Decoding AI’s Evolution Using Big Data: A Methodological Approach

Sophie Gvasalia, Mauro Pelucchi, Simone Perego, Rita Porcelli

2024

Abstract

This study presents a novel approach to measuring the impact of Artificial Intelligence on occupations through an analysis of the Atlante del Lavoro dataset and web job postings. By focusing on data preparation and model selection, we provide real-time insights into how AI is reshaping job roles and required skills. Our methodological framework enables a detailed examination of specific labour market segments, emphasizing the dynamic nature of occupational demands. Through a rigorous mixed-method approach, the study highlights the AI impact on sectors such as ICT, telecommunications, and mechatronic, revealing distinct skill clusters and their significance. This innovative analysis not only delineates the convergence of digital, soft, and hard skills but also offers a multidimensional view of future workforce competencies. The findings serve as a valuable resource for educators, policymakers, and industry stakeholders, guiding workforce development in line with emerging AI-driven demands.

Paper Citation

in Harvard Style

Gvasalia S., Pelucchi M., Perego S. and Porcelli R. (2024). Decoding AI’s Evolution Using Big Data: A Methodological Approach. In Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR; ISBN 978-989-758-716-0, SciTePress, pages 424-432. DOI: 10.5220/0013016600003838

in Bibtex Style

@conference{kdir24,
author={Sophie Gvasalia and Mauro Pelucchi and Simone Perego and Rita Porcelli},
title={Decoding AI’s Evolution Using Big Data: A Methodological Approach},
booktitle={Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR},
year={2024},
pages={424-432},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013016600003838},
isbn={978-989-758-716-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
TI - Decoding AI’s Evolution Using Big Data: A Methodological Approach
SN - 978-989-758-716-0
AU - Gvasalia S.
AU - Pelucchi M.
AU - Perego S.
AU - Porcelli R.
PY - 2024
SP - 424
EP - 432
DO - 10.5220/0013016600003838
PB - SciTePress