Systematic Analysis of Digital Skills Training Models: Adaptability, Sustainability, and a Framework for Iran's Future Labor Market

This research aimed to systematically analyze digital skills training models and develop a framework for adaptability, sustainability, and preparing Iran's workforce for the future labor market. Using a systematic analysis method, 87 sources from 2014–2024 were reviewed from reputable databases (Scopus, Web of Science, Google Scholar, SID) and global reports (UNESCO, OECD), analyzed through qualitative (thematic coding) and quantitative (comparative tables) approaches. The theoretical framework was based on the digital economy, digital governance, and digital skills training. Findings revealed that Iran's digital economy, with a projected 65% growth in IT jobs by 2030, has increased demand for advanced skills (blockchain, AI), but a low baseline digital skills rate (40%), a 70% skills gap in programming, and 20.4% of the population being offline are key challenges, necessitating more cohesive policymaking. The conceptual framework identified the digital economy as a driver and digital governance as a guide for education. Benchmarking successful global and localized models tailored to Iran's needs was recommended. Proposals include establishing national skills training and assessment platforms, developing a national digital competency model, and creating a financing model (government-industry joint fund). Suggestions for future studies were also provided.