Generative AI (GenAI) is now common in university project work, yet previous studies often examine students’ trust, creativity, overload, or engagement separately. This leaves a key gap: how students regulate GenAI across a full project workflow. This exploratory study addresses that gap by examining a four-process “ AI-mediated Self-Regulated Learning ” ( AI-SRL ) cycle: (1) evaluating and selecting GenAI suggestions, (2) experiencing shifts in creative agency, (3) managing overload through filtering and summarizing, and (4) monitoring time and energy to stop or continue working with GenAI. We conducted a two-course basic qualitative design study with 97 undergraduate and graduate students. Data came from an open-ended questionnaire aligned to the four processes. We used inductive content analysis with a shared codebook, reliability checks, and cross-level comparisons. Findings show that students use combinations of strategies across the AI-SRL cycle. They exercise agency through goal alignment, revision, and verification, with graduates reporting stronger cross-checking and source-based justification. Creativity was described as a conditional outcome: it increased when GenAI widened ideas but declined when it replaced personal exploration. Overload was managed through targeted prompts and structured outputs, again more common among graduates. Most students did not lose track of time; they used clear stopping cues such as fatigue, repetition, or satisfaction. Together, results reveal two distinct metacognitive regulation styles: “ Exploratory-Simplification ” and “ Systematic-Methodical ”.

Mobile Ad hoc Networks (MANETs) are particularly vulnerable to the presence of selfish nodes, which aim to preserve their own resources by refusing to forward packets for others. Selfish behavior, especially when nodes refuse to forward route request (RREQ) messages during route discovery, can severely degrade network connectivity and packet delivery. We address this problem by formulating a lightweight Bayesian game model for studying RREQ forwarding. Each node maintains a single neighborhood-level belief coefficient rather than per-neighbor trust tables. This belief coefficient is updated using a Beta-Binomial rule with two counters, for cooperative and non-cooperative propagation observations, and nodes make RREQ forwarding decisions by comparing the expected utility of forwarding against a resource-dependent cost, yielding a simple threshold-based forwarding rule. At the path level, a multiplicative trust metric combines the belief coefficients of intermediate relays and is used as a route-quality indicator. The proposed model is integrated into the Ad hoc On-Demand Distance Vector (AODV) routing protocol to assist routing decisions and is evaluated through network simulation under varying fractions of selfish nodes, node densities, and mobility conditions. Results show that the proposed approach mitigates the impact of selfish behavior by improving packet delivery performance in the presence of selfish nodes, at the cost of a measurable delay and a routing-overhead trade-off.

This study investigated how undergraduate students collaborated when working with ChatGPT and what teamwork approaches they used, focusing on students’ preferences, conflict resolution, reliance on AI-generated content, and perceived learning outcomes. In a course on the Applications of Information Systems, 153 undergraduate students were organized into teams of 3. Team members worked together to create a report and a presentation on a specific data mining technique, exploiting ChatGPT, internet resources, and class materials. The findings revealed no strong preference for a single collaborative mode, though Modes #2, #4, and #5 were marginally favored due to clearer structures, role clarity, or increased individual autonomy. Students reasonably encountered initial disagreements (averaging 30.44%), which were eventually resolved—indicating constructive debates that improve critical thinking. Data also showed that students moderately modified ChatGPT’s responses (50% on average) and based nearly half (44%) of their overall output on AI-generated content, suggesting a balanced yet varied level of reliance on AI. Notably, a statistically significant relationship emerged between students’ perceived learning and actual performance, implying that self-assessment can complement objective academic measures. Students also employed a diverse mix of communication tools, from synchronous (phone calls) to asynchronous (Instagram) and collaborative platforms (Google Drive), valuing their ease of use but facing scheduling, technical, and engagement issues. Overall, these results reveal the need for flexible collaborative patterns, more supportive AI use policies, and versatile communication methods so that educators can apply collaborative learning effectively and maintain academic integrity.

