## 2 State of the Art
### Anticipating Job Market Demands  
Anticipating job market demands is a difficult proposition. The lockdown measures imposed during the COVID-19 pandemic, for instance, triggered declines in labor demand of up to 30% [3]. A 2023 study estimates that only 30.7% of current jobs are likely to remain unaffected by disruptions caused by generative artificial intelligence (AI) such as ChatGPT. The study's authors expect that a majority of jobs will either be fully or partially impacted by generative AI [4]. Another 2023 study states that Industry 5.0 requires highly skilled individuals with various soft skills (e.g., communication, teamwork, emotional intelligence) capable of collaborating with both humans and machines [5]. Such economic megatrends tend to disrupt and reshape job markets, often acting together (e.g., the twin impact of COVID-19 and AI [6]) and requiring both employers and workers to adapt.  
A recent survey that used data mining techniques to predict student employability [7] identifies the following challenges: (i) focus on gender instead of psychometric attributes; (ii) unbalanced and incomplete datasets; (iii) studies based on heuristics; (iv) scalability is an issue as participants usually come from the same environment (e.g., universities); and (v) reproducibility since data and source code are not publicly available. Most of the studies investigated in the survey center on students and lack integration of online data sources, such as job advertisements or CVs. This tendency could potentially be attributed to the nature of the study cohort. For instance, there might be fewer online job opportunities directly targeting students. Notably, studies focusing on workers, as exemplified by [8], predominantly draw from online sources.  
[PERSON] and his group [8] survey skill identification technology [8], one of the pillars of predicting job market demands. They define the following objects: (i) online data sources (e.g., job ads, academic curricula, CVs); (ii) skill bases developed by experts (e.g., public ontologies and taxonomies) and customized skill bases (e.g., manually built or based on embeddings); (iii) skill identification methodologies (e.g., skill count, topic modeling, embeddings, ML-based); (iv) evaluation metrics (e.g., precision, recall, f1 for binary classification tasks or MRR for multi-label classification tasks); (v) skill identification granularity (e.g., sentences, n-grams, sentences and n-grams); and (vi) industry sectors (e.g., IT, engineering, healthcare, multiple sectors). The second part of the survey focuses on the various applications of this technology, such as market analysis, curricula development, job recommendation engines, talent search, skill demand prediction and gender bias identification. The article ends with an overview of future work, which includes deep learning, graph embeddings, or generation of skill bases.  
Most of the articles present global trends, but job demand forecasting is typically local. Some recent studies, for example, focused on analyzing the Norwegian IT market to improve the computing curriculum [9] or assessing skill demand in the Lithuanian market by analyzing the job ads with clustering and NLP techniques [10].