Designing a human-AI collaborative model for precision teaching in undergraduate physiology laboratory education: A modified Delphi study

Abstract

Artificial intelligence (AI) offers new possibilities for enhancing precision in undergraduate physiology laboratory education. However, ethical, pedagogical, and technical challenges remain in designing human-AI collaborative models. This study aimed to develop and validate a precision teaching framework grounded in expert consensus. The model consists of four core modules: precision learning analysis, precision teaching objective setting, precision teaching implementation and organization, and precision teaching evaluation and feedback. Using a modified Delphi method, consensus on the scientific validity and feasibility of the model was reached through two rounds of consultations with 15 interdisciplinary experts. Results indicate that the theoretical framework received high recognition from experts, who also reached unified consensus on key practical pathways, including phased technological implementation, strengthening teacher leadership and ethical safeguards, development of a Knowledge Graph, and targeted teacher training. This study presents a theoretically sound and ethically grounded human-AI collaborative model for undergraduate physiology laboratory teaching. It offers a practical roadmap for institutions seeking to integrate AI while preserving teacher agency, pedagogical integrity, and equitable student support.

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