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\nPsychometric validation of the artificial intelligence anxiety scale: A confirmatory factor analysis for academic research  \nMariam Hejab Alshaibani 1  \n 0000-0002-6598-1407  \nWaleed Mugahed Al-Rahmi 2*  \n 0000-0002-0980-7439  \nRagad M. Tawafak 3  \n 0000-0001-8969-1642  \n1 Department of Psychology, College of Arts, Taif University, Taif, SAUDI ARABIA  \n2 Department of Management Information System, College of Business Administration, Dar Al Uloom University, Riyadh, SAUDI ARABIA  \n3 Department of Information Technology, AlBuraimi College, Al Buraimi, OMAN  \n* [Corresponding author:](Corresponding author: waleed.alrahmi@dau.edu.sa)[ waleed.alrahmi@dau.edu.sa](Corresponding author: waleed.alrahmi@dau.edu.sa)  \nCitation: Alshaibani, M. H., Al-Rahmi, W. M., & Tawafak, R. M. (2025) . Psychometric validation of the artificial intelligence anxiety scale: A confirmatory factor analysis for academic research. Contemporary Educational Technology, 17(4), ep595.  \n[https://doi.org/10.30935/cedtech/17309](https://doi.org/10.30935/cedtech/17309)  \nARTICLE INFO ABSTRACT  \n\n| Received: 22 Mar 2025\u003Cbr>Accepted: 20 Jul 2025\u003Cbr>INTRODUCTION | This study introduces and validates the artificial intelligence anxiety scale (AIAS), a novel instrument designed to measure researchers’ anxieties when employing artificial intelligence (AI) tools in academic writing. As AI technologies rapidly infiltrate scholarly work, however, the primary concern grows about their ethical implications, impact on traditional research skills, and the lack of institutional readiness issues that remain underexplored in existing literature. Addressing this critical gap, the AIAS offers a novel framework grounded in real-world academic concerns. Using an inductive approach, data were collected from 219 faculty members and graduate students at Taif University, Saudi Arabia, revealing four core dimensions of AI-related anxiety: (1) concerns about the accuracy of AI outputs, (2) fear of committing plagiarism, (3) lack of institutional guidelines, and (4) fear of losing research skills. Exploratory and confirmatory factor analyses confirmed the existence of these factors, and reliability testing indicated robust internal consistency. By offering the first validated tool specifically tailored to measure AI-related anxiety in academic contexts, this study provides a significant resource for researchers andinstitutions. Its application can guide universities in devising training and support initiatives to ensure the ethical and practical integration of AI, thereby sustaining the integrity of traditional research competencies and enhancing the overall quality and credibility of academic endeavors.\u003Cbr>Keywords: AI anxiety, confirmatory factor analysis, academic research, psychological assessment |\n| --- | --- |\n\nArtificial intelligence (AI) is significantly transforming the landscape of academic research across various disciplines by enhancing efficiency, accuracy, and the scope of research activities. In higher education, AI tools are being integrated into research processes to streamline data analysis, literature reviews, and even the drafting of scientific texts, thereby saving researchers considerable time and effort. AI’s ability to process vast amounts of data rapidly enables more efficient literature reviews, facilitating the identification of relevant studies and highlighting new research directions or gaps in the existing literature. Moreover, AI technologies such as natural language processing (NLP) and large language models are revolutionizing text generation, enabling the production of high-quality scientific texts that can assist researchers in overcoming writer’s block  \nand ensuring the accuracy of scientific publications, particularly in fields like pharmacy and medicine where precision is crucial.  \nWithin contemporary academic settings, the use of AI-ba","cbCaivmWNhJhOhnm","https://ap.wps.com/l/cbCaivmWNhJhOhnm","pdf",393271,16,"English","# Introduction\n## Purpose and gap addressed\n## Development and validation approach\n## Scale dimensions and findings\n# Background: AI in academic research\n## Applications and benefits\n## Integrity, authorship, and concerns\n# AI-related anxiety construct\n## Definition and need for measurement","[{\"question\":\"What does the Artificial Intelligence Anxiety Scale (AIAS) measure?\",\"answer\":\"AIAS measures researchers’ anxieties related to using AI tools in academic writing, capturing concerns tied to scholarly accuracy, integrity, institutional preparedness, and skill retention.\"},{\"question\":\"How were data collected and how many participants were involved?\",\"answer\":\"Data were collected using an inductive approach from 219 faculty members and graduate students at Taif University in Saudi Arabia.\"},{\"question\":\"What four dimensions of AI-related anxiety were identified?\",\"answer\":\"The study identifies four dimensions: worries about AI output accuracy, fear of committing plagiarism, lack of institutional guidelines, and fear of losing research skills.\"}]","Psychometric validation of the artificial intelligence anxiety scale - A confirmatory factor analysis for academic research | PDF",1792090813]