and work exhaustion.