Examining Factors of Student Ai Adoption Through The Value-Based Adoption Model

This research paper delves into student perspectives on the adoption, impact, and future expectations of generative AI technologies within academic settings, employing the Value-Based Adoption Model (VAM). Data were gathered through focus group interviews with university and college students, revealing multifaceted factors that drive both adoption and resistance towards these technologies. Students articulated that the primary benefits of generative AI include its usefulness as an academic aid—facilitating homework and idea generation—and its capacity to enhance enjoyment by simplifying complex tasks. Conversely, apprehensions regarding the technical challenges and potential errors associated with AI use, as well as the implicit costs of premium features, were identified as significant barriers. These concerns, categorized as sacrifices, play a crucial role in shaping students’ perceived value of generative AI tools. The perceived value, in turn, influences their intentions to adopt such technologies. Importantly, the study highlights a strong inclination among students to embrace AI tools when perceived benefits outweigh the perceived sacrifices. This paper offers valuable insights into the factors influencing student engagement with AI technologies and suggests directions for future technological enhancements and educational policies to maximize positive outcomes and mitigate drawbacks.