Designing an iOS intelligent tourist guide application with integrated AI services: architecture, UX, and technologies

K.A. Berezhnoi

---

Abstract. Mobile applications have become the primary channel for supporting travelers throughout all stages of their journey, while the advancement of artificial intelligence shapes user expectations for a tailored “smart guide”. However, existing travel services rarely combine deep personalization, contextaware routing, data privacy protection, and robust performance under unstable internet connection conditions.
Aim. The study aims to develop and justify an approach to designing an iOS intelligent tourist guide application with integrated AI services, including the choice of architecture, technology stack, personalization methods, and UX solutions.
Materials and methods. An analysis of 47 “Travel” applications in the App Store and 17 specialized AI services was performed; a comparative analysis of approaches to personalization and recommendation systems in tourism was conducted; a multi-tiered architecture (Clean Architecture, MVVM, SwiftUI, Core ML, cloud-based language models) was designed, and a prototype, the iOS mobile application “TripSight”, was implemented.
Results. An application architecture and a hybrid AI inference scheme have been proposed, distributing processing between local Core ML models and cloud-based language models based on criteria of latency, privacy, network availability, and request complexity. The scientific novelty and the author’s contribution have been formulated, along with recommendations for the technology stack and UX patterns; furthermore, the limitations and the scope of applicability of the proposed solution have been defined.

Keywords: mobile travel applications, iOS development, artificial intelligence, personalization, recommender systems, Core ML, SwiftUI, routing, UX/UI design, hybrid AI inference

For citation. Berezhnoi K.A. Designing an iOS intelligent tourist guide application with integrated AI services: architecture, UX, and technologies. News of the Kabardino-Balkarian Scientific Center of RAS. 2026. Vol. 28. No. 4. Pp. 46–61. DOI: 10.35330/1991-6639-2026-28-4-46-61

©  Berezhnoi K.A., 2026

References

1. Deliyannis I., Mazi I.A., Poulimenou S.M. et al. AI-Driven personalization in tourism: Enhancing visitor experience. Proceedings of the International Conference on Museum Big Data (MBD 2024), Athens, Greece, 18–19 November 2024. CEUR Workshop Proceedings. Vol. 4021. Paper 14. URL: https://ceur-ws.org/Vol-4021/paper14.pdf
2. Gavalas D., Konstantopoulos C., Mastakas K., Pantziou G. Mobile recommender systems in tourism. Journal of Network and Computer Applications. 2014. Vol. 39. P. 319–333. DOI: 10.1016/j.jnca.2013.04.006
3. Borràs J., Moreno A., Valls A. Intelligent tourism recommender systems: A survey. Expert Systems with Applications. 2014. Vol. 41. No. 16. Pp. 7370–7389. DOI: 10.1016/j.eswa.2014.06.007
4. Nikas I.A., Koutras A., Antonopoulou T. A Conceptual framework for developing and evaluating personalized tourist recommendation systems using large language models. Innovation and creativity in tourism, business and social sciences (IACuDiT 2024). Springer Proceedings in Business and Economics. Cham: Springer, 2025. DOI: 10.1007/978-3-031-78471-2_20
5. Huang H., Othman A., Muhaiyuddin N.D.M. User experience deficiencies in mobile tourism applications: A preliminary study from generation Z and tourism practitioners. International Journal of Advanced Computer Science and Applications (IJACSA). 2025. Vol. 16. No. 11. Pp. 261–270. DOI: 10.14569/IJACSA.2025.0161128
6. Li Y., Dang X., Tian H., Sun T., Wang Z., Ma L., Klein J., Bissyandé T.F. An empirical study of AI techniques in mobile applications. Journal of Systems and Software. 2024. Vol. 219. Art. 112233. DOI: 10.1016/j.jss.2024.112233
7. Kumova D.M. The use of artificial intelligence-based platforms in tourism. Servis v Rossii i za rubezhom [Services in Russia and Abroad]. Vol. 15. No. 3. Pp. 18–26. DOI: 10.24412/1995-042X-2021-3-18-26. (In Russian)
8. Lyalikova V.G., Bezryadin M.M. Development of a hybrid recommendation system. Proceedings of Voronezh State University. Series: Systems Analysis and Information Technologies. 2021. No. 4. Pp. 121–129. DOI: 10.17308/sait.2021.4/3802. (In Russian)
9. Marinin A.K. Choosing an architecture for mobile applications. News of the Kabardino-Balkarian Scientific Center of RAS. 2024. Vol. 26. No. 5. Pp. 84–93. DOI: 10.35330/1991-6639-2024-26-5-84-93. EDN: LEFHZL. (In Russian)
10. Dergachev K., Essam M.F.N. Development of an automated system for creating and testing user interface prototypes. Ergodesign. 2022. No. 4. Pp. 252–261. DOI: 10.30987/2658-4026-2022-4-252-261. (In Russian)

Information about the author

Kirill A. Berezhnoi, Master Student, Department of Informatics, Plekhanov Russian University of Economics;
36, Stremyannyy lane, Moscow, 115054, Russia;
unknownkirya@gmail.com, ORCID: https://orcid.org/0009-0000-2693-3077

Funding

The study was performed without external funding.