Phishers target Internet users and send them phishing emails or create phishing websites to deceive victims and extract financial gains. This study investigated the factors that affect Internet users in phishing detection. The responses of 252 participants to an online survey were collected and analysed. Participants were mostly careful when a website requested urgent action, its content had errors, or an email message asked recipients to give personal information or click a suspicious link. They believed that education about phishing, email filtering software, and connecting directly to the official website, and not through a link, were the best practices. They also stated that they could learn how to easily detect phishing attempts and evaluate whether an email was spam, advertising, phishing, or a scam. It was also found that the creation of blacklists and whitelists did not provide sufficient protection against phishing, active warnings were more effective than passive warnings, and users were receiving phishing emails that escaped email filtering and antivirus software. Further statistical analysis of their responses revealed that there was a moderate positive correlation between each of the variables Self-Efficacy in Phishing Detection, Information Evaluation Skills, E-Banking Phishing Detection Skills with Phishing Detection Effectiveness. Finally, it was found that the Facilitating Conditions for Phishing Detection, E-Banking Phishing Detection Skills, and E-Mail Phishing Detection Skills influenced Phishing Detection Effectiveness.

As our society increasingly relies on digital platforms for information, the spread of fake news has become a pressing concern. This study investigates the ability of Greek and Portuguese Instagram users to identify fake news, highlighting the influence of cultural differences. The responses of 220 Instagram users were collected through questionnaires in Greece and Portugal. The data analysis investigates characteristics of Instagram posts, social endorsement, and platform usage duration. The results reveal distinct user behaviors: Greeks exhibit a unique inclination towards social connections, displaying an increased trust in friends’ content and investing more time on Instagram, reflecting the importance of personal connections in their media consumption. They also give less importance to a certain post’s characteristics, such as content opposing personal beliefs, emotional language, and poor grammar, spelling, or formatting when identifying fake news, compared to the Portuguese, suggesting a weaker emphasis on content quality in their evaluations. These findings show that cultural differences affect how people behave on Instagram. Hence, content creators, platforms, and policymakers need specific plans to make online spaces more informative. Strategies should focus on enhancing awareness of key indicators of fake news, such as linguistic quality and post structure, while addressing the role of personal and social networks in the spread of misinformation.

The use of Flying Ad-hoc Networks (FANETs) in precision agriculture requires the development of advanced routing protocols to manage UAV-specific challenges effectively. This paper presents AirPro-FL, a proactive routing protocol that uses fuzzy logic to optimize UAV performance in precision agriculture tasks. Unlike conventional FANET research, which often relies on stochastic mobility models that do not accurately reflect real-world agricultural missions, AirPro-FL is designed to address these gaps by enhancing UAV cooperation in scanning operations such as crop scouting, crop surveying and mapping, spraying applications, and geofencing. Traditionally, these agricultural activities rely on a single UAV, often resulting in inefficiencies. The UAV's limited real-time data transmission capabilities, vulnerability to operational failures, and potential mission execution delays contribute to reduced overall effectiveness. The proposed system involving multiple UAVs significantly speeds up mission completion and enables real-time data transfer through the cooperation between FANETs and Mobile Ad-hoc Networks (MANETs). This innovation empowers agricultural stakeholders to make faster and more reliable decisions based on accurate data collection. Simulation results indicate that AirPro-FL consistently achieves the highest Packet Delivery Ratio (PDR) across all scenarios, halves the average end-to-end delay compared to the second-best protocol, and exhibits superior energy efficiency. The protocol's success in optimizing data collection during scanning operations underscores its broader applicability beyond agriculture, extending to other fields such as environmental monitoring, disaster management, and surveillance, where similar mobility patterns are employed.

