A Principal Component Analysis of the Factors Influencing University Students' Trust in AI-Based Educational Technologies

Abstract

This study examined the key factors influencing university students' trust in AI-based educational technologies in Calabar, Nigeria, using Principal Component Analysis (PCA). It was conducted to address the increasing integration of AI-driven learning tools in higher education and the necessity of understanding students' trust in these systems to ensure successful adoption. The primary objective was to determine the underlying dimensions shaping students' confidence in AI-based educational technologies. A structured survey was administered to 388 university students, and PCA was employed to identify and categorize the principal components affecting trust. The analysis revealed ten (10) key dimensions shaping students' trust: perceived system reliability; dependable AI functionality; institutional and social validation; explainable and secure AI engagement; ethical assurance and transparency; adaptive user engagement; operational integrity; accountable AI performance; institutional AI trust; and system legitimacy. These findings highlight that students’ trust in AI-based educational technologies is a complex, multidimensional construct shaped by both technical and socio-institutional factors. Hence, the study concludes that fostering trust in AI-based educational technologies requires a balanced approach that prioritizes not only technical robustness—such as reliability, security, and adaptability—but also institutional credibility, ethical transparency, and user-centered design to align with students' expectations and academic needs. As such, the study recommended that AI-based educational technology companies should enhance transparency, strengthen security, offer adaptive and user-centered experiences, collaborate with educational institutions, and implement real-time feedback and error-handling mechanisms to foster trust and adoption of their products by Nigerian students.

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