TY - GEN
T1 - Leveraging AI to Transform Rail Higher Education
T2 - 20th European Dependable Computing Conference Companion, EDCC-C 2025
AU - Shinde, Prachiti
AU - Marinov, Marin
AU - Hadeed, Reem
N1 - Copyright ©2025 IEEE. All rights reserved, including rights for text and data mining and training of artificial intelligence and similar technologies. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
PY - 2025/9/3
Y1 - 2025/9/3
N2 - The rapid digital transformation, an aging workforce, and persistent skills shortages in rail industry has highlighted the need for railway higher education program that remains relevant and aligned with industry requirement. This paper explores the integration of Artificial Intelligence (AI) in higher education, specifically within the context of rail education. This paper proposes developing of an evaluation tool for railway programmes' curricula that integrates AI-driven methods. Employing data-driven analytics and predictive modelling, the tool will examine curriculum relevance, skills coverage, and the integration of emerging technologies. These results will underscore the potential of AI to revolutionise curriculum design and evaluation, bridging the gap between university provision and industry demands.
AB - The rapid digital transformation, an aging workforce, and persistent skills shortages in rail industry has highlighted the need for railway higher education program that remains relevant and aligned with industry requirement. This paper explores the integration of Artificial Intelligence (AI) in higher education, specifically within the context of rail education. This paper proposes developing of an evaluation tool for railway programmes' curricula that integrates AI-driven methods. Employing data-driven analytics and predictive modelling, the tool will examine curriculum relevance, skills coverage, and the integration of emerging technologies. These results will underscore the potential of AI to revolutionise curriculum design and evaluation, bridging the gap between university provision and industry demands.
KW - AI in education
KW - curriculum evaluation
KW - rail higher education
KW - tool development
UR - https://ieeexplore.ieee.org/document/11144860
UR - http://www.scopus.com/inward/record.url?scp=105017671498&partnerID=8YFLogxK
U2 - 10.1109/EDCC-C66476.2025.00044
DO - 10.1109/EDCC-C66476.2025.00044
M3 - Conference publication
AN - SCOPUS:105017671498
SN - 9798331537425
T3 - Proceedings - 2025 20th European Dependable Computing Conference Companion Proceedings, EDCC-C 2025
SP - 125
EP - 129
BT - 2025 20th European Dependable Computing Conference Companion Proceedings (EDCC-C)
PB - IEEE
Y2 - 8 April 2025 through 11 April 2025
ER -