Generative AI for Students Courses: Evaluating the Impact on Fresh Graduate Performance

Abstract

Generative AI is increasingly being used in the workplace to enhance productivity and accelerate the adaptation of new employees. This study aims to analyze how Generative AI can accelerate the learning curve and increase the productivity of fresh graduates by using the Technology Acceptance Model (TAM) framework. This quantitative study involved fresh graduate employees from various universities in Medan as respondents. The Structural Equation Modeling-Partial Least Squares (SEM-PLS) method was used to analyze the data, specifically in examining the relationship between perceived ease of use and perceived usefulness on the acceleration of the learning curve and its impact on productivity. The results showed that fresh graduates who have taken the "Generative AI for Students" course have a higher perception of the ease of use and usefulness, which ultimately accelerates the learning curve and increases their productivity. This increase in productivity is also indicated by the rise in the ability to complete tasks, adapt to new roles and responsibilities, and solve problems in the workplace. These findings have important implications for companies supporting the adoption of AI technology and for policymakers supporting AI-related training, particularly the "Generative AI for Students" course, in higher education curricula.

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