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.

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.

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.

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Monografija »Analitični pogled na najvidnejše družbene medije v letu 2023« na primeru analize enajstih družbenih medijev predstavlja strokovni vpogled v trenutno pokrajino najbolj priljubljenih družbenih medijev. Ta prispevek je namenjen sintezi in primerjavi obravnavanih družbenih medijev s primerjavo konceptov, kot so identiteta, skupine, prisotnost, odnosi, deljenje, sloves in pogovori ter primerjavo metrik, uporabljenih za njihovo analizo. Pri tem sta deljenje in sloves v vzorcu prepoznana kot dva najpomembnejša gradnika. Ugotavljamo, da so zasebni in javni pogovori skupna funkcionalnost vseh opazovanih družbenih medijev; Discorda, Facebooka, Instagrama, Linkedina, Pinteresta, Reddita, Snapchata, TikToka, Twitcha, Twitterja in YouTuba. Prevladujoč objekt deljenja v opazovanih družbenih medijih so video vsebine. Prepoznali smo presek metrik za merjenje konceptov pretvorbe, skrbi za stranke, vključenosti in zavedanja med različnimi družbenimi mediji. Te metrike so stopnja konverzije, razmerje med prikazi in kliki, odzivni čas, stopnja odziva, stopnja vključenosti, skupno število vključenih uporabnikov, število prikazanih objav in vključenih uporabnikov ter demografski doseg.

Web performance testing with tools such as Google Lighthouse is a common task in software practice and research. However, variability in time-based performance measurement results is observed quickly when using the tool, even if the website has not changed. This can occur due to variability in the network, web, and client devices. In this paper, we investigated how this challenge was addressed in the existing literature. Furthermore, an experiment was conducted, highlighting how unrepresentative measurements can result from single runs; thus, researchers and practitioners are advised to run performance tests multiple times and use an aggregation value. Based on the empirical results, 5 consecutive runs using a median to aggregate results reduce variability greatly, and can be performed in a reasonable time. The study’s findings alert to p otential pitfalls when using single run-based measurement results and serve as guidelines for future use of the tool.