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Rapid developments in the Artificial Intelligence (AI) industry have triggered an increased need for workers with specialized competencies, which has implications for significant variations in salary levels. This research aims to analyze the factors that influence salaries in the AI sector using the multiple linear regression method. The dataset used includes 15,000 AI job vacancies with variables including job and company characteristics. The data was engineered via the one-hot encoding method and divided into two parts: training data (80%) and test data (20%). The analysis results show that the regression model is able to explain 85% of the variation in salary, with an R² value of 0.85 and a Root Mean Square Error (RMSE) of USD 23,221. The three main factors identified as having a significant influence on salaries in the AI field are work experience, company location, and the industry in which the company operates. The experience factor reflects the skills and knowledge developed over many years, which can increase productivity (Rony et al., 2023). Company location also plays an important role, as the cost of living and demand for skilled labor varies by region (Badran, 2019). Additionally, the specific industry in which an employee works influences salary, given that more developed industries can often offer higher compensation (Huang, 2025). This research makes a significant empirical contribution to the understanding of compensation structures in the AI labor market.

The digital competency gap between vocational school graduates and the rapidly evolving demands of modern industry has become a pressing issue in today’s workforce. One of the main challenges lies in the limited digital literacy and insufficient practical skills of students, particularly in applying Artificial Intelligence (AI) tools to support productivity and creativity. This situation hinders the readiness of graduates to compete in an AI-driven labor market, where innovation and technological adaptability are essential. To address this challenge, a training program was designed for vocational high school students at Darma Siswa Sidoarjo with the primary objective of enhancing both their conceptual understanding and practical competencies in the utilization of AI technologies. The program adopted the Participatory Action Research (PAR) method, emphasizing collaboration and active engagement between trainers and students. It was conducted through a series of interactive workshops that combined theoretical introductions with hands-on practice. Students were guided to explore AI-based platforms such as Ideogram for visual creativity, PixVerse for multimedia production, and Suno for AI-generated music. To evaluate the effectiveness of the program, pre-test and post-test instruments were used, focusing on the dimensions of knowledge, technical skills, and student confidence in using AI tools. The findings revealed that students initially demonstrated minimal awareness and limited practical ability in AI applications, as reflected in the pre-test results. However, post-test data showed a significant improvement not only in their technical proficiency but also in their enthusiasm to further explore AI technologies. Moreover, students gained increased confidence in producing creative outputs, transforming their role from passive consumers of digital tools into active and competent content creators. In conclusion, this participatory and practice-oriented training model effectively bridges the digital competency gap, enhances students’ job readiness, and fosters an innovative mindset.

qualified labor is an important factor in achieving economic development of a region. To improve the quality of the workforce, job training is needed that can provide job skills for the workforce. This study aims to explain the benefits of job training for improving the quality of the workforce based on the policies and programs of the NTT Provincial work training UPTD and explain the productivity of graduate participants after attending job training. This study uses a descriptive qualitative approach, by conducting structured interviews to informants and instructors UPTD work training, and also graduates from job training. The results showed that job training is very beneficial for improving the quality of the workforce through the empowerment of Labor expertise in order to have skills, attitudes, and knowledge about the world of work. Many of the job training graduates have been successful in their efforts and are able to absorb other workers, but not infrequently job training graduates who fail to achieve productivity due to not being serious when attending job training, lack of availability of business capital, and lack of readiness to compete in the world of work. In order for job training graduates to achieve their productivity, supervision is needed by the Uptd of work training and also financial assistance from local governments for the effectiveness of training activities and the provision of business capital to support the efforts of job training graduates.