Artificial intelligence (AI) is forcing a dramatic transformation of the methods by which we acquire knowledge and engage in collaborative learning. Although there are several studies on how AI can support collaborative learning, there are no published studies examining how students can actually collaborate among themselves while interacting with AI tools. For this study, thirty postgraduate students were organized into teams of three, and each team developed a project mainly exploiting responses from ChatGPT, Google Gemini, and MS Copilot, as well as the internet and class resources. Each team selected a specific internet of things (IoT) application area and described the technologies and real-world cases in this area. Then, each team delivered a report with the full description of their project and their interactions with these generative AI (GenAI) tools and presented their work in class. Additionally, students answered an online questionnaire with closed- and open-ended questions and participated in focus group discussions. Members of each team collaborated to design prompts using five suggested modes of collaboration. Eventually, half of the students exploited all five collaborative modes, but they mostly liked and preferred three of these collaborative modes. On average, teammates initially disagreed 24% of the time but eventually reached an agreement. Students appreciated GenAI tools for their quick and well-structured responses, natural communication style, broad subject coverage, as well as their ability to simplify complex topics and support personalized learning. However, they expressed concerns about GenAI tools’ inaccurate and inconsistent responses and identified key risks, such as passive learning, over-dependence, outdated information, and privacy issues. Finally, students recommended that GenAI tools should provide a shared and well-organized discussion space for collaborative prompt asking, allowing all team members to simultaneously view each other’s prompts and the tool’s responses. They also advised source verification and proper training to ensure these tools remain supplementary rather than primary learning resources.

Augmented Reality (AR) can offer benefits to education however, its successful implementation heavily relies on teachers’ competencies personal characteristics, attitudes, and skills. The Teachers’ Augmented Reality Competences (TARC) framework defines the main AR capacity areas for educators to employ in their teaching practice: creating, using, and managing AR resources. The current study proposes and validates a model about the impact of teachers’ personal innovativeness, attitudes towards AR and digital skills on the TARC defined competence areas. Quantitative data was collected from 150 educators worldwide. Structured equation modeling was used for the analysis. The model explains and predicts approximately 73

The OpenLang Network platform is a sustainable online environment designed to support language learning, intercultural exchange, and open educational practices across Europe. This paper presents the conceptual framework and design of an AI-enhanced OpenLang Network platform, in which Generative AI is embedded across all language learning services offered by the platform. The integration of Generative AI transforms the placement tests offered by the platform into adaptive diagnostic tools, extends the platform’s tandem language learning service through AI-mediated conversation, and enriches the open educational resources of the platform through automated adaptation, translation, and content generation. These innovations collectively reposition the OpenLang Network platform as a dynamic, learner-centred, and sustainable ecosystem that unites human collaboration with AI-powered personalisation. Through a pedagogically informed integration of Generative AI, the case of the OpenLang Network platform demonstrates how AI can enhance openness, collaboration, and personalisation in language learning.

Augmented Reality (AR) can enhance learning experiences offering many benefits to students. However, its integration in educational practice is rather limited due to several obstacles. One of these obstacles is the absence of AR digital competencies among instructors. Limited research exists about teachers’competence areas in integrating AR in teaching and learning. The current study utilizes the validated Teachers’ AR Competences (TARC) framework to investigate teachers’ self-perceived competences in creating, using, and managing AR resources. Furthermore, it investigates educators’ attitudes towards integrating AR in education. An online survey received responses from 150 educators worldwide. Quantitative results indicated that while teachers have positive attitudes towards educational AR, they do not feel confident in creating, using, or managing AR resources and experiences. All TARC subscales found to be significantly correlated to attitudes towards AR. No significant differences were found across all competence areas in regard to gender, age, and teaching level. However, statistically significant differences were found across all competence areas with respect to the teaching subject, general digital skills level, and previous class use of AR. Among the main practice and policy implications discussed, we suggest the need for training teachers in instructional design that deploys AR experiences.

Augmented Reality (AR) can enhance learning experience by offering various benefits to learners. However, its integration in classroom practice remains challenging and one reason of this is the lack of teachers’ AR competences. The Teachers’ AR Competences (TARC) framework defines the main AR competences that educators should have in order to successfully employ AR in their teaching: Creating, Using and Managing AR resources. The current study, building upon the TARC framework, aims to examine the effect of the TARC components of Creation and Management to the educators’ ability to Use AR in class. It is the first study that investigates the impact of the educators’ AR competences on their ability to use AR in classes. Moreover, while studies for primary and secondary teachers’ AR skills exist, this is the first study that explores also university lecturers’/professors’ ability to use AR in classes. A survey was conducted with 150 educators around the globe. Regression analysis revealed that the Creation and the Management competences significantly predict university lecturers’/professors’ and primary/secondary school teachers’ ability to Use AR in their classes. Study findings deemed important for educators and education administration and implications are discussed.

