Examining Normative Drivers of GenAI Adoption in Education: Gender and Work Experience as Moderators

Abstract

The rapid emergence of generative artificial intelligence (GenAI) presents significant opportunities and challenges for contemporary education systems. Understanding the factors that shape students’ willingness to adopt GenAI is crucial for effective and responsible integration. This study investigates the role of normative influences particularly social influence and perceived image on perceived usefulness and subsequently on students’ intention to adopt GenAI tools in academic settings. Drawing upon established behavioral models such as the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB), a conceptual framework was developed and tested using Structural Equation Modeling (SEM). 493 participant data was collected through a structured questionnaire administered to university students across multiple disciplines. The hypothesized pathways were analyzed using SEM to evaluate the direct and indirect relationships among key constructs. The results reveal that social influence and image are not important predictors of students’ intention to adopt GenAI tools which indicates the changing dynamic of the social structure within formal education settings. Students' behavioral intention appears to be driven more by personal and task-related factors rather than perceived social expectations or pressure to conform. These findings challenge assumptions in existing GenAI adoption literature and suggest that normative pressure may play a diminished role in digitally native learner populations. The study contributes to theory by questioning the universal applicability of normative constructs in technology adoption models and provides practical insights for educators and policymakers to shift focus toward usability, personalization, and autonomy-supportive strategy. The study also discusses the limitations and future research scope.