Evaluating the Use of Generative AI Travel Assistants in Smart Tourism through Student Feedback

2025-11-01T06:35:22+03:00

The rapid infusion of artificial intelligence into the tourism industry is reshaping service quality, operational efficiency, and the user experience. Among recent advances, generative AI–powered travel assistants can recommend destinations, build itineraries, and answer trip-planning queries through natural conversation. This paper presents a case study involving 35 hospitality management students who interacted with a generative AI travel assistant for travel planning purposes and subsequently evaluated its performance in terms of accuracy, ease of use, response speed, personalization, trust, and overall satisfaction. Findings indicate that students generally perceived the tool as helpful, fast, and user-friendly for early-stage planning. However, concerns emerged regarding the reliability of the information, occasional inconsistencies in response, and limited socio-emotional sensitivity. At the same time, participants valued generative AI for ideation and comparison, but most preferred human guidance for final decisions. Situated within the context of smart service design and smart tourism, the study offers practical implications for hospitality education and service designers. The results highlight both the opportunities and constraints of generative AI travel assistants in shaping traveler decision-making and perceived service quality.

Optimizing Predictive Maintenance in Industrial IoT Networks Using Machine Learning

2025-03-26T16:17:06+03:00

Predictive maintenance (PdM) in Industrial Internet of Things (IIoT) environments enables proactive fault detection by analyzing real-time sensor data streams. This study presents a comparative evaluation of three machine learning (ML) algorithms—Support Vector Machines (SVM), Decision Trees (DT), and Artificial Neural Networks (ANN)—implemented within a unified IIoT predictive-maintenance framework. A synthetic multivariate sensor dataset was generated using controlled fault injection, Gaussian noise modeling, and stratified sampling across multiple operating regimes. Each model was trained and validated using five-fold cross-validation to ensure statistical robustness. Model performance was assessed using accuracy, precision, recall, F1-score, and ROC-AUC, along with operational key performance indicators (KPIs) related to downtime and maintenance-cost reduction. A maintenance policy simulator mapped prediction outputs to maintenance decisions and estimated cost savings using Monte Carlo evaluation. Experimental results show that ANN achieved the highest prediction accuracy (94.8 ± 0.6%) and produced the greatest operational gains (50% downtime reduction and 32% maintenance-cost reduction), outperforming SVM and DT. The proposed architecture incorporates edge preprocessing, cloud analytics, and automated alerting, offering scalability for real-world industrial deployments. The findings demonstrate that ML-driven PdM significantly improves asset reliability and reduces operational expenditure in IIoT systems. All simulation scripts and synthetic data generators used in this study are available to support reproducibility and benchmarking.

Journal of Computers, Mechanical and Management

Evaluating the Use of Generative AI Travel Assistants in Smart Tourism through Student Feedback

Optimizing Predictive Maintenance in Industrial IoT Networks Using Machine Learning