Exploring technological evolution of AIED field using topic modelling and link prediction analysis based on patent data
This study aims to analyze the artificial intelligence in education (AIED) field using patent data to identify the key actors, main themes, and their evolution over time. The study also aims to predict potential innovative technological development areas that may emerge in the AIED field in the futu...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Sustainable Futures |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666188825006033 |
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| author | Hüseyin Özçınar Aylin Sabancı Bayramoğlu |
| author_facet | Hüseyin Özçınar Aylin Sabancı Bayramoğlu |
| author_sort | Hüseyin Özçınar |
| collection | DOAJ |
| description | This study aims to analyze the artificial intelligence in education (AIED) field using patent data to identify the key actors, main themes, and their evolution over time. The study also aims to predict potential innovative technological development areas that may emerge in the AIED field in the future. Descriptive statistics were used to investigate the current actors in the field and the changes in their contributions. Technological developments in the field were attempted to be revealed using topic modelling and IPC code pair trend analysis methods. To obtain predictions about the future of the field, the IPC co-occurrence network was analyzed using link prediction methods. Research results indicate that educational robots, characterized by personalized interaction and social engagement capabilities, are among the most frequently patented technologies in the AIED field. Similarly, innovative foreign language teaching materials, notably speech recognition and interactive learning tools, also emerge as prominent areas of technological innovation. Additionally, artificial intelligence (AI) technologies have been increasingly integrated into educational management, particularly in areas such as admission processes, performance tracking, and resource management. These AI-driven management innovations enable institutions to operate more sustainably and efficiently by optimizing resource allocation and reducing administrative burdens. The use of AI is also expected to find application in various innovative forms in foreign language instruction. |
| format | Article |
| id | doaj-art-aeee77c93e6f41eda99c4e40eba54edc |
| institution | Kabale University |
| issn | 2666-1888 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Sustainable Futures |
| spelling | doaj-art-aeee77c93e6f41eda99c4e40eba54edc2025-08-20T03:56:50ZengElsevierSustainable Futures2666-18882025-12-011010103910.1016/j.sftr.2025.101039Exploring technological evolution of AIED field using topic modelling and link prediction analysis based on patent dataHüseyin Özçınar0Aylin Sabancı Bayramoğlu1Department of Computer Education and Instructional Technology, Pamukkale University, Denizli, Turkey; Corresponding author.Department of Management Information Systems, Pamukkale University, Denizli, TurkeyThis study aims to analyze the artificial intelligence in education (AIED) field using patent data to identify the key actors, main themes, and their evolution over time. The study also aims to predict potential innovative technological development areas that may emerge in the AIED field in the future. Descriptive statistics were used to investigate the current actors in the field and the changes in their contributions. Technological developments in the field were attempted to be revealed using topic modelling and IPC code pair trend analysis methods. To obtain predictions about the future of the field, the IPC co-occurrence network was analyzed using link prediction methods. Research results indicate that educational robots, characterized by personalized interaction and social engagement capabilities, are among the most frequently patented technologies in the AIED field. Similarly, innovative foreign language teaching materials, notably speech recognition and interactive learning tools, also emerge as prominent areas of technological innovation. Additionally, artificial intelligence (AI) technologies have been increasingly integrated into educational management, particularly in areas such as admission processes, performance tracking, and resource management. These AI-driven management innovations enable institutions to operate more sustainably and efficiently by optimizing resource allocation and reducing administrative burdens. The use of AI is also expected to find application in various innovative forms in foreign language instruction.http://www.sciencedirect.com/science/article/pii/S2666188825006033Artificial intelligenceEducationPatent dataLink predictionTopic modelling |
| spellingShingle | Hüseyin Özçınar Aylin Sabancı Bayramoğlu Exploring technological evolution of AIED field using topic modelling and link prediction analysis based on patent data Sustainable Futures Artificial intelligence Education Patent data Link prediction Topic modelling |
| title | Exploring technological evolution of AIED field using topic modelling and link prediction analysis based on patent data |
| title_full | Exploring technological evolution of AIED field using topic modelling and link prediction analysis based on patent data |
| title_fullStr | Exploring technological evolution of AIED field using topic modelling and link prediction analysis based on patent data |
| title_full_unstemmed | Exploring technological evolution of AIED field using topic modelling and link prediction analysis based on patent data |
| title_short | Exploring technological evolution of AIED field using topic modelling and link prediction analysis based on patent data |
| title_sort | exploring technological evolution of aied field using topic modelling and link prediction analysis based on patent data |
| topic | Artificial intelligence Education Patent data Link prediction Topic modelling |
| url | http://www.sciencedirect.com/science/article/pii/S2666188825006033 |
| work_keys_str_mv | AT huseyinozcınar exploringtechnologicalevolutionofaiedfieldusingtopicmodellingandlinkpredictionanalysisbasedonpatentdata AT aylinsabancıbayramoglu exploringtechnologicalevolutionofaiedfieldusingtopicmodellingandlinkpredictionanalysisbasedonpatentdata |