Forthcoming and Online First Articles

International Journal of Learning and Change

International Journal of Learning and Change (IJLC)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Learning and Change (One paper in press)

Regular Issues

  • Artificial intelligence powered engagement: unveiling trends, driving learning, and shaping the future of employee experience   Order a copy of this article
    by D. Prema, Ragini 
    Abstract: This bibliometric analysis examines the intersection of artificial intelligence in enhancing employee learning and boosting employee engagement. This research aims to systematically map the existing literature, identify key contributors, highlight influential journals, and cover prevalent themes in this emerging field by conducting a bibliometric analysis. It sheds light on the growing application in these areas, such as sentiment analysis, personalised learning, and engagement strategies. The study utilises bibliometric analysis, leveraging the Scopus database to examine publications spanning from 2012 to 2024. The analysis reveals a growing trend in artificial intelligence-related research. Artificial intelligence enhances real-time performance tracking, personalised learning, and seamless communication. Nevertheless, ethical considerations regarding privacy and data security remain crucial. This study is the first to offer a bibliometric analysis of employee engagement through artificial intelligence. It advances scholarly understanding of artificial intelligences impact on employee engagement and provides valuable insights for future research and practical applications.
    Keywords: artificial intelligence; AI; personalised learning; employee engagement; EE; bibliometric analysis; sentiment analysis; predictive analytics.
    DOI: 10.1504/IJLC.2025.10071160
     

 

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