The tourist industry is undergoing a change thanks to the automation of service delivery duties brought about by the use of new technology, especially chatbots. This study examines the relationship between travellers’ assessment of the quality of electronic services and their level of digital literacy (DL). An experiment comparing a more traditional method (email) with a more innovative technology (chatbots) for service delivery was conducted. Those with higher DL rated chatbot service quality higher than those with lower DL, who favoured email due to its perceived security, empathy, reliability, and high-quality information, the data showed. Chatbots were chosen by high-DL people because they were responsive and task-competent. These results support the usage of chatbots in the travel sector for customer service. The paper also contrasts two recent studies on chatbots for tourism, examining their objectives, methodologies, conclusions, and contributions. The study provides insights into the effectiveness and usefulness of the several approaches taken to implement chatbot technology in the travel sector, highlighting both similarities and differences between them. In addition, the paper explores the impact of the COVID-19 pandemic on the travel and tourism industry, emphasizing the potential of chatbots to mitigate the spread of the virus by increasing the accessibility of travel information and minimizing touch. In order to improve customer experiences and identify gaps in the tourism industry, the research, which focuses exclusively on Mauritius, uses off-the-shelf technologies like Rasa and Telegram to construct a tourist information chatbot. With features like Google search, weather reports, and COVID-19 information, this chatbot assists travellers with trip planning by providing important details and recommendations. It is advised to create a chatbot system in addition to a smart tourism website or application to deliver effective smart tourist services. The intelligent tourist chatbot system is composed of three models: named entity recognition (NER), dialogue state tracking (DST), and question and answering (QA). The SOM-DST model was used in the study to create multi-domain DST datasets for tourism information, which included 22 slots and five domains. When combined, they achieved the target accuracy of 0.622. These developments aim to enhance personalized travel arrangements and tour guide services, which will enhance the entire visitor experience. The importance of chatbots is growing, according to recent studies, especially for users with little technical proficiency. The study’s main objective was to create a chatbot that would enhance Mauritius’ travel experience and aid with the island nation’s tourism recovery from COVID-19.