Professional Development Analytics: A Smart Model for Industry 5.0
This paper presents a novel AI-driven conceptual smart model designed to help organizations enhance workforce professional development by upskilling and reskilling employees while fostering job satisfaction and staying competitive in their markets; this novel model is called Professional Development Analytics (PDA). The model’s main focus is to provide a new design model that concentrates on how artificial intelligence (AI) can optimize personalized training and how it can improve employees’ technical and soft skills, enabling companies to create their talent map at the same time. By compiling personnel data and their roles within the company, AI is able to create detailed and personalized profiles. In the next stage, this information is classified, analyzed, and used to enhance current skills while also predicting future training needs. These processes result in the creation of personalized learning paths, where AI recommends customized courses tailored to each employee’s unique needs. The system will be automatically fed and adjusted by means of the gathered data and continuous feedback from the employees and their supervisors. The proposed AI tools are powered by machine learning, deep learning, natural language processing, generative AI and data analytics. Our model aims to support learning and development departments by delivering precise, personalized training solutions that address employees’ unique needs, enabling skill development and professional growth through an automated and customized process.
Authors
- Raúl Santiago Campión (ORCID: https://orcid.org/0000-0002-1256-5338)
- Ramón Palau (ORCID: https://orcid.org/0000-0002-9843-3116)
- Natalia Tusquellas (ORCID: https://orcid.org/0009-0003-5471-0526)
Institutions
- Navarrabiomed (ES)
- Universitat Rovira i Virgili (ES)
Publication Details
- Journal
- Applied Sciences
- Published
- 2025-02-16
- DOI
- https://doi.org/10.3390/app15042057
- Citations
- 8
- Primary Topic
- AI and HR Technologies
- Type
- article
- Field-Weighted Citation Impact
- 22.11