Digital Mindset and Sensemaking in AI Integration: A Study of Open Education Teachers, Competence and Trust in AI

This study investigates how digital mindset, AI sensemaking, trust in AI, digital teaching competence, and institutional support influence AI integration among open education teachers in China. Drawing on Sensemaking Theory, the Digital Mindset Framework, and Algorithm Aversion Theory, the study employs a three-wave survey of 366 instructors in Guangdong Province and analyzes the data using Partial Least Squares Structural Equation Modeling (PLS-SEM). Findings reveal that digital mindset and AI sensemaking significantly predict AI integration, while trust in AI and digital teaching competence do not have direct or consistent mediating effects. Surprisingly, perceived institutional support negatively moderates the relationship between digital mindset and both AI sensemaking and trust, suggesting that overly directive support may undermine autonomy in open education settings. The study contributes theoretically by extending digital mindset and sensemaking frameworks to the AI-in-education context and offering a context-sensitive interpretation of trust theory. Practically, it highlights the need for autonomy-respecting, interpretive support structures and mindset-based training programs. These insights inform more effective AI adoption strategies in decentralized learning environments.