Abstract
Digital transformation in the 21st century has redefined traditional concepts of cultural management. In this context, museums—as institutions responsible for preserving cultural heritage—are increasingly facing environmental pressures resulting from globalization, technological change, and evolving audience needs. In response to these shifts, the concept of the "sixth-generation museum" has emerged. This new generation emphasizes pervasive smart technologies, data-driven decision-making, predictive capabilities, multidimensional interactivity, and a strong focus on user needs and preferences. Within this landscape, AI-based foresight algorithms have become key pillars in redesigning and transforming museum management structures.
       This study identifies three core functions of AI-foresight algorithms in sixth-generation museum management. First, modeling and predicting visitor behavior by integrating machine learning and big data analytics. Second, optimizing the management of cultural resources through data-driven systems and decision support tools. Third, designing personalized interactive experiences using technologies such as natural language processing (NLP), augmented reality (AR), and recommender systems. Based on these elements, a three-layer conceptual framework is proposed, consisting of the predictive-analytical layer, the smart cultural governance layer, and the user-centric experience layer. This framework demonstrates that combining foresight methodologies with artificial intelligence not only enhances museums’ adaptability to dynamic environments but also enables the development of flexible, responsive management models and offers a theoretical foundation for forward-looking cultural policymaking.
Introduction
Recent advances in digital technologies, particularly artificial intelligence, have profoundly transformed the management and visitor experience in museums. Traditional museology no longer meets the complex demands of today’s audiences, and the concept of the "sixth-generation museum" has emerged as a new approach rooted in principles such as data-centricity, foresight, interactivity, smart integration, and adaptive policymaking. Within this framework, foresight algorithms and AI-based infrastructures serve as strategic tools for enhancing decision-making processes and fostering cultural interaction. The central question of this research is the design of a conceptual framework to leverage these algorithms in improving the management of sixth-generation museums. By addressing theoretical gaps and offering an operational model, this study enables cultural institutions to advance their management processes in a smart, targeted, and localized manner, ultimately enhancing the visitor experience and promoting sustainable cultural development in the post-digital era.
       The objectives of this research focus on the role of artificial intelligence in modern museum management and include both a main goal and several sub-goals. The main goal is to analyze the role of AI-based foresight algorithms in transforming sixth-generation museum management and to design a framework for intelligent decision-making and interactive cultural experiences. The sub-goals include identifying the key components of these algorithms, examining the role of AI in optimizing the visitor experience, analyzing the challenges and opportunities of implementing such technologies in museum management, and presenting a conceptual model for integrating these technologies into museum governance.
       This research adopts a descriptive-analytical approach and is based on secondary data sources such as academic articles, technology reports, cultural policy papers, and international standards. Given the novelty of the topic in the Persian-speaking context, the study also draws on comparative research and international sources including ICOM, UNESCO, and the Horizon Report. Data were analyzed using the directed qualitative content analysis method in four stages: extracting and coding concepts, categorizing themes, intertextual analysis, and conceptual model design. The primary focus is on cultural management, digital museology, artificial intelligence, and foresight within the timeframe of 2015 to 2025. Sources were selected based on criteria such as scientific credibility, data currency, cultural diversity, and thematic relevance to the research keywords. The study views sixth-generation museums as interactive knowledge ecosystems that utilize technologies such as AI, AR/VR, the Internet of Things, and block chain to recreate cultural experiences, support intelligent decision-making, and design responsive policies.
 
Findings, Final Analysis, and Conceptual Framework Design
Foresight algorithms, relying on advanced machine learning, deep neural networks, and scenario analysis, play a vital role in identifying and forecasting emerging cultural and technological trends (Yang et al., 2021; Smith & Clark, 2019). By analyzing big data from diverse sources such as sensors, transactions, and digital feedback, these algorithms can accurately predict visitor patterns and cultural demand (Li et al., 2020). Furthermore, through cultural scenario simulations, they offer various potential trajectories for cultural transformation and museum management, thus supporting strategic decision-making (Kim & Patel, 2018). Another key feature of these algorithms is their capacity for continuous learning and model updating in response to environmental changes (Zhao et al., 2022). In addition, by networking dispersed cultural and technological data, they enable more integrated and precise analysis (Wong & Lin, 2019).
       In sixth-generation museum management, artificial intelligence plays a central role in redefining the cultural experience. This includes intelligent decision-making through big data analytics, personalized visitor experiences via recommender algorithms, multisensory interaction using technologies such as AR, VR, and voice interfaces, and real-time feedback analysis for service improvement. Key parameters in this process include behavioral, demographic, environmental, social, and cultural data gathered from various digital sources. These datasets underpin adaptive learning models that shape museum policymaking and management. Interactive cultural experiences in this new generation of museology are strengthened through content personalization, visitor participation, inclusive access, and real-time engagement. The success of this approach lies in the convergence of three domains: cultural management, artificial intelligence technologies, and strategic foresight. Supported by theories such as digital transformation, adaptive systems, cultural experience design, and strategic future studies, this research proposes a conceptual framework capable of addressing the complex cultural and technological challenges faced by future museums.
 
Practical Recommendations
Based on the results of this research, the following measures are recommended for institutions responsible for museum governance, particularly in developing countries. First, a national strategy for digital museology should be developed, grounded in artificial intelligence and foresight methodologies, to ensure a purposeful and sustainable path for digital transformation in museums. Second, establishing cultural foresight laboratories within major urban museums in collaboration with universities and research centers is essential for developing innovative and locally adapted solutions in cultural engagement. Third, the design and implementation of intelligent recommender and cultural navigation systems, based on users’ behavioral data and preferences, would enable a more personalized visitor experience. Lastly, reimagining the human resource structure in museums—with a focus on enhancing data literacy, machine learning skills, and digital competencies among staff—is crucial for aligning with the evolving demands of sixth-generation museum management.
 
Final Conclusion
The transformation toward sixth-generation museology, in response to technological and cultural shifts, requires a redefinition of the role of museums and their management approaches. This study, by focusing on AI-based foresight algorithms, proposes a conceptual framework aimed at enhancing strategic decision-making and enriching the cultural experience. The findings demonstrate that foresight algorithms play a central role in forecasting cultural trends and informing policymaking. Moreover, artificial intelligence, through the analysis of large-scale data, enables more precise decision-making and performance optimization. Technologies such as augmented reality and recommender systems further contribute to creating interactive and personalized experiences for museum audiences. These developments necessitate the reinvention of organizational structures, the enhancement of digital competencies among staff, and increased managerial agility. At the same time, ethical considerations—such as privacy protection, algorithmic transparency, and equitable access—must be integral to the design of future-oriented cultural policies.