Generative AI in Airline Tourism: Enhancing Personalization with Equity and Accessibility

SeyyedAbdolHojjat  MoghadasNian

ПРЕПРИНТ

Эта статья является препринтом и не была отрецензирована.
О результатах, изложенных в препринтах, не следует сообщать в СМИ как о проверенной информации.

Generative AI in Airline Tourism: Enhancing Personalization with Equity and Accessibility

S. MoghadasNian

2026-01-11

https://doi.org/10.24108/preprints-3114247

This study investigates how generative AI can be leveraged in airline tourism to personalize customer experiences while upholding stringent standards of equity, accessibility, and cultural sensitivity. The primary objectives are to develop fairness-aware approaches for loyalty programs, establish ethical guardrails for AI-driven pricing and travel recommendations that comply with accessibility mandates, and analyze the impact of cross-cultural preferences on itinerary fairness. Employing a mixed-methods design, the research integrates quantitative performance metrics such as improvements in customer satisfaction, reduction of algorithmic bias, and enhanced transparency indices with qualitative insights gathered from stakeholder interviews. The results demonstrate that incorporating conditional fairness constraints and explainability measures not only increases customer satisfaction by approximately 20% but also significantly reduces bias and enhances trust through a 30% improvement in transparency. These outcomes affirm that tailored personalization driven by robust equity-auditing frameworks can simultaneously boost operational efficiency and promote social inclusivity in airline tourism. Implications for theory include an enrichment of digital transformation frameworks by integrating ethical and cultural dimensions, while practical recommendations advise airline managers to deploy fairness-aware systems, establish KPI dashboards, and foster multi-stakeholder engagement to secure sustained competitive advantage.

Ссылка для цитирования:

MoghadasNian S. 2026. Generative AI in Airline Tourism: Enhancing Personalization with Equity and Accessibility. PREPRINTS.RU. https://doi.org/10.24108/preprints-3114247

Список литературы

1. Tee, M. K. A., Omar, S. I., & Mansor, N. A. (2024). The Ethical Implications of AI-Powered Personalization in Hospitality and Tourism. Advances in Hospitality, Tourism and the Services Industry (AHTSI) Book Series, 103–122. https://doi.org/10.4018/979-8-3693-6755-1.ch006

2. Aggarwal, A. (2024). Navigating the Complex Landscape of AI-Driven Personalization: Challenges and Considerations in the Generative AI Era. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10(6), 2284–2295. https://doi.org/10.32628/cseit2410612424

3. Xiang, A. (2024). Fairness & Privacy in an Age of Generative AI. Science and Technology Law Review. https://doi.org/10.52214/stlr.v25i2.12765.

4. Palmer, Z., & Oswal, S. (2024). Constructing Websites with Generative AI Tools: The Accessibility of Their Workflows and Products for Users With Disabilities. Journal of Business and Technical Communication. https://doi.org/10.1177/10506519241280644.

5. Semwal, R., Tripathi, N., Rana, A., Dafouti, B. S., Bairwa, M. K., & Mathur, V. (2024). AI-Powered Personalization and Emotional Intelligence Integration for Enhanced Service Marketing in Transformative Tourism Experiences. https://doi.org/10.1109/ic3se62002.2024.10592909

6. Suanpang, P., & Pothipassa, P. (2024). Integrating Generative AI and IoT for Sustainable Smart Tourism Destinations. Sustainability. https://doi.org/10.3390/su16177435.

7. Moghadasnian, S. (2023). Excellence in the Air: The Chief Customer Services Officer's (CCSO) Definitive KPI Guide in the Airline Industry [Digital edition]. Maximizing Customer Satisfaction Through Effective Utilization of Key Performance Indicators. Aviation and Tourism Research and Innovation Center (ATRIC). Tehran, Iran & Milan, Italy.

8. Moghadasnian, S. (2022). Flight to Excellence: A Comprehensive Guide to Key Performance Indicators in the Airline Industry – Unlocking Success Through Data-Driven Strategies and Performance Metrics [Digital publication]. Aviation and Tourism Research and Innovation Center (ATRIC). Tehran, Iran & Milan, Italy.

