Understanding AI Technology Adoption in Educational Settings: A Review of Theoretical Frameworks and their Applications
Abstract
Artificial Intelligence (AI) technologies are increasingly integrated into educational environments, promising transformative impacts on learning experiences and administrative efficiencies. This review synthesizes prominent theoretical frameworks used to understand AI technology adoption among students and educators in educational settings. The Value-based Adoption Model (VAM), Theory of Planned Behavior (TPB), Unified Theory of Acceptance and Use of Technology (UTAUT), and Technology Acceptance Model (TAM) are examined for their strengths and limitations in explaining the factors influencing technology adoption. Through a comprehensive analysis of recent literature, this paper highlights the involvement of user acceptance, incorporating cognitive, social, and emotional dimensions. Understanding theoretical frameworks related to AI technology adoption could provide a comprehensive overview of existing theoretical frameworks related to AI technology adoption in educational settings, integrating findings into a cohesive narrative.
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