The system dynamics applied in this research on modeling a tourist destination (area) life cycle (TALC) contributes to understanding its behavior and the way that information feedback governs the use of feedback loops, delays and stocks and flows. On this basis, a system dynamic three-staged TALC model is conceptualized, with the number of visitors V as an indicator of the carrying capacities’ dynamics and the flow function V(t) to determine the TALC stages. In the first supply-dominance stage, the model indicated that arrivals are growing until the point of inflexion. After this point, arrivals continue growing (but with diminishing growth rates), indicating the beginning of the demand-dominance stage, ending up with the saturation point, i.e., the maximum number of visitors. The simulated TALC system dynamics model was then applied to five EU destinations (Living Labs) to explain their development along the observed period (2007–2019). The analysis revealed that all observed Living Labs reached the second lifecycle stage, with one entered as early as in 2015 and another in 2018. Lifecycle stage durations may significantly differ across the destinations, as do the policies used either to prevent stagnation or to restructure the offer to become more sustainable and resilient.

There is minimal research concerning the role of custom-trained artificial intelligence (AI) tools in higher education, with a lack of research concerning the pedagogical qualities of an AI-based personal tutor. To fill this literature gap, we examined how a custom GPT personal tutor shapes key teaching and learning qualities. Using the mixed-methods approach, we aimed to demonstrate preliminary and exploratory empirical evidence concerning the contribution of custom-trained AI tutors to building up students’ competencies. Our research analyzed the subjective assessments of students related to the GPT tutor’s contribution to improving their competencies. Both the qualitative and quantitative empirical results confirmed the positive contribution. In addition, we triangulated the results to evaluate the potential of custom-trained AI chatbots in higher education, focusing on undergraduate business courses. However, the results of this study cannot be generalized to the entire student population of business schools, since the participation in the AI-assisted tutor program was voluntary, attracting only intrinsically motivated students.

Višegodišnja elektronička obrada podataka omogućila je pohranu velike količine podataka koja se danas može iskoristiti za poboljšanje procesa u obrazovanju putem algoritama strojnog učenja. Koristeći se podacima iz sustava Moodle za učenje na daljinu...

Purpose – touristic destinations develop over time, which is why, in order to get a comprehensive picture of their development, it is necessary to observe it's dynamics. Methodology – in this paper system dynamics methodology and of DPSIR framework will use. In order to model reasoning behind the TALC behaviour, presented research in this paper leans on TALC logistic curve. Findings – deeper analysis of the causes and/or consequences elements of destination (sub)system (supply and demand) will indicate way of affect touristic area life cycle dynamics. Contribution – better understanding of the background structure of TALC pattern behaviour may help destination managers/planners to bring appropriate policies to move destination’s sustainability towards higher level of organisation.