ENHANCING EMPLOYABILITY OUTCOMES THROUGH AI TOOLS: A SEM-SPLS APPROACH WITH TAM AND SOFT SKILLS MEDIATION - Alabri , Ahmed; Shannaq , Boumedyen (Bangladesh Journal of Multidisciplinary Scientific Research, 2025)
There has been increased interest in understanding how AI is enhancing people’s ability to secure a good job lately, due to its rapid adoption in schools and workplaces. However, the relationships between how easy AI is to use and how valuable people think it is to its actual usefulness for getting a job are little studied. It examines the relationship between the usability of AI tools, their practical value, and their impact on employability, where soft skills act as a bridge between them. It studies the relationship between factors using Structural Equation Modeling and Partial Least Squares (SEM-PLS), exploring data from 429 users of learning environments. The study highlights significant relationships between constructs that are statistically significant, utilizing the Technology Acceptance Model (TAM). The findings show that the perceived usefulness of AI tools explains nearly a fifth of the changes in soft skills (18.1%) and close to a fifth of the improvements in employability outcomes (19%). In the same way, how easy a technology is to use (AI_EU_TAM) is essential for developing soft skills (β = 0.374, p = 0.000) and for getting a job (β = 0.246, p = 0.000). Having strong soft skills is very important for employment since it affects employability by 0.504 points (p = 0.000). Mediation confirms that soft skills help explain 56.1% of the relationship between AI_PU_TAM and EM and 76.8% of the relationship between AI_EU_TAM and EM. The results offer a unique perspective, demonstrating that the use of AI tools facilitates the development of new skills that support employability, which can inform future studies on online education and employment preparation. JEL Classification Codes: I21, J24, O33.