ClinTutor-R1: Advancing Scalable and Robust One-to-Many Alignment in Clinical Socratic Education

While Large Language Models (LLMs) have achieved remarkable success in dyadic (one-on-one) instruction, they face significant challenges in One-to-Many alignment, such as clinical ward rounds, where an instructor must simultaneously guide a diverse group of trainees. Current models often suffer from context dilution and goal misalignment, failing to balance individual scaffolding with collective learning progress. To address this, we introduce ClinEdu, a multi-agent pedagogical simulator that model the complexity of group dynamics. Leveraging this platform, we construct ClinTeach, a large-scale dataset of Socratic teaching dialogues, and propose ClinTutor-R1, the first multimodal agent explicitly architected to achieve one-to-many alignment in clinical education, employing an explicit internal thinking mechanism to model both individual belief states and group consensus. We validate our framework through a comprehensive protocol covering both standard static benchmarks and rigorous in-situ interactive evaluation within ClinEdu. Experimental results demonstrate that ClinTutor-R1 outperforms base models by over 20% and achieves parity with proprietary reasoning models , while exhibiting exceptional scalability in maintaining instructional quality across expanding student cohorts.