The rapid advancement of artificial intelligence (AI) has transformed higher education by providing increasingly sophisticated support for teaching preparation, instructional design, assessment, and academic management. While previous studies have primarily focused on teachers' acceptance of AI technologies, relatively limited attention has been devoted to how university teachers determine the appropriate level of trust in AI-generated outputs while maintaining professional responsibility. This study aims to explore the development strategies for AI-assisted teaching by examining the roles of professional judgment and trust calibration among university teachers. Guided by AI Agents in Higher Education, Trust Calibration Theory, and Professional Judgment Capability, the study adopts a qualitative research design using semi-structured interviews with university teachers who have experience using generative AI tools in teaching. The interview findings indicate that teachers do not simply accept or reject AI-generated content; instead, they continuously evaluate its accuracy, pedagogical appropriateness, ethical implications, and relevance to students' learning needs before making instructional decisions. Professional judgment functions as the core mechanism through which teachers calibrate trust and determine appropriate reliance on AI in different teaching contexts. The study further identifies major challenges related to AI reliability, varying levels of AI literacy, ethical concerns, and insufficient institutional support. Based on these findings, several development strategies are proposed, including strengthening teachers' AI literacy, establishing institutional AI governance frameworks, reinforcing professional judgment, and promoting sustainable human–AI collaboration. This study contributes to the literature by conceptualizing trust calibration as a dynamic decision-making process and provides practical implications for the responsible integration of AI-assisted teaching in higher education.
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