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.

The purpose of this article is to investigate the information-seeking behavior of hospitalized patients in a peripheral hospital in Greece, based on Wilson’s theoretical model. After a thorough literature review, we report on the results of a cross-sectoral sur\vey that was conducted based on Wilson’s macro-model of information-seeking behavior. The survey exploits the feedback we obtained from 150 hospitalized patients in the General Hospital of Corfu island in Greece. For the feedback collection, we relied on a structured questionnaire that was distributed to patients and correlates information-seeking behavior of hospitalized patients’ clinical data and eHealth Literacy. According to our findings, the most commonly recorded factors that pertain to information needs of hospitalized patients relate to treatment information, procedural information as well as information about the patients’ rights and support. The main information sources to which patients turn for receiving information are digital scientific sources, health professionals, other patients, insurance funds, family members and friends and the media. Concerning the most frequent obstacles associated with the information-seeking process, these account primarily to individual, interpersonal and environmental factors. Finally, the research came to a conclusion with the adaptation of Wilson’s theoretical model to hospitalized patients by confirming the original-initial model. The findings of our survey reveal the necessity to equip hospital units with adequate staff and to develop programs that offer updated information services to the hospitalized population. Moreover, our survey suggests that the availability of advanced health-related information services can empower the general population toward establishing e-Health literacy skills as well as toward increasing their level of satisfaction from the quality of the health services offered. In this direction, a clinical librarian program would empower hospitals worldwide to make their patients literate about health-related information.

In many situations, searching the web is synonymous to information seeking. Currently, web search engines are the most popular vehicle via which people get access to the web. Their popularity is partially due to the intrinsic way that people interact with them, i.e., by typing some keywords to the corresponding input box. Despite their popularity, search engines often fail to satisfy certain information needs, especially when the latter are haze and poorly articulated. In this paper, we focus on the occasions when large-scale web search engines find it difficult to cope with specific information-seeking behaviors and we accordingly introduce a query construction service that is targeted towards the solution of this problem. The proposed service leverages information coming from various DBpedia datasets and provides an intuitive GUI via which searchers determine the semantic orientation of their queries before these are addressed to the underlying search engine. The evaluation of the query construction service justifies the motive of this paper and indicates that it can considerably improve the searchers’ querying ability when search engines fail to provide adequate help.

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.