Modeling Generative AI Adoption in Higher Education: The Role of Interface Quality, Algorithmic Transparency, and Trust in Human-AI Interaction - Baiq Yulia Fitriyani; Khairul Imtihan; Amrullah; Maulana Ashari; Wire Bagye (Journal of Information System and Informatics, 2026)
This study examines generative artificial intelligence (AI) adoption in higher education by integrating interface quality, algorithmic transparency, and trust within a Human-AI Interaction framework. The study addresses limitations of traditional technology acceptance models, which often overlook psychological and relational factors in AI-enabled environments. Data were collected through a cross-sectional survey of 195 respondents, including students, lecturers, and administrative staff from higher education institutions in West Nusa Tenggara, Indonesia, within a cross-sectional regional sample, and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that Trust in AI is the strongest predictor of Behavioral Intention (β = 0.623, p < 0.001), followed by Perceived Usefulness (β = 0.270, p < 0.05). Interface Quality significantly affects Perceived Ease of Use (β = 0.803), while Algorithmic Transparency strongly influences Perceived Control (β = 0.824) and Perceived Usefulness (β = 0.562). AI Anxiety was not found to have a significant direct or moderating effect. The model demonstrates substantial explanatory power (R² = 0.710) and strong predictive relevance. This study proposes an integrated dual-path model combining cognitive and affective mechanisms to explain generative AI adoption in higher education. The findings emphasize that AI systems should be designed not only for functionality, but also for trust, transparency, and user confidence.