Development and Validation of an Artificial Intelligence Acceptance Questionnaire in Higher Education Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) Model:  A Mixed Methods Instrument Development Design

Zanganeh, Amirhosein; Hejazi, Elahe; Salehi, Keyvan

doi:10.30473/etl.2026.74352.4391

Development and Validation of an Artificial Intelligence Acceptance Questionnaire in Higher Education Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) Model: A Mixed Methods Instrument Development Design

Document Type : Research

Authors

¹ MS in Educational Psychology, Faculty of Psychology and Education, University of Tehran, Tehran, Iran.

² Department of Educational Psychology, Faculty of Psychology and Education, University of Tehran, Tehran, Iran

³ Associate Professor of Educational Assessment, Division of Research and Assessment, Faculty of Psychology and Education, University of Tehran, Tehran, Iran

10.30473/etl.2026.74352.4391

Abstract

With the rapid expansion of artificial intelligence technology in higher education, identifying and evaluating the factors that influence the adoption and utilization of this technology has become a significant concern for researchers, instructional designers, policymakers, and educational administrators. Although tools for measuring technology acceptance are available, no comprehensive, localized instrument has yet been developed specifically to assess the components affecting AI acceptance in higher education. The present study aimed to develop and validate a questionnaire to measure the factors influencing the acceptance and use of AI technology in higher education, based on UTAUT. This study employed a mixed-methods approach for instrument development, conducted in both qualitative and quantitative phases. In the qualitative phase, through the analysis of 10 relevant documents, the indicators of the perceived risk construct were identified. While adapting other items in a precise and localized manner, the developed version was prepared. In the quantitative phase, a sample consisting of 230 faculty members and students from universities in Tehran was selected using convenience sampling. The research instrument consisted of 29 items across seven main constructs. The instrument’s face and content validity were confirmed through expert review and the CVR and CVI indices. Additionally, the reliability and internal consistency of the instrument were verified through test–retest reliability, Cronbach’s alpha, and Omega coefficients for each construct. The findings indicate that this instrument possesses strong psychometric properties and can be effectively used to assess and analyze the factors influencing the acceptance and use of artificial intelligence in higher education.

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