Minas Pergantis

Recent developments in artificial intelligence, including the emergence of generative AI and large language models, have brought changes to user interaction habits as well as interface design. These emerging technologies can be integrated into digital art repositories to reshape interaction paradigms through content enrichment, context-based recommendations, natural-language querying and automated artwork classification. As this landscape evolves, it is important to understand how these changes are reflected in user behavior. In this study, we present a comparative behavioral evaluation based on two versions of a publicly available digital repository for academic artworks: a traditional version and one incorporating a series of AI-powered features. A mixed factorial design with task counterbalancing was used to evaluate user behavior through an approach combining platform analytics, self-reported metrics and qualitative insights. This exploratory analysis identified feature-dependent differences in participants’ behavior and perceptions, and yielded preliminary observations that can inform large-scale research. Understanding how users perceive and engage with AI features can allow content providers to make informed decisions regarding their integration and streamline user interactions while remaining aligned with stakeholder expectations.

Digital repositories have been an important gateway for the dissemination of information regarding objects of art and cultural heritage throughout the World Wide Web, but the vast number of available artifacts, both historical and modern, makes their discovery by interested users an arduous task. Often deviating from general-purpose search behavior, people searching online for art and culture adjust their habits to address this challenge. In this study, real-world data from the federated search engine and online art and culture repository ArtBoulevard are used to explore this evolution throughout a period of three years. By collecting and analyzing a large amount of user session data, this research aims to investigate user engagement, query formulation, search behavior, and in-platform and outbound engagement in order to outline the longitudinal behavioral patterns of the platform’s user base. Over the period of the analysis, shifts and trends are identified and discussed within the ever-evolving context of behavioral analysis in the field. This process leads to useful insights that are not only indicative of the platform’s limited but global user base, but which can be useful to all stakeholders active in content dissemination and may also be relevant to broader discussions about the changes in the discovery pathways in art and cultural heritage.

Digital art repositories strive to disseminate works of art through the World Wide Web and to reach the widest possible global audience. To that end, providing an optimal user experience (UX) is essential. Usability is the cornerstone of UX in all interactions between the visitor and the platform, but at the same time, as virtual places of art and culture, digital art repositories aim to also provide an aesthetically pleasing interface that stimulates the senses. These goals are not always aligned, and how end users perceive the interplay between aesthetics and usability is an important factor in creating a balanced UX. This study presents a streamlined methodology for the collection of visitor insights concerning aesthetics and usability, taking advantage of the attention tracking capabilities of the iMedius platform. The iMedius Form Builder digital research tool allows the collection of both self-reported feedback through survey replies and candid data through gaze and mouse tracking, thus creating a robust dataset that can lead to interesting insights. An interactive questionnaire investigating user reaction to three different digital art repositories is presented, and feedback from higher education students from the fields of digital art and media is presented and analyzed in detail. Through this analysis, interesting insights are derived regarding striking a balance between high usability and memorable aesthetics.

In the modern era, a major avenue of dissemination for cultural and artistic events is through the World-Wide Web, where every such event has a multifaceted distinct digital footprint. This digital footprint is an indicator of how strong the influence of each event is in the public’s perception and to what extent it becomes part of the audiovisual art landscape. This study aims to present how the impact of an audiovisual event may be estimated using quantitative data collected through its online presence. This data-driven approach is made possible through web data extraction techniques and the use of generative artificial intelligence, which allows for structured information extraction from an endless variety of websites. Based on an event’s innate characteristics, web outreach, estimated scope, and thematical popularity, an encompassing impact factor is calculated, which may be used to rank events on the basis of perceived influence. For the purposes of this study, a dataset consisting of thousands of events in Greece was collected over an extended period. These data were used for a computational statistical analysis. Through this process of data collection, impact calculation, and analysis, data-driven insights were derived concerning the landscape of audiovisual art events.