Augmented Reality (AR) enhances learning by integrating interactive and immersive elements that bring content to life, thus increasing motivation and improving retention. AR also supports personalized learning, allowing learners to interact with content at their own pace and according to their preferred learning styles. This adaptability not only promotes self-directed learning but also empowers learners to take charge of their educational journey. Effective interface design is crucial for these AR applications, requiring careful integration of user interactions and visual cues to blend AR elements seamlessly with reality. This paper explores the impact of AR on user experience within educational settings, examining engagement, motivation, and learning outcomes to determine how AR can enhance the educational experience. Additionally, it addresses design considerations and challenges in developing AR user interfaces, drawing on current research and best practices to propose effective and adaptable solutions for educational AR applications. As AR technology evolves, its potential to transform educational experiences continues to grow, promising significant advancements in how users interact with, personalize, and immerse themselves in learning content.

Museum use of artificial intelligence (AI) is becoming increasingly common, but its contribution to museum attendance is yet to be confirmed. This paper investigates whether the adoption of AI impacts museum visitation using data from 19 museums. Statistical analyses, including ANOVA and Spearman correlation, were conducted to determine if the use of AI has significant effects on visitors. The findings indicate no statistically significant difference between museums that use AI and those that do not (ANOVA: p = 0.263, F = 1.34), but the Spearman correlation (r = 0.448, p = 0.055) indicates a moderate positive correlation that is not statistically significant. The findings suggest that AI enhances visitor experience rather than increasing attendance. Additionally, this study proposes a conceptual framework for AI prototyping in museums. The study contributes to the ongoing debate on AI in cultural institutions by emphasizing that future research should incorporate longitudinal studies and qualitative visitor feedback in order to capture the overall impact of AI on engagement and sustainability in museums.

In the current media world, there is a huge debate about the importance of the visibility of a news website in order to secure its existence. Thus, search engine optimization (SEO) practices have emerged in the news media systems around the world. This study aimed to expand the current literature about the SEO practices by focusing on examining, via the walkthrough method, the ways that news companies allow the users to comment on their online news articles. The comments on the news websites are related to the notions of social influence, information diffusion, and play an essential role as a SEO practice, for instance, by providing content and engagement. The examined sample was collected by the most visited news websites’ rankings of alexa.com for a global scale and for the countries Greece and Cyprus. The findings reveal that the news websites throughout the globe use similar features and ways to support the comments of the users. In the meantime, though, a high number of the news websites did not allow the users to use their social media accounts in order to comment the provided news articles, or provided multiple comment platforms. This trend goes against the SEO practices. It is believed that this finding is associated with the difficulty of the news organizations to regulate and protect themselves by the users’ comments that promote, in some case harmful rhetoric and polarization.

Generative Artificial Intelligence (AI) has become a rapidly expanding area of scientific research, generating a growing body of literature across technical and applied domains. This study provides a comprehensive bibliometric analysis of global generative AI research to characterize its publication growth, disciplinary and geographical distribution, institutional participation, funding patterns, citation performance, and thematic development. The analysis covers 22,758 Scopus-indexed journal articles and conference papers published between 2020 and 2025, identified using the phrase “generative artificial intelligence” enclosed in double quotation marks in TITLE-ABS-KEY fields. A reproducible computational workflow was used to examine publication output, document types, subject areas, countries, institutions, funding sponsors, citation patterns, and keyword co-occurrence. Citation analysis incorporated annualized citation rates and cohort-normalized annual citation rates to improve comparisons across publication years. Results show a pronounced acceleration in publication output after 2022, with an approximate 105% compound annual growth rate over 2020–2025. Computer Science remained the largest subject area, while substantial representation extended across Engineering, Social Sciences, Medicine, Mathematics, and other domains. Publication activity was concentrated among leading countries and institutions, with the United States and China recording the highest output. Funding analysis identified major national and international sponsors through publication–sponsor associations. Citation performance varied substantially across cohorts, with the 2023 cohort exhibiting the highest cohort-normalized annual citation rate (1.58). Keyword analysis revealed a thematic shift from early AI and GAN-related research toward generative AI and large language model themes, alongside education, innovation, human–AI interaction, and responsible AI. The findings provide an evidence-based, multidimensional characterization of the rapidly evolving generative AI research landscape.

Language tourism combines language learning with cultural exploration and offers new opportunities for engaging visitors with local linguistic heritage. Recent advances in Augmented Reality (AR) and Artificial Intelligence (AI) have enabled the development of immersive tourism experiences; however, their application to low-resource dialects remains limited. This paper proposes TalkCorfiota, a conceptual framework that integrates AR, conversational AI, geolocation services, and cultural storytelling to support language tourism experiences centered on the Corfiot dialect. Drawing upon situated learning, experiential learning, and digital heritage perspectives, the framework conceptualizes dialect learning as a form of heritage interpretation through which visitors engage with local identity, collective memory, and place-based cultural narratives. The proposed architecture combines location-aware cultural exploration, AR-enhanced contextual interaction, and AI-supported conversational engagement within a unified tourism ecosystem. The framework is supported by a structured linguistic resource developed during the course of this study from publicly available repositories of the Corfiot dialect. The primary contribution of this research is the conceptual design of the TalkCorfiota architectural framework, which is the foundation for future development and evaluation on AR- and AI-supported language tourism applications for low-resource dialects.