Integrating generative artificial intelligence facilitates faculty interaction network and role transition in a biostatistics curriculum

In a two-year action-research study within an undergraduate biostatistics curriculum for nursing, pharmacy, and medical biotechnology students, we examined how generative artificial intelligence (GenAI) facilitates educators to design, supervise, and evaluate learning. Students used GenAI first as a technical tutor and then as a partner in knowledge co-creation in case-based statistical problem-solving. Teachers’ reflection records and 4697 LINE messages during the study period were analyzed using qualitative coding, GenAI-assisted semantic analysis, and social network analysis. Teachers’ roles shifted from knowledge transmitters to facilitators of critical inquiry and human–GenAI co-learning. Overall, faculty communication was dense, reciprocal, and decentralized, whereas GenAI-related discourse was more selective and centralized. GenAI-related ties were less likely to form but, once formed, strongly reciprocal and locally clustered. GenAI integration can catalyze new forms of faculty leadership through expertise sharing and collaborative problem-solving. Intentional structures for cross-disciplinary exchange and reflective faculty development are needed.