Publicação
AI-Powered Career Navigation Platform: Predicting Automation Risk and Guiding Professionals to Optimal Occupation Transitions with a Focus on Generation X in Portugal
| datacite.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | pt_PT |
| dc.contributor.advisor | Henriques, Roberto André Pereira | |
| dc.contributor.author | Almeida, Ricardo da Costa | |
| dc.date.accessioned | 2024-11-06T15:20:04Z | |
| dc.date.available | 2024-11-06T15:20:04Z | |
| dc.date.issued | 2024-10-28 | |
| dc.description | Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science | pt_PT |
| dc.description.abstract | The rapid advance of artificial intelligence (AI) and automation technologies has raised significant concerns about job displacement, particularly among Generation X professionals in Portugal. This project addresses these concerns by developing an interactive recommendation system named Career Xplorer, designed to help professionals navigate potential career transitions in a personalised way. Using advanced Natural Language Processing (NLP) models such as BERT and GPT-4 and a task-based approach that assesses automation risk for individual tasks rather than entire occupations, Career Xplorer estimates the automation probability for each user’s occupation. The recommendation system also suggests alternative occupations with lower automation risks and provides a detailed job transition guide outlining the essential skills and competences needed for these transitions. This guide helps users identify which skills to focus on and which to deprioritise, facilitating a more confident and smoother professional transition. The study finds that not only occupations with repetitive and low-skilled tasks face the greatest risks of automation but also more technical roles, mainly due to the presence of well-established rules. On the other hand, more communicative and creative occupations have lower levels, suggesting an area of greater security in a labour market impacted by AI and automation. By empowering Generation X professionals with the tools and knowledge to proactively manage their careers amidst the evolving technological landscape, Career Xplorer helps mitigate the impacts of job displacement and promotes longterm career stability and growth while taking employee happiness into account. | pt_PT |
| dc.identifier.tid | 203776674 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10362/174699 | |
| dc.language.iso | eng | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Job Displacement | pt_PT |
| dc.subject | Job Transition Guide | pt_PT |
| dc.subject | Automation Risk | pt_PT |
| dc.subject | Natural Language Processing | pt_PT |
| dc.subject | Interactive Recommendation System | pt_PT |
| dc.subject | SDG 1 - No poverty | pt_PT |
| dc.subject | SDG 4 - Quality education | pt_PT |
| dc.subject | SDG 8 - Decent work and economic growth | pt_PT |
| dc.subject | SDG 9 - Industry, innovation and infrastructure | pt_PT |
| dc.subject | SDG 10 - Reduced inequalities | pt_PT |
| dc.subject | SDG 17 - Partnerships for the goals | pt_PT |
| dc.title | AI-Powered Career Navigation Platform: Predicting Automation Risk and Guiding Professionals to Optimal Occupation Transitions with a Focus on Generation X in Portugal | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Mestrado em Ciência de Dados e Métodos Analíticos Avançados, especialização em Ciência de Dados | pt_PT |