Ethical Dilemmas in the Integration of Artificial Intelligence in ESL Education Within Chinese College Settings: A Systematic Review
The research on the moral conundrums linked with using AI in ESL instruction in Chinese higher education is checked critically in this systematic review. Web of Science, Science Direct, and Springer were among the data sources for this review. The paper summarizes research on the difficulties AI has...
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2-s2.0-85215665755 Shi J.; Narasuman S.; Ning H.; Grigoryan G.; Ren W. Ethical Dilemmas in the Integration of Artificial Intelligence in ESL Education Within Chinese College Settings: A Systematic Review 2024 ACM International Conference Proceeding Series 10.1145/3700297.3700357 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215665755&doi=10.1145%2f3700297.3700357&partnerID=40&md5=bad145466a2e5b8a7cb55e0d3a59902d The research on the moral conundrums linked with using AI in ESL instruction in Chinese higher education is checked critically in this systematic review. Web of Science, Science Direct, and Springer were among the data sources for this review. The paper summarizes research on the difficulties AI has while reading ESL text, emphasizing the need for high-quality training data and algorithm development. Although AI has the ability to provide individualized learning experiences, there are unknowns when it comes to data that is outside of its training set. The review highlights the need for more thorough research by exposing a lack of high-quality evidence on the results of AI integration. While AI is recognized for its ability to track student progress and provide feedback, the effectiveness of these efforts depends on the choice of suitable AI-driven learning models. The findings suggest an imperative for enhanced methodologies and a call for educators to judiciously implement AI in ESL education, addressing ethical considerations and ensuring the quality of student learning experiences. © 2024 Copyright held by the owner/author(s). Association for Computing Machinery English Conference paper |
author |
Shi J.; Narasuman S.; Ning H.; Grigoryan G.; Ren W. |
spellingShingle |
Shi J.; Narasuman S.; Ning H.; Grigoryan G.; Ren W. Ethical Dilemmas in the Integration of Artificial Intelligence in ESL Education Within Chinese College Settings: A Systematic Review |
author_facet |
Shi J.; Narasuman S.; Ning H.; Grigoryan G.; Ren W. |
author_sort |
Shi J.; Narasuman S.; Ning H.; Grigoryan G.; Ren W. |
title |
Ethical Dilemmas in the Integration of Artificial Intelligence in ESL Education Within Chinese College Settings: A Systematic Review |
title_short |
Ethical Dilemmas in the Integration of Artificial Intelligence in ESL Education Within Chinese College Settings: A Systematic Review |
title_full |
Ethical Dilemmas in the Integration of Artificial Intelligence in ESL Education Within Chinese College Settings: A Systematic Review |
title_fullStr |
Ethical Dilemmas in the Integration of Artificial Intelligence in ESL Education Within Chinese College Settings: A Systematic Review |
title_full_unstemmed |
Ethical Dilemmas in the Integration of Artificial Intelligence in ESL Education Within Chinese College Settings: A Systematic Review |
title_sort |
Ethical Dilemmas in the Integration of Artificial Intelligence in ESL Education Within Chinese College Settings: A Systematic Review |
publishDate |
2024 |
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ACM International Conference Proceeding Series |
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doi_str_mv |
10.1145/3700297.3700357 |
url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215665755&doi=10.1145%2f3700297.3700357&partnerID=40&md5=bad145466a2e5b8a7cb55e0d3a59902d |
description |
The research on the moral conundrums linked with using AI in ESL instruction in Chinese higher education is checked critically in this systematic review. Web of Science, Science Direct, and Springer were among the data sources for this review. The paper summarizes research on the difficulties AI has while reading ESL text, emphasizing the need for high-quality training data and algorithm development. Although AI has the ability to provide individualized learning experiences, there are unknowns when it comes to data that is outside of its training set. The review highlights the need for more thorough research by exposing a lack of high-quality evidence on the results of AI integration. While AI is recognized for its ability to track student progress and provide feedback, the effectiveness of these efforts depends on the choice of suitable AI-driven learning models. The findings suggest an imperative for enhanced methodologies and a call for educators to judiciously implement AI in ESL education, addressing ethical considerations and ensuring the quality of student learning experiences. © 2024 Copyright held by the owner/author(s). |
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Association for Computing Machinery |
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English |
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Conference paper |
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scopus |
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Scopus |
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1823296153634996224 |