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.

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.

E-business technology is becoming one of the most important global markets where e-business solutions will have to adapt to new technologies. The main objective in this study was to synthesize existing knowledge in the field of e-business technology acceptance and to understand differences in Technology Acceptance Model (TAM) related causal effect sizes for different e-business contexts. A quantitative meta-analysis of existing empirical research about factors affecting e-business adoption was conducted using 89 published papers that provided empirical data about causal relationships. A moderator analysis was carried out to investigate the moderating effect of four factors: consumer type, device type, continent and respondent type. The results of the study showed a moderating effect for all four proposed factors in almost all TAM-related causal paths. The study also showed that TAM is the most common theory being applied in e-business adoption research.

Despite efforts to promote digital inclusion for all, individuals with disabilities continue to experience a significant digital divide. Developing usable and accessible solutions also demands conducting user-based testing with real end users, particularly for users with disabilities, to detect and address real problems and barriers they experience and improve usability and accessibility based on their feedback through a user-centered approach. When including users with disabilities in user testing, additional attention must be paid to ensure that the testing is planned and carried out in such a way as to enable the successful and efficient integration of users with disabilities. In doing so, it is necessary to consider various restrictions related to users’ disabilities. By conducting a systematic literature review, we collected and analyzed the challenges of including users with disabilities in user-based testing and best practices that researchers can apply in future user-based testing with users with disabilities. In the existing literature, a positive trend toward publishing articles describing testing with users with disabilities is noticeable. There is also an apparent need for more reporting on some phases of the testing studies. The main result of this study is a list of challenges and best practices that are important in the different phases of user-based testing with users with disabilities.

Qualitative data analysis (QDA) tools are essential for extracting insights from complex datasets. This study investigates researchers’ perceptions of the usability, user experience (UX), mental workload, trust, task complexity, and emotional impact of three tools: Taguette 1.4.1 (a traditional QDA tool), ChatGPT (GPT-4, December 2023 version), and Gemini (formerly Google Bard, December 2023 version). Participants (N = 85), Master’s students from the Faculty of Electrical Engineering and Computer Science with prior experience in UX evaluations and familiarity with AI-based chatbots, performed sentiment analysis and data annotation tasks using these tools, enabling a comparative evaluation. The results show that AI tools were associated with lower cognitive effort and more positive emotional responses compared to Taguette, which caused higher frustration and workload, especially during cognitively demanding tasks. Among the tools, ChatGPT achieved the highest usability score (SUS = 79.03) and was rated positively for emotional engagement. Trust levels varied, with Taguette preferred for task accuracy and ChatGPT rated highest in user confidence. Despite these differences, all tools performed consistently in identifying qualitative patterns. These findings suggest that AI-driven tools can enhance researchers’ experiences in QDA while emphasizing the need to align tool selection with specific tasks and user preferences.