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Artificial Intelligence-Virtual Trainer: Innovative Didactics Aimed at Personalized Training Needs
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First, the benefits of AI-based training for business development are presented. Then, the theoretical literature on various stages from traditional training to AI-based training is analyzed. Finally, we analyze AI applications in the training process and managerial response. This study presents a framework for the application of artificial intelligence (AI) technology to training and managerial challenges. AI tools can be applied to the training process, including knowledge management (KM), needs analysis, training organization, and results feedback. AI-based training transforms organizations into knowledge organizations that can meet the demands of personalized training and improve learning quality. AI tools bring about a shift in the training phases of knowledge base creation, needs surveys, the organization of training, and feedback on results.
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“Such on-the-fly adjustment not only results in better learning but also prevents cognitive burnout, such that the trainee is able to maintain high performance over extended training periods [217].”
From Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis (2024) · cites this paper as [217]
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Reddy, V. S., Preethi, K. Y. S., Mounika, T., & Kousar, N. (2025) · International Journal of Research Publication and Reviews · doi:10.55248/gengpi.6.0425.15133 · Cited by 2
PERSONALIZED LEARNING PATHWAYS IN INFORMAL IT LEARNING USING GENERATIVE AI
Mayilyan, A. (2025) · Education in the 21st Century · doi:10.46991/educ-21st-century.v6.i2.81 · Cited by 2
Metadata
- Authors
- Chen, Z.
- Affiliations
- Nanjing University of Aeronautics and Astronautics (CN)
- Corresponding author
- Chen, Zhisheng
- ORCID
- Chen, Zhisheng
- Venue
- Journal of the Knowledge Economy
- Published
- 2022-02-24
- Volume
- 14
- Issue
- 2
- Pages
- 2007-2025
- ISSN
- 1868-7865 (ISSN-L)
- 1868-7873
- PMC
- PMC8865945
- OpenAlex
- W4213424378
- Cited by
- 156
- References
- 36
- Citations by year
- 2026: 26 · 2025: 58 · 2024: 48 · 2023: 22 · 2022: 2
- FWCI
- 32.17
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of the Knowledge Economy (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- artificial intelligence in training, AI-based training, individualized training, business development training, knowledge management, training needs assessment, training organization, training feedback, knowledge organization, learning quality
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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