This study investigates the psychological, educational, and technological difficulties faced by primary education students with special needs during online teaching. An interpretative phenomenological analysis was used for the qualitative analysis of data obtained through semi-structured interviews with twenty-two (22) teachers in primary education at a European country. The results revealed that their students showed negative emotions and behaviour. Those diagnosed with autism and learning disabilities had difficulty concentrating in class, while those with sensory disabilities had epileptic instances. Students with mild mental retardation in particular found it difficult to use digital tools. Many problems, however, are due to the lack of infrastructure and digital skills, as well as proper preparation of teachers for online teaching. Therefore, students and teachers should be equipped with the necessary digital skills, specialised digital tools and accessible open educational resources (OER) in order to effectively participate in online education.

The aim of this study is to present the findings of a qualitative study aiming at capturing key stakeholders’ perceptions with regard to: (a) gender equality in academia and the workplace; (b) challenges, needs, and experiences in academia and workplace with regard to gender. This research captures the current situation of gender equality in the fields of Science, Technology, Engineering and Mathematics (STEM) and provides a deep understanding of the needs, challenges and experiences both men and women encounter in academia vis-a-vis the industry. Forty-one interviews were conducted in Cyprus, Greece, Italy, Slovenia, and Spain. Data collected demonstrate a variety of challenges faced by all genders in the workplace and in academia, as well as the need for more concrete actions that will allow for a gender-balanced perspective to be heard in the STEM fields. Implications for practitioners, policymakers and researchers are also provided.

Purpose The purpose of this paper is to review ontologies and data models currently in use for augmented reality (AR) applications, in the cultural heritage (CH) domain, specifically in an urban environment. The aim is to see the current trends in ontologies and data models used and investigate their applications in real world scenarios. Some special cases of applications or ontologies are also discussed, as being interesting enough to merit special consideration. Design/methodology/approach A search using Google Scholar, Scopus, ScienceDirect and IEEE Xplore was done in order to find articles that describe ontologies and data models in AR CH applications. The authors identified the articles that analyze the use of ontologies and/or data models, as well as articles that were deemed to be of special interest. Findings This review found that CIDOC-CRM is the most popular ontology closely followed by Historical Context Ontology (HiCO). Also, a combination of current ontologies seems to be the most complete way to fully describe a CH object or site. A layered ontology model is suggested, which can be expanded according to the specific project. Originality/value This study provides an overview of ontologies and data models for AR CH applications in urban environments. There are several ontologies currently in use in the CH domain, with none having been universally adopted, while new ontologies or extensions to existing ones are being created, in the attempt to fully describe a CH object or site. Also, this study suggests a combination of popular ontologies in a multi-layer model.

Introduction Oral immunotherapy (OIT) has become a valuable modality for food allergy management. Reliable tools for monitoring OIT response can be useful in clinical management. We compared Total IgE (tIgE), specific IgE for peanut (sIgE-PN), and Ara h2 (sIgE-Arah2) to the Basophil Activation Test for peanut (BAT-PN) and IgG4-PN before and after OIT. Methods We conducted a retrospective chart review of subjects with a history of severe peanut allergy who underwent OIT. Baseline tIgE, sIgE-PN, sIgE-Arah2, BAT-PN, IgG4-PN, and IgG4-Arah2 were compared with subsequent test results to assess the usefulness of monitoring OIT progress with the various diagnostic assays. Results We identified 34 patients with an average age of 12 years (range 5-17 years) at the beginning of OIT. The average period between tests was 15 months (range 5-25 months). The dose of peanut protein at the last test ranged from 18 mg to 7000 mg (median: 287 mg). The baseline parameters (mean ± SE) were tIgE (811.2±169.3 IU/mL), sIgE-PN (59.9±6.9 kU/L), and sIgE-Arah2 (45.9±8.1 kU/L). There was an increase in tIgE (67.6%±25.3%), sIgE-PN (33.5%±18.2%), and sIgE-Arah2 (45.2%±20.3%) after OIT. IgG4-PN and IgG4-Arah2 levels also increased with OIT. A significant decrease (-45.8%±11.7%, p < 0.01) in basophil activation was observed by BAT-PN after OIT. The positive eliciting threshold for BAT-PN had increased, correlating with OIT escalating dosage and duration. Conclusion BAT-PN showed consistent clinical correlation during OIT and was a valuable tool in assessing OIT progress. Conventional tIgE, sIgE-PN, and sIgE-Arah2 were not as helpful in monitoring OIT response.