9. Moghadasnian, S. (2023). Strategica Aeronautica: Mastering KPI-Driven Leadership Across the Airline and Tourism Ecosystem – A Comprehensive Guide for Executives: From Analytic Hierarchy Process to Zero-Based Budgeting, Navigate the Full Spectrum of Strategic Decision-Making Metrics [Digital publication]. Aviation and Tourism Research and Innovation Center (ATRIC). Tehran, Iran & Milan, Italy.

10. MoghadasNian, S. (2025). AI-Driven Inventory Optimization in Airline Logistics: Enhancing Efficiency, Sustainability, and Operational Performance. In Proceedings of the International Conference on Artificial Intelligence in the Age of Digital Transformation. International Organization of Academic Studies, Tbilisi, Georgia. English.

11. MoghadasNian, S. (2025). AI-Powered Predictive Maintenance in Aviation Operations. In Proceedings of the 16th International Conference on Advanced Research in Science, Engineering and Technology. Arvin Alborz Conference Organizer, Bern, Switzerland. Language: English.

12. Moghadasnian, S. (2024). Tourism 4.0 in Iran: Navigating the Digital Transformation for Sustainable and Inclusive Growth. International Journal of Innovative Research in Humanities, 4(3), 77–84. ISSN: 2783-2058.

13. Moghadasnian, S. (2024). Optimization Strategy for Pilgrimage Tourism: Developing a Key Performance Indicators Framework Based on Industry Best Practices and Integrating Data-Driven Management Insights into Religious Tourism Experience. In Proceedings of the First National Conference on Planning and Development of Tourism, the Development and Facilitation of Pilgrimage and Tourism. Ferdowsi University of Mashhad, Mashhad, Iran. Language: Farsi.

14. Moghadasnian, S., & Moghadasnian, S. M. (2023). Optimizing Airline Health Tourism Services: A Strategic KPI Approach. Enhancing Patient Journeys and Operational Excellence. In Proceedings of the First National Conference on Tourism, Culture, Civilization and History. Lorestan University, Lorestan, Iran. Language: Farsi.

15. Moghadasnian, S. (2014). Pilgrimage in the Skies: The Ultimate KPI Guide for the Religious Pilgrimage Tourism Director (RPTD) – Strategically Leveraging Key Performance Indicators for Enhancing Spiritual Tourism in the Airline Industry [Digital publication]. Aviation and Tourism Research and Innovation Center (ATRIC). Tehran, Iran & Milan, Italy.

16. Moghadasnian, S. (2014). Healing Journeys: The Essential KPI Guide for the Health Tourism Director in the Airline Industry. Utilizing Key Performance Indicators to Enhance Patient Experience and Streamline Medical Travel [Digital publication]. Aviation and Tourism Research and Innovation Center (ATRIC). Tehran, Iran & Milan, Italy.

17. Moghadasnian, S. A. (2015). Flying with Technology: A Guide to Key Performance Indicators in Airline IT [Digital edition]. Technology at the Helm: Impactful IT Metrics in the Airline Industry. Aviation and Tourism Research and Innovation Center (ATRIC). Tehran, Iran & Milan, Italy.

18. Cheng, C., Ruess, H., Wu, C., & Zhao, X. (2024). Conditional Fairness for Generative AIs.

19. Acosta-Vargas, P., Salvador-Acosta, B., Novillo-Villegas, S., Sarantis, D., & Salvador-Ullauri, L. (2024). Generative Artificial Intelligence and Web Accessibility: Towards an Inclusive and Sustainable Future. Emerging Science Journal. https://doi.org/10.28991/esj-2024-08-04-021.

20. Fakfare, P., Manosuthi, N., Lee, J., Han, H., & Jin, M. (2025). Customer word-of-mouth for generative AI: Innovation and adoption in hospitality and tourism. International Journal of Hospitality Management. https://doi.org/10.1016/j.ijhm.2024.104070.

21. Ilieva, G., Yankova, T., & Klisarova-Belcheva, S. (2024). Effects of Generative AI in Tourism Industry. Information. https://doi.org/10.3390/info15110671.

ПРЕПРИНТ

ИНФОРМАЦИЯ

Использование куки-файлов