Generative AI (GenAI) tools, including ChatGPT, Microsoft Copilot, and Google Gemini, are rapidly reshaping higher education by transforming how students, educators, and researchers engage with learning, teaching, and academic work. Despite their growing presence, the adoption of GenAI remains inconsistent, largely due to the absence of universal guidelines and trust-related concerns. This study examines how trust, defined across three key dimensions (accuracy and relevance, privacy protection, and nonmaliciousness), influences the adoption and use of GenAI tools in academic environments. Using survey data from 823 participants across different academic roles, this study employs multiple regression analysis to explore the relationship between trust, user characteristics, and behavioral intention. The results reveal that trust is primarily experience-driven. Frequency of use, duration of use, and self-assessed proficiency significantly predict trust, whereas demographic factors, such as gender and academic role, have no significant influence. Furthermore, trust emerges as a strong predictor of behavioral intention to adopt GenAI tools. These findings reinforce trust calibration theory and extend the UTAUT2 framework to the context of GenAI in education. This study highlights that fostering appropriate trust through transparent policies, privacy safeguards, and practical training is critical for enabling responsible, ethical, and effective integration of GenAI into higher education.

A significant portion of the academic community, including students, teachers, and researchers, has incorporated generative artificial intelligence (GenAI) tools into their everyday tasks. Alongside increased productivity and numerous benefits, specific challenges that are fundamental to maintaining academic integrity and excellence must be addressed. This paper examines whether ethical implications related to copyrights and authorship, transparency, responsibility, and academic integrity influence the usage of GenAI tools in higher education, with emphasis on differences across academic segments. The findings, based on a survey of 883 students, teachers, and researchers at University North in Croatia, reveal significant differences in ethical awareness across academic roles, gender, and experience with GenAI tools. Teachers and researchers demonstrated the highest awareness of ethical principles, personal responsibility, and potential negative consequences, while students—particularly undergraduates—showed lower levels, likely due to limited exposure to structured ethical training. Gender differences were also significant, with females consistently demonstrating higher awareness across all ethical dimensions compared to males. Longer experience with GenAI tools was associated with greater ethical awareness, emphasizing the role of familiarity in fostering understanding. Although strong correlations were observed between ethical dimensions, their connection to future adoption was weaker, highlighting the need to integrate ethical education with practical strategies for responsible GenAI tool use.

In this paper we describe research in the ways in which consumers reach the information on television programme schedule and what motivates them to use the electronic programme guide service. The research was carried out using the...

The integration of Internet services and telephony services is a new area for the development of telecommunications services. One example is an e-mail alerting service that uses the telephony network for e-mail notification. The EMA system is a computer telephony integration (CTI) application that checks a user's mailbox on the mail server and informs him/her over the phone when new e-mail arrives, eliminating the need for permanent Internet connection. The EMA system has a Web-based interface, enabling the user to configure service parameters. The EMA system is developed as a distributed and concurrent application. It consists of seven modules: the console, Web interface, Web handler, controller, voice machine, database, and mail checker, using communication solutions based on component object model (COM) technology. This article describes the structure of the EMA system, its implementation, and advantages for users

This paper presents a memetic algorithm (MA) for energy cost estimation of a robot path. The developed algorithm uses a random recombination genetic algorithm (GA) as the basis for the first stage of the algorithm and performs a local search based on feature importances determined from the data in the second stage. To allow for the faster determination of the solution quality, the algorithm uses an ML-driven fitness function, based on MLP, for the determination of path energy. The performed tests show that not only does the GA itself optimize the point-to-point paths well, but the usage of MA can lower the energy use by 58% on average (N = 100) when compared to a linear path between the same two points.

The swift progress of artificial intelligence (AI), deep learning, and natural language processing brought massive changes across diverse domains. A substantial body of research underscored the advantages of AI technologies for consumers and identified motivational factors for their adoption. Current systematic literature reviews usually examined the traditional Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), and AI Device Use Acceptance (AIDUA), focused on a single solution or technology. On the other hand, the importance of perceived switching cost and measurement of switching behavior is an essential part of well-established and widely used theories (Push-Pull-Mooring (PPM) framework, Status Quo Bias theory, Lazy User Theory, etc.). By following the PRISMA 2020 guidelines, this literature review highlights studies that observe switching costs and switching behavior towards AI-based solutions. $\mathrm{N} = 2,739$ articles were initially identified in prominent databases (IEEE Xplore, Scopus, Web of Science, and EBSCO Host). After screening, $n = 7$ articles were deemed suitable for inclusion, and most of them relied on the Dual-factor framework and PPM framework. In many cases, these frameworks were integrated with theories and models such as the Status Quo Bias theory, UTAUT, TAM, and TAM3 to provide an understanding of underlying factors affecting user behavior and decision-making. Positive factors influencing switching behavior towards AI adoption include recognizing human limitations, while simultaneously perceiving advantages of AI technology, such as ease of use, efficiency, and personalized experiences. Negative factors, which hinder AI acceptance, encompass privacy risks, inertia, uncertainty, regret avoidance, and cognitive biases.