Aim/Purpose: This study aims to examine the influence of digital competences, technology acceptance, and individual factors (gender and educational level) on academic achievement in Physical Education and Sports Science (PESS). Background: Prior research has established a positive correlation between digital competences and performance, but the mediating role of technology acceptance remains unclear. Furthermore, there is no evidence in the literature about this relationship among students pursuing degrees in PESS. Methodology: A survey was administered to 344 students pursuing degrees in PESS. The Students’ Digital Competence Scale (SDiCoS) measured digital competences, while the Technology Acceptance Model (TAM) assessed technology acceptance. Academic performance was evaluated based on students’ GPAs. Contribution: This paper investigated the role of digital competence within the TAM framework and its influence on academic performance. We propose that digital competence variables positively impact students’ intention to use digital tools for learning. This aligns with TAM principles, where intention and attitude toward technology predict its actual use. Our findings further strengthen the understanding of TAM by confirming strong connections between perceived ease of use, perceived usefulness, and attitude toward technology. Additionally, the study suggests that digital competence and frequent device usage patterns might be more prevalent in postgraduate education. Findings: The investigation supports the link between digital competences and technology acceptance in PESS students. Specifically, TAM variables, particularly attitudes and intentions regarding technology use, significantly predicted these students’ academic performance. Interestingly, no direct association was found between SDiCoS digital competences and academic performance. Digital competence variables were positively associated with students’ intention to use digital tools for learning. Gender differences emerged, with females reporting higher academic performance and proficiency in Communicate, Collaborate, and Share (CCS) competences. Furthermore, postgraduate students reported digital competences, higher academic performance, stronger intentions to use technology, and more frequent utilization of laptops/tablets. Recommendations for Practitioners: Educators, administrators, and policymakers should consider targeted interventions and curriculum development to enhance academic performance in the fields of physical education and sports science. Specifically, strategies should focus on fostering digital competences in areas relevant to the field while addressing gender-specific needs. Recommendation for Researchers: Future research should further explore the nuanced relationship between digital competences, technology acceptance, and academic performance, with a focus on refining the predictive efficacy of TAM variables, and examining the role of individual factors, such as motivation and self-efficacy. Impact on Society: The findings have implications for improving academic outcomes in PESS, ultimately contributing to the development of a highly skilled and technology-literate workforce in this field. Future Research: Future research should examine the specific mechanisms through which digital competences and technology acceptance influence academic performance to develop effective interventions and strategies.

Remote persons can be represented by telepresence robots (TRs) located at another location. TRs are used in diverse fields including education. However, most of the previous studies have explored particular instances of introducing TRs in education. The present study aims to bring together the viewpoints of educators from different countries and educational institutes. The partners of the Erasmus+ project TRinE conducted interviews and focus groups among 46 educators in Austria, France, Germany, Iceland, and Malta. Findings indicated that educators appreciated that a remote student using a TR can feel and being felt present as well as move around in the class and the school. TRs enhance inclusiveness since the remote user can be an ill student or anyone at a remote location. The educators mentioned TRs’ weaknesses such as their unstable Wi-Fi connectivity and poor audio video quality. They also pointed out challenges concerning privacy issues, loss of Wi-Fi connectivity, noisy environments and obstacles along the way as the TR moves (elevators, doors, stairs, etc.). Finally, the educators recommended that TRs’ manufacturers build more user friendly, visible, and accessible TRs as well as educational institutes apply effective TRs management procedures.