Exploring An AI-Supported Collaborative Path for Inquiry-Based Teaching and Ideological-Ethical Education
XIE Bingteng, YI Yue, HAO Zikai, LIANG Axin, LUO Aiqin*
With the increasing demand for higher-level, research-oriented, and challenging learning in graduate education, integrating AI (artificial intelligence) technologies with ideological and ethical education to foster both scientific literacy and ethical responsibility has become a key objective of higher education reform. This study takes the “Frontier Progress in Biological Diagnosis and Treatment” graduate course at Beijing Institute of Technology as a case to explore a four-in-one instructional model that integrates “major disease-driven themes, inquiry-based group learning, AI-enabled support, and ideological-ethical integration”. Centered on the scientific logic of “disease mechanisms-biomedical diagnostics-intervention and therapeutic strategies”, students conduct group investigations around major diseases such as cancer, neurodegenerative disorders, autoimmune conditions, and infectious diseases. The course covers eight core modules, including disease mechanisms, biomarker discovery and detection, and targeted therapies. AI tools such as the IBIT platform and large language models are introduced throughout the course to assist students with mechanism mapping, literature summarization, and diagnostic pathway simulations, thereby enhancing their research capabilities and interdisciplinary thinking. To ensure effective use, students receive relevant training and operational guidance. Simultaneously, the course integrates ideologicalethical education through case-based learning, ethical discussions, and national scientific achievements to foster students’ sense of responsibility and value recognition. Teaching practice demonstrates that this model effectively enhances students’ research engagement and ethical awareness, offering a viable approach for graduate education reform in the AI era.
CN
EN