Modeling how diverse communities respond to public health messages is critical for training students to design targeted communication materials. However, no method is currently available to iteratively evaluate how different populations may perceive their campaign designs before human-subject testing. Despite progress in VLM/LLM-based psychometric simulation, community simulators for public health education remain largely unexplored. We reframe VLMs as attribute-aware community response simulators and introduce CLAAS, to our knowledge the first multimodal response simulation tool for public-health higher education. CLAAS aligns open-source VLMs, such as Gemma3, Llama-3.2, and Qwen2.5-VL, with an in-house survey dataset of 33K+ responses from 1K+ participants to simulate trait-conditioned emotional, behavioral, and sentimental responses. Quantitatively, attribute-aligned VLMs consistently outperform zero-shot and random baselines and generalize to unseen communication strategies. Beyond dataset evaluation, our pilot studies in real course sessions across two semesters demonstrate CLAAS’s potential practical relevance in educational settings. Most students reported CLAAS provided useful feedback and generated responses aligned with their expectations. This study suggests attribute-aware VLM support experiential public health education and promote a socially impactful research direction: interactive population simulator for community-centered communication design.