Menna Maged Kamel

This paper argues that contemporary generative AI systems are not yet consistently equipped to provide the level of culturalization required in the creative and cultural industries (CCIs). Debates on generative AI in cultural domains have focused primarily on productivity, innovation, intellectual property, labor disruption and bias. While these concerns are important, they do not fully capture a more basic issue: cultural production depends on forms of meaning that are historically situated, socially mediated and unevenly distributed across languages, communities and markets. In such contexts, fluency, safety and technical usefulness are not sufficient measures of system quality. The paper discusses how an existing line of thought from localization, cross-cultural design and culturalization research should be carried into the era of generative AI as explicit attention to cultural context, symbolic meaning and interpretive plurality. The CCIs provide a particularly revealing testbed for this claim because they expose limitations of generic AI evaluation in domains where value depends on tone, memory, symbolism and representation. The argument combines published evidence on cultural bias and uneven representation in contemporary models with diagnostic case studies using ChatGPT 5.5 in museums, heritage sites and pilgrimage contexts. These studies illustrate concrete cultural-mediation problems under the conditions tested. On that basis, the article proposes a framework for culturalized generative AI and outlines an integrated research and governance agenda spanning AI, the humanities and cultural-sector practice.

This paper argues that today’s mainstream generative AI models do not yet provide the level of culturalization required in the creative and cultural industries (CCIs). Debates on generative AI in cultural domains have focused primarily on productivity, innovation, intellectual property, labor disruption and bias. While these concerns are important, they do not fully capture a more basic issue: cultural production depends on forms of meaning that are historically situated, socially mediated and unevenly distributed across languages, communities and markets. In such contexts, fluency, safety and technical usefulness are not sufficient measures of system quality. The paper discusses how an existing line of thought from localization, cross-cultural design and culturalization research should be carried into the era of generative AI as explicit attention to cultural context, symbolic meaning and interpretive plurality. The CCIs provide a particularly revealing testbed for this claim because they expose the limits of generic AI systems in domains where value depends on tone, memory, symbolism and representation. To ground the argument, the article revisits earlier work on targeted narratives, personalization and adaptive cultural mediation across museums, heritage sites and pilgrimage routes, and uses recent exploratory attempts with widely used generative AI models to show what remains missing. On that basis, the article proposes a framework for culturalized generative AI and outlines an integrated research and governance agenda spanning AI, the humanities and cultural-sector practice.

Cultural heritage is one of many fields that has seen a significant digital transformation in the form of digitization and asset annotations for heritage preservation, inheritance, and dissemination. However, a lack of accurate and descriptive metadata in this field has an impact on the usability and discoverability of digital content, affecting cultural heritage platform visitors and resulting in an unsatisfactory user experience as well as limiting processing capabilities to add new functionalities. Over time, cultural heritage institutions were responsible for providing metadata for their collection items with the help of professionals, which is expensive and requires significant effort and time. In this sense, crowdsourcing can play a significant role in digital transformation or massive data processing, which can be useful for leveraging the crowd and enriching the metadata quality of digital cultural content. This paper focuses on a very important challenge faced by cultural heritage crowdsourcing platforms, which is how to attract users and make such activities enjoyable for them in order to achieve higher-quality annotations. One way to address this is to offer personalized interesting items based on each user preference, rather than making the user experience random and demanding. Thus, we present an image annotation recommendation system for users of cultural heritage platforms. The recommendation system design incorporates various technologies intending to help users in selecting the best matching images for annotations based on their interests and characteristics. Different classification methods were implemented to validate the accuracy of our work on Egyptian heritage.