To equip computers with human communication skills and to enable natural interaction between the computer and a human, intelligent solutions are required based on artificial intelligence (AI) methods, algorithms, and sensor technology. This study aimed at identifying and analyzing the state-of-the-art AI methods and algorithms and sensors technology in existing human–computer intelligent interaction (HCII) research to explore trends in HCII research, categorize existing evidence, and identify potential directions for future research. We conduct a systematic mapping study of the HCII body of research. Four hundred fifty-four studies published in various journals and conferences between 2010 and 2021 were identified and analyzed. Studies in the HCII and IUI fields have primarily been focused on intelligent recognition of emotion, gestures, and facial expressions using sensors technology, such as the camera, EEG, Kinect, wearable sensors, eye tracker, gyroscope, and others. Researchers most often apply deep-learning and instance-based AI methods and algorithms. The support sector machine (SVM) is the most widely used algorithm for various kinds of recognition, primarily an emotion, facial expression, and gesture. The convolutional neural network (CNN) is the often-used deep-learning algorithm for emotion recognition, facial recognition, and gesture recognition solutions.

Intelligent user interfaces (IUI) are driven by the goal of improvement in human–computer interaction (HCI), mainly improving user interfaces’ user experience (UX) or usability with the help of artificial intelligence. The main goal of this study is to find, assess, and synthesize existing state-of-the-art work in the field of IUI with an additional focus on the evaluation of IUI. This study analyzed 211 studies published in the field between 2012 and 2022. Studies are most frequently tied to HCI and SE domains. Definitions of IUI were observed, showing that adaptation, representation, and intelligence are key characteristics associated with IUIs, whereas adaptation, reasoning, and representation are the most commonly used verbs in their description. Evaluation of IUI is mainly conducted with experiments and questionnaires, though usability and UX are not considered together in evaluations. Most evaluations (81% of studies) reported partial or complete improvement in usability or UX. A shortage of evaluation tools, methods, and metrics, tailored for IUI, is noticed. Most often, empirical data collection methods and data sources in IUI evaluation studies are experiment, prototype development, and questionnaire.

This study aimed to observe the impact of eight explainable AI (XAI) explanation techniques on user trust and satisfaction in the context of XAI-enhanced learning analytics while comparing two groups of STEM college students based on their Bologna study level, using various established feature relevance techniques, certainty, and comparison explanations. Overall, the students reported the highest trust in local feature explanation in the form of a bar graph. Additionally, master’s students presented with global feature explanations also reported high trust in this form of explanation. The highest measured explanation satisfaction was observed with the local feature explanation technique in the group of bachelor’s and master’s students, with master’s students additionally expressing high satisfaction with the global feature importance explanation. A detailed overview shows that the two observed groups of students displayed consensus in favored explanation techniques when evaluating trust and explanation satisfaction. Certainty explanation techniques were perceived with lower trust and satisfaction than were local feature relevance explanation techniques. The correlation between itemized results was documented and measured with the Trust in Automation questionnaire and Explanation Satisfaction Scale questionnaire. Master’s-level students self-reported an overall higher understanding of the explanations and higher overall satisfaction with explanations and perceived the explanations as less harmful.

Advancements in protocols, computing paradigms, and electronics have enabled the development of wireless sensor networks (WSNs) with high potential for various location-based applications in different fields. One of the most important topics in WSNs is the localization in environments with sensor nodes being scattered randomly over a region. Localization techniques are often challenged by localization latency, efficient energy consumption, accuracy, environmental factors, and others. The objective of this study was to improve the technique for detecting the nearest Bluetooth Low Energy sensor, which would enable the development of more efficient mobile applications for location advertising at fairs, exhibitions, and museums. The technique proposed in this study was based on the iBeacon protocol, and it was tested in a controlled room with three environmental settings regarding the density of obstacles, as well as in a real-world setting at the Expo Museum at Postojna in Slovenia. The results of several independent measures, conducted in the controlled room and in the real-world environment, showed that the proposed algorithm outperformed the standard algorithm, especially in the environments with a medium or high densities of obstacles. The results of this study can be used for the more effective planning of placing beacons in space and for optimizing the algorithms for detecting transmitters in mobile location-based applications that provide users with contextual information based on their current location.

During maintenance, software systems undergo continuous correction and enhancement activities due to emerging faults, changing environments, and evolving requirements, making this phase expensive and time-consuming, often exceeding the initial development costs. To understand and manage software under development and maintenance better, several maintainability measures have been proposed. The Maintainability Index is commonly used as a quantitative measure of the relative ease of software maintenance. There are several Index variants that differ in the factors affecting maintainability (e.g., code complexity, software size, documentation) and their given importance. To explore the variants and understand how they compare when evaluating software maintainability, an experiment was conducted with 45 Java-based object-oriented software systems. The results showed that the choice of the variant could influence the perception of maintainability. Although different variants presented different values when subjected to the same software, their values were strongly positively correlated and generally indicated similarly how maintainability evolved between releases and over the long term. Though, when focusing on fine-grained results posed by the Index, the variant selection had a larger impact. Based on their characteristics, behavior, and interrelationships, the variants were divided into two distinct clusters, i.e., variants that do not consider code comments in their calculation and those that do.