GENERATIVE AI-DRIVEN TROUBLESHOOTING FOR TELECOMMUNICATIONS SERVICE EXPERIMENTATION IN EDUCATIONAL ENVIRONMENTS

Authors

  • Ing. Pedro Julio Cairo Martínez Universidad Técnica de La Habana "José Antonio Echeverría", CUJAE

Abstract

Generative artificial intelligence has significantly transformed the teaching process in higher education regarding the roles of professors and students in teaching and learning methodologies. Its impact necessitates new approaches to strategically guide it toward more formative uses. The Telecommunications Networks profile of Plan E in the Telecommunications and Electronics Engineering program at CUJAE includes, among its objectives, problem-solving at the foundational level of the profession. A key element in developing professional competencies within this profile has been the troubleshooting during experimentation with telecommunication services. By leveraging generative artificial intelligence to support this aspect, the benefit of this technique as a teaching resource was demonstrated. Experiments were conducted with VoIP services using Asterisk and Linphone, as well as data storage services using Nextcloud, both deployed on virtual machines in VirtualBox. For troubleshooting, the generative AI tools Perplexity (paid version) and DeepSeek (free version) were employed. The study was applied to a sample of 20 teams of projects developed by teams of up to four students, each in the Telecommunications Networks II course of the Telecommunications and Electronics Engineering program at CUJAE during the 2024-2025 academic year.

Index terms: troubleshooting, generative artificial inteligence, telecommunication networks.

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Published

2025-09-21

How to Cite

Cairo Martínez, P. J. (2025). GENERATIVE AI-DRIVEN TROUBLESHOOTING FOR TELECOMMUNICATIONS SERVICE EXPERIMENTATION IN EDUCATIONAL ENVIRONMENTS. Telemática, 23, 170–176. Retrieved from https://revistatelematica.cujae.edu.cu/index.php/tele/article/view/1001

Issue

Vol. 23 (2024): Vol. 23 (2024)

Section

Papers

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