Determining university’s readiness to implement AI technologies for personalizing educational paths
The key issue of the article is to determine readiness of HEIs to implement artificial intelligence and machine learning technologies for personalization of students’ individual educational trajectories (hereinafter – IET). In case of compliance of HEIs with the proposed groups of factors, including...
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| Format: | Article |
| Language: | English |
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Publishing House of the State University of Management
2023-12-01
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| Series: | Вестник университета |
| Subjects: | |
| Online Access: | https://vestnik.guu.ru/jour/article/view/4811 |
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| _version_ | 1832541549556137984 |
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| author | V. S. Starostin K. A. Arzhanova D. V. Dolgopolov A. D. Ditrikh |
| author_facet | V. S. Starostin K. A. Arzhanova D. V. Dolgopolov A. D. Ditrikh |
| author_sort | V. S. Starostin |
| collection | DOAJ |
| description | The key issue of the article is to determine readiness of HEIs to implement artificial intelligence and machine learning technologies for personalization of students’ individual educational trajectories (hereinafter – IET). In case of compliance of HEIs with the proposed groups of factors, including developed information architecture, there is an opportunity to use multidimensional structured and unstructured data more technologically to improve various types of university activities. High-tech marketing analytical tools facilitate the collection of contextual data and insights into various processes within university, as well as in-depth analysis of learners’ digital footprints. Methods of machine learning, predictive analytics, and modern generative neural networks allow to create recommendation services, with the help of which individual educational trajectories are formed by machine intelligence, simultaneously considering hundreds of parameters. Beyond the tasks of IET formation, machine intelligence can successfully solve tasks for other stakeholders of the university such as professors, researchers, and administration. |
| format | Article |
| id | doaj-art-5e86b092a62049559fd7d01aba13f4ac |
| institution | Kabale University |
| issn | 1816-4277 2686-8415 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Publishing House of the State University of Management |
| record_format | Article |
| series | Вестник университета |
| spelling | doaj-art-5e86b092a62049559fd7d01aba13f4ac2025-02-04T08:28:18ZengPublishing House of the State University of ManagementВестник университета1816-42772686-84152023-12-01010293910.26425/1816-4277-2023-10-29-392912Determining university’s readiness to implement AI technologies for personalizing educational pathsV. S. Starostin0K. A. Arzhanova1D. V. Dolgopolov2A. D. Ditrikh3State University of ManagementState University of ManagementState University of ManagementState University of ManagementThe key issue of the article is to determine readiness of HEIs to implement artificial intelligence and machine learning technologies for personalization of students’ individual educational trajectories (hereinafter – IET). In case of compliance of HEIs with the proposed groups of factors, including developed information architecture, there is an opportunity to use multidimensional structured and unstructured data more technologically to improve various types of university activities. High-tech marketing analytical tools facilitate the collection of contextual data and insights into various processes within university, as well as in-depth analysis of learners’ digital footprints. Methods of machine learning, predictive analytics, and modern generative neural networks allow to create recommendation services, with the help of which individual educational trajectories are formed by machine intelligence, simultaneously considering hundreds of parameters. Beyond the tasks of IET formation, machine intelligence can successfully solve tasks for other stakeholders of the university such as professors, researchers, and administration.https://vestnik.guu.ru/jour/article/view/4811artificial intelligencemarketinguniversitypersonalizationmachine learningindividual educational trajectories |
| spellingShingle | V. S. Starostin K. A. Arzhanova D. V. Dolgopolov A. D. Ditrikh Determining university’s readiness to implement AI technologies for personalizing educational paths Вестник университета artificial intelligence marketing university personalization machine learning individual educational trajectories |
| title | Determining university’s readiness to implement AI technologies for personalizing educational paths |
| title_full | Determining university’s readiness to implement AI technologies for personalizing educational paths |
| title_fullStr | Determining university’s readiness to implement AI technologies for personalizing educational paths |
| title_full_unstemmed | Determining university’s readiness to implement AI technologies for personalizing educational paths |
| title_short | Determining university’s readiness to implement AI technologies for personalizing educational paths |
| title_sort | determining university s readiness to implement ai technologies for personalizing educational paths |
| topic | artificial intelligence marketing university personalization machine learning individual educational trajectories |
| url | https://vestnik.guu.ru/jour/article/view/4811 |
| work_keys_str_mv | AT vsstarostin determininguniversitysreadinesstoimplementaitechnologiesforpersonalizingeducationalpaths AT kaarzhanova determininguniversitysreadinesstoimplementaitechnologiesforpersonalizingeducationalpaths AT dvdolgopolov determininguniversitysreadinesstoimplementaitechnologiesforpersonalizingeducationalpaths AT additrikh determininguniversitysreadinesstoimplementaitechnologiesforpersonalizingeducationalpaths |