Technology readiness predictors of AI integration: SEM-PLS evidence from pre-service biology teachers in Indonesia - Agil Zuhal Triandro; Raharjo Raharjo; Mahanani Tri Asri (Universitas Islam Negeri Sumatera Utara, 2025)
The rapid advancement of Artificial Intelligence (AI) requires educators to possess adequate technological readiness. However, current literature predominantly focuses on in-service teachers using general acceptance models, often overlooking whether traditional psychological inhibitors remain relevant for 'digital native' students in developing contexts. This study aims to analyze the technology readiness profile of pre-service biology teachers and investigate the predictive effect of Technology Readiness Index (TRI) dimensions Optimism, Innovativeness, Discomfort, and Insecurity on their readiness to integrate AI in biology teaching. Employing a cross-sectional survey design, data were collected from 200 active undergraduate students at Universitas Negeri Surabaya and analyzed using Structural Equation Modeling-Partial Least Squares (SEM-PLS). The model explains a substantial variance in AI readiness (R2 = 0.538). The results revealed that Optimism acts as the most dominant significant driver, followed by Innovativeness. Crucially, this study offers new evidence that psychological inhibitors (Discomfort and Insecurity) no longer significantly affect AI integration readiness among mature users, challenging common assumptions in early adoption literature. The findings suggest a paradigm shift where pre-service teachers are pragmatic users driven by perceived utility rather than fear. Therefore, curriculum developers and policymakers must shift strategies from anxiety mitigation to the creation of biology-specific