For more than a decade, User eXperience (UX) has grown into a core concept of Human--Computer Interaction (HCI) and has been widely disseminated and accepted in the HCI community. At the same time, Cultural Heritage (CH) has been a favored domain for UX research, while the visitor is able to exploit CH material before, during, and after the visit, having different goals and requirements in each phase. Thus, CH organisations need to carefully explore the potential of investing in UX, as visitor satisfaction and intention to return and spread positive word-of-mouth is closely linked to the destination's overall success. A multitude of methods for UX analysis exist, but a clear overview of the current state of the available UX research methods in CH is missing. Recent studies [Othman 2012; Roussou 2018] have successfully developed frameworks to measure UX in cultural spaces, but most of them still lack understanding about how visitors interact with new technologies and simultaneously with the exhibits. In this article, a survey that describes the conceptual frameworks, models, research methodologies, and paradigms of incorporating Cultural User eXperience (CUX) in applications is reported, as well as the overall interaction between UX and CH and the related aspects that are influenced by the CH application domain and the parameters leading to optimization of the CUX. The outcome of this survey lays ground for understanding and defining the concept of UX research in CH, while current challenges and issues for future work are discussed.

This paper proposes the utilization of large language models as recommendation systems for museum visitors. Since the aforementioned models lack the notion of context, they cannot work with temporal information that is often present in recommendations for cultural environments (e.g., special exhibitions or events). In this respect, the current work aims to enhance the capabilities of large language models through a fine-tuning process that incorporates contextual information and user instructions. The resulting models are expected to be capable of providing personalized recommendations that are aligned with user preferences and desires. More specifically, Generative Pre-trained Transformer 4, a knowledge-based large language model is fine-tuned and turned into a context-aware recommendation system, adapting its suggestions based on user input and specific contextual factors such as location, time of visit, and other relevant parameters. The effectiveness of the proposed approach is evaluated through certain user studies, which ensure an improved user experience and engagement within the museum environment.

This paper introduces a groundbreaking approach to enriching the museum experience using ChatGPT4, a state-of-the-art language model by OpenAI. By developing a museum guide powered by ChatGPT4, we aimed to address the challenges visitors face in navigating vast collections of artifacts and interpreting their significance. Leveraging the model’s natural-language-understanding and -generation capabilities, our guide offers personalized, informative, and engaging experiences. However, caution must be exercised as the generated information may lack scientific integrity and accuracy. To mitigate this, we propose incorporating human oversight and validation mechanisms. The subsequent sections present our own case study, detailing the design, architecture, and experimental evaluation of the museum guide system, highlighting its practical implementation and insights into the benefits and limitations of employing ChatGPT4 in the cultural heritage context.

This study investigates the use of educational data mining (EDM) techniques to predict student performance and enhance learning outcomes in higher education. Leveraging data from Moodle, a widely used learning management system (LMS), we analyzed 450 students’ academic records spanning nine semesters. Five machine learning algorithms—k-nearest neighbors, random forest, logistic regression, decision trees, and neural networks—were applied to identify correlations between courses and predict grades. The results indicated that courses with strong correlations (+0.3 and above) significantly enhanced predictive accuracy, particularly in binary classification tasks. kNN and neural networks emerged as the most robust models, achieving F1 scores exceeding 0.8. These findings underscore the potential of EDM to optimize instructional strategies and support personalized learning pathways. This study offers insights into the effective application of data-driven approaches to improve educational outcomes and foster student success.

Recent developments in digital technologies regarding the cultural heritage domain have driven technological trends in comfortable and convenient traveling, by offering interactive and personalized user experiences. The emergence of big data analytics, recommendation systems and personalization techniques have created a smart research field, augmenting cultural heritage visitor’s experience. In this work, a novel, hybrid recommender system for cultural places is proposed, that combines user preference with cultural tourist typologies. Starting with the McKercher typology as a user classification research base, which extracts five categories of heritage tourists out of two variables (cultural centrality and depth of user experience) and using a questionnaire, an enriched cultural tourist typology is developed, where three additional variables governing cultural visitor types are also proposed (frequency of visits, visiting knowledge and duration of the visit). The extracted categories per user are fused in a robust collaborative filtering, matrix factorization-based recommendation algorithm as extra user features. The obtained results on reference data collected from eight cities exhibit an improvement in system performance, thereby indicating the robustness of the presented approach.

In this paper, we discuss the gamification strategies and methodologies used by TRIPMENTOR—a game-oriented cultural tourism application in the region of Attica. Its primary purpose is to provide visitors with rich media content via the web and mobile environments by redirecting travellers, highlighting points of interest, and providing information for tour operators. Gamification is a critical component of the project; it relates users to specific sites and activities, improves their visiting experiences, and encourages a constant interaction with the application through a playful experience. In TRIPMENTOR, gamification serves both as a tourism marketing strategy and as a tool for encouraging users to share their experiences while exploring Attica in a way designed to meet their personal needs, interests, and habits. This paper aims to describe and analyse the gamification mechanisms applied, following the Octalysis framework, and discuss the opportunities and challenges of gamification as a tourist marketing strategy.