Applying the AI Framework to Albania’s Tourism Sector: A Regional Case Study
摘要
While many industries are being transformed by Artificial Intelligence (AI), the tourism sector is undergoing rapid digitalization with AI playing a pivotal role in enhancing personalization and service delivery. AI software tools are changing the interaction tourists have with destinations, whether it be during trip planning, travel, or even commenting after the trip. However, the tourism industry does not have a comprehensive set of standards and principles to guide the principled and orderly adoption of AI technologies. This research effort proposes a construction for an AI-centered architectural framework utilizing five layers that correspond to the needs of the tourism domain to improve operational effectiveness and intelligent decision-making alongside personalized user experience. These layers are the data Layer, which receives credentials from users and platforms; the AI Processing Layer where Machine Learning and Natural Language Processing take place; the Application Layer which contains the hosting tools: the chatbot and itinerary planner; Interface/API Layer for linking chatbots and other services with tourism operators, and Governance and Security layer which provides ethical, compliance and data protection. Albania, as an emerging travel destination in South-East Europe, is used as a case study to illustrate the framework. In addition, the study introduces Explore Albania, a conceptual prototype of an AI-powered interactive web platform that engages users through gamification, user personalization, and AI assistance. This research connects the technical framework with a practical, user-facing application which, for the first time, integrates AI and tourism practice and offers a solution that is scalable, flexible, and ethically sensitive for digital transformation in tourism. Unlike existing tourism applications, this framework emphasizes modularity, ethical AI integration and end-to-end personalization, making it both scalable and adaptable to under-digitized regions.