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Bridging the skills gap: A machine learning approach to align business education with labor market needs in the UAE

Rachidatou I. Traoret et al.

Industry and Higher Education2026https://doi.org/10.1177/09504222261436625article
AJG 1ABDC C
Weight
0.50

What the paper says

The job market is rapidly evolving due to digital transformation, resulting in the creation of roles requiring new skills. This transformation presents a challenge for professionals and new graduates to remain competitive in the workforce. In this context, higher education institutions (HEIs) play a vital role in preparing graduates for employability. Effective alignment between education and industry requires a clear understanding of labor market demands and the skills currently offered by HEIs. Therefore, this study aims to examine the gap between business graduates’ skills and employer expectations in the United Arab Emirates (UAE). To this end, this study employs neural topic modeling to cluster data from the labor market and HEIs. The findings reveal that although business graduates remain in demand for traditional roles, there is a growing need for graduates who are equipped with modern skills. In particular, the key soft skills required in the UAE labor market include teamwork, communication, and interpersonal abilities, along with digital marketing and technical skills. However, although HEIs are fostering analytical and technical competencies, there remain gaps between their offerings and market needs. This study offers recommendations to address these gaps by improving curricula and strengthening graduates’ readiness for labor market demands.

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https://doi.org/https://doi.org/10.1177/09504222261436625

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@article{rachidatou2026,
  title        = {{Bridging the skills gap: A machine learning approach to align business education with labor market needs in the UAE}},
  author       = {Rachidatou I. Traoret et al.},
  journal      = {Industry and Higher Education},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1177/09504222261436625},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.