Instrument Development for Assessing Continuance Intention toward Generative AI Tools in Education
TL;DR
This study provides an instrument identifying factors that affect the continuance intention toward generative Artificial Intelligence tools in the educational sector by integrating frameworks of the Information System Success Model and Expectation-Confirmation Model, as well as privacy concerns and trust.
Abstract
The objective of this study is to develop and validate an instrument identifying factors that affect the continuance intention toward generative Artificial Intelligence (AI) tools in the educational sector by integrating frameworks of the Information System (IS) Success Model and Expectation-Confirmation Model (ECM), as well as privacy concerns and trust. Constructs examined are perceived usefulness, trust, system quality, satisfaction, and other relevant dimensions. Expert reviews, pre-testing, and statistical techniques including Cronbach’s alpha and Exploratory Factor Analysis using Principal Axis Factoring were employed to assess reliability and construct validity respectively, with reliability results exceeding 0.75. The final questionnaire contained 21 items addressing these dimensions. Even though the study does not test the significance of these factors, it identifies and operationalizes key constructs that will subsequently help future empirical research. This study provides a strong instrument that forms the foundation for understanding how Generative AI tools could be integrated into education to support personalized learning and improve accessibility. These insights can contribute to the improvement of user experience and more effective AI-based educational tools by policymakers, educators, and developers alike.