Challenges and prospects of artificial intelligence in aviation: a bibliometric study
The primary motivation for this study is the recent growth and increased interest in artificial intelligence (AI). Despite the widespread recognition of its critical importance, a discernible scientific gap persists within the extant scholarly discourse, particularly concerning exhaustive systematic...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co. Ltd.
2025-06-01
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| Series: | Data Science and Management |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666764924000626 |
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| author | Nuno Moura Lopes Manuela Aparicio Fátima Trindade Neves |
| author_facet | Nuno Moura Lopes Manuela Aparicio Fátima Trindade Neves |
| author_sort | Nuno Moura Lopes |
| collection | DOAJ |
| description | The primary motivation for this study is the recent growth and increased interest in artificial intelligence (AI). Despite the widespread recognition of its critical importance, a discernible scientific gap persists within the extant scholarly discourse, particularly concerning exhaustive systematic reviews of AI in the aviation industry. This gap spurred a meticulous analysis of 1,213 articles from the Web of Science (WoS) core database for bibliometric knowledge mapping. This analysis highlights China as the primary contributor to publications, with the Nanjing University of Finance and Economics as the leading institution in paper contributions. Lecture Notes in Artificial Intelligence and the IEEE AIAA Digital Avionics System Conference are the leading journals within this domain. This bibliometric research underscores the key focus on air traffic management, human factors, environmental initiatives, training, logistics, flight operations, and safety through co-occurrence and co-citation analyses. A chronological examination of keywords reveals a central research trajectory centered on machine learning, models, deep learning, and the impact of automation on human performance in aviation. Burst keyword analysis identifies the leading-edge research on AI within predictive models, unmanned aerial vehicles, object detection, and convolutional neural networks. The primary objective is to bridge this knowledge gap and gain comprehensive insights into AI in the aviation sector. This study delineates the scholarly terrain of AI in aviation using a bibliometric methodology to facilitate this exploration. The results illuminate the current state of research, thereby enhancing academic understanding of developments within this critical domain. Finally, a new conceptual framework was constructed based on the primary elements identified in the literature. This framework can assist emerging researchers in identifying the fundamental dimensions of AI in the aviation industry. |
| format | Article |
| id | doaj-art-465f9c5701e0416fa64d4f9b7e6a2b33 |
| institution | OA Journals |
| issn | 2666-7649 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Data Science and Management |
| spelling | doaj-art-465f9c5701e0416fa64d4f9b7e6a2b332025-08-20T02:37:35ZengKeAi Communications Co. Ltd.Data Science and Management2666-76492025-06-018220722310.1016/j.dsm.2024.11.001Challenges and prospects of artificial intelligence in aviation: a bibliometric studyNuno Moura Lopes0Manuela Aparicio1Fátima Trindade Neves2Corresponding author.; NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, 1070-312, PortugalNOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, 1070-312, PortugalNOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, 1070-312, PortugalThe primary motivation for this study is the recent growth and increased interest in artificial intelligence (AI). Despite the widespread recognition of its critical importance, a discernible scientific gap persists within the extant scholarly discourse, particularly concerning exhaustive systematic reviews of AI in the aviation industry. This gap spurred a meticulous analysis of 1,213 articles from the Web of Science (WoS) core database for bibliometric knowledge mapping. This analysis highlights China as the primary contributor to publications, with the Nanjing University of Finance and Economics as the leading institution in paper contributions. Lecture Notes in Artificial Intelligence and the IEEE AIAA Digital Avionics System Conference are the leading journals within this domain. This bibliometric research underscores the key focus on air traffic management, human factors, environmental initiatives, training, logistics, flight operations, and safety through co-occurrence and co-citation analyses. A chronological examination of keywords reveals a central research trajectory centered on machine learning, models, deep learning, and the impact of automation on human performance in aviation. Burst keyword analysis identifies the leading-edge research on AI within predictive models, unmanned aerial vehicles, object detection, and convolutional neural networks. The primary objective is to bridge this knowledge gap and gain comprehensive insights into AI in the aviation sector. This study delineates the scholarly terrain of AI in aviation using a bibliometric methodology to facilitate this exploration. The results illuminate the current state of research, thereby enhancing academic understanding of developments within this critical domain. Finally, a new conceptual framework was constructed based on the primary elements identified in the literature. This framework can assist emerging researchers in identifying the fundamental dimensions of AI in the aviation industry.http://www.sciencedirect.com/science/article/pii/S2666764924000626Artificial intelligenceScientific mappingKnowledge mappingScientific framework |
| spellingShingle | Nuno Moura Lopes Manuela Aparicio Fátima Trindade Neves Challenges and prospects of artificial intelligence in aviation: a bibliometric study Data Science and Management Artificial intelligence Scientific mapping Knowledge mapping Scientific framework |
| title | Challenges and prospects of artificial intelligence in aviation: a bibliometric study |
| title_full | Challenges and prospects of artificial intelligence in aviation: a bibliometric study |
| title_fullStr | Challenges and prospects of artificial intelligence in aviation: a bibliometric study |
| title_full_unstemmed | Challenges and prospects of artificial intelligence in aviation: a bibliometric study |
| title_short | Challenges and prospects of artificial intelligence in aviation: a bibliometric study |
| title_sort | challenges and prospects of artificial intelligence in aviation a bibliometric study |
| topic | Artificial intelligence Scientific mapping Knowledge mapping Scientific framework |
| url | http://www.sciencedirect.com/science/article/pii/S2666764924000626 |
| work_keys_str_mv | AT nunomouralopes challengesandprospectsofartificialintelligenceinaviationabibliometricstudy AT manuelaaparicio challengesandprospectsofartificialintelligenceinaviationabibliometricstudy AT fatimatrindadeneves challengesandprospectsofartificialintelligenceinaviationabibliometricstudy |