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.
Matina Kiourexidou, Marina Lioliou, Sofia Stamou· The Heritage· 1 citation
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.
Sofia Stamou, Matina Kiourexidou· Information· 0 citations
