Publicação
Implementing and improving a chatbot for tourism
| Resumo: | As travelers increasingly seek personalized and seamless experiences, the tourism industry is undergoing rapid digital transformation, with artificial intelligence playing a central role in shaping new solutions. Among these, chatbots have emerged as a promising tool for simplifying travel planning by offering users tailored recommendations through natural, conversational interfaces. Instead of navigating complex menus across multiple websites, users can now access real-time suggestions in a more intuitive and efficient way. However, while some travel chatbots have shown potential, many continue to face challenges such as limited contextual awareness, fragmented integration with external services, and an inability to adapt to dynamic user preferences. Despite growing interest in AI-driven applications within tourism, there remains a noticeable gap in the literature concerning the development of chatbots that are truly capable of providing intelligent, personalized travel assistance. Most existing systems fall short when it comes to delivering real-time, context-sensitive guidance that aligns with users’ specific needs. Addressing this gap is crucial for improving both user satisfaction and the operational effectiveness of digital travel platforms. The primary objective of this research is to design, implement, and evaluate a chatbot capable of generating user-specific travel recommendations by leveraging natural language processing, machine learning techniques, and live external data sources. The intention is not merely to build another travel assistant, but to create a system that simplifies the entire planning process through a single, coherent conversation, making the experience faster, more intuitive, and better aligned with individual users goals. To achieve this, the study involved the development of a working prototype that interprets user input and dynamically pulls relevant data to suggest personalized travel options. The chatbot was tested with users to assess its usability and ability to provide meaningful, context-aware assistance. These evaluations revealed that users found the conversational format both engaging and efficient. Importantly, the chatbot demonstrated the capacity to integrate various services and refine its recommendations over time based on user interactions. The findings of this study suggest that AI-powered chatbots can play a transformative role in modern tourism by enhancing customer engagement, improving decision-making, and streamlining operations. For travelers, the technology offers a more responsive and customized planning experience. For tourism stakeholders, it presents opportunities to harness user data for better service design and resource allocation. Ultimately, this research contributes to a broader understanding of how AI can be effectively integrated into tourism ecosystems, paving the way for more intelligent, user-centered travel experiences. |
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| Autores principais: | Marcos, Carolina Jacobson |
| Assunto: | Chatbots Tourism AI in Tourism Conversational Agent Dialogflow Essentials SDG 9 - Industry, innovation and infrastructure |
| Ano: | 2025 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | As travelers increasingly seek personalized and seamless experiences, the tourism industry is undergoing rapid digital transformation, with artificial intelligence playing a central role in shaping new solutions. Among these, chatbots have emerged as a promising tool for simplifying travel planning by offering users tailored recommendations through natural, conversational interfaces. Instead of navigating complex menus across multiple websites, users can now access real-time suggestions in a more intuitive and efficient way. However, while some travel chatbots have shown potential, many continue to face challenges such as limited contextual awareness, fragmented integration with external services, and an inability to adapt to dynamic user preferences. Despite growing interest in AI-driven applications within tourism, there remains a noticeable gap in the literature concerning the development of chatbots that are truly capable of providing intelligent, personalized travel assistance. Most existing systems fall short when it comes to delivering real-time, context-sensitive guidance that aligns with users’ specific needs. Addressing this gap is crucial for improving both user satisfaction and the operational effectiveness of digital travel platforms. The primary objective of this research is to design, implement, and evaluate a chatbot capable of generating user-specific travel recommendations by leveraging natural language processing, machine learning techniques, and live external data sources. The intention is not merely to build another travel assistant, but to create a system that simplifies the entire planning process through a single, coherent conversation, making the experience faster, more intuitive, and better aligned with individual users goals. To achieve this, the study involved the development of a working prototype that interprets user input and dynamically pulls relevant data to suggest personalized travel options. The chatbot was tested with users to assess its usability and ability to provide meaningful, context-aware assistance. These evaluations revealed that users found the conversational format both engaging and efficient. Importantly, the chatbot demonstrated the capacity to integrate various services and refine its recommendations over time based on user interactions. The findings of this study suggest that AI-powered chatbots can play a transformative role in modern tourism by enhancing customer engagement, improving decision-making, and streamlining operations. For travelers, the technology offers a more responsive and customized planning experience. For tourism stakeholders, it presents opportunities to harness user data for better service design and resource allocation. Ultimately, this research contributes to a broader understanding of how AI can be effectively integrated into tourism ecosystems, paving the way for more intelligent, user-centered travel experiences. |
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