Leveraging Educational Chatbots to Train Future Worker: A Review of Applications, Outcomes, and Challenges.

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Title: Leveraging Educational Chatbots to Train Future Worker: A Review of Applications, Outcomes, and Challenges.
Authors: Ranggana, Alfaza1, Riza, Lala Septem1 lala.s.riza@upi.edu, Abdullah, Ade Gafar1
Source: Journal of Interdisciplinary Studies in Education. 2026, Vol. 15 Issue 5, p321-353. 33p.
Abstract: This study presents a systematic literature review and bibliometric analysis examining how chatbots and virtual agents are used in educational and professional training. Drawing from 52 articles (2021-2025) and a thematic synthesis of ten empirical studies, this review addresses eight research questions concerning chatbot applications, feedback mechanisms, challenges, design features, and effectiveness. The findings show that chatbots are widely deployed as virtual patients, simulated students, and interactive tutors across medicine, teacher education, programming, and risk assessment. Personalized, real-time feedback positively affects learning outcomes, while key challenges include limited empathy, inconsistent responses, and AI overreliance. The review also maps publication trends and emerging themes around LLMs and emotionally responsive interfaces, offering a framework for educators, developers, and policymakers in the design of ethical, sustainable AI-based training interventions. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Interdisciplinary Studies in Education is the property of Journal of Interdisciplinary Studies in Education and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: Leveraging Educational Chatbots to Train Future Worker: A Review of Applications, Outcomes, and Challenges.
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  Data: <searchLink fieldCode="AR" term="%22Ranggana%2C+Alfaza%22">Ranggana, Alfaza</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Riza%2C+Lala+Septem%22">Riza, Lala Septem</searchLink><relatesTo>1</relatesTo><i> lala.s.riza@upi.edu</i><br /><searchLink fieldCode="AR" term="%22Abdullah%2C+Ade+Gafar%22">Abdullah, Ade Gafar</searchLink><relatesTo>1</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Interdisciplinary+Studies+in+Education%22">Journal of Interdisciplinary Studies in Education</searchLink>. 2026, Vol. 15 Issue 5, p321-353. 33p.
– Name: Abstract
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  Data: This study presents a systematic literature review and bibliometric analysis examining how chatbots and virtual agents are used in educational and professional training. Drawing from 52 articles (2021-2025) and a thematic synthesis of ten empirical studies, this review addresses eight research questions concerning chatbot applications, feedback mechanisms, challenges, design features, and effectiveness. The findings show that chatbots are widely deployed as virtual patients, simulated students, and interactive tutors across medicine, teacher education, programming, and risk assessment. Personalized, real-time feedback positively affects learning outcomes, while key challenges include limited empathy, inconsistent responses, and AI overreliance. The review also maps publication trends and emerging themes around LLMs and emotionally responsive interfaces, offering a framework for educators, developers, and policymakers in the design of ethical, sustainable AI-based training interventions. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Interdisciplinary Studies in Education is the property of Journal of Interdisciplinary Studies in Education and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.32674/jrw9d811
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        Text: English
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            NameFull: Riza, Lala Septem
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              Text: 2026
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