As online language education increasingly shifts toward personalized and immersive learning, scenario-based learning (SBL) offers a promising way to connect language knowledge with authentic communicative contexts. However, creating or generating immersive scenario-oriented learning materials remains challenging, since systems must interpret complex real-world situations, organize them into pedagogically coherent learning paths, adapt linguistic difficulty to learners’ proficiency levels, and support interactive learning beyond static content delivery. In this work, we develop ImmersiveCFL, a multi-agent framework that generates scenario-oriented learning paths for enhancing immersive experiences during learning Chinese as Foreign Language (CFL). Given learner-customized scenarios, ImmersiveCFL schedules a pedagogically coherent learning path with knowledge retrieved from a Chinese Education Knowledge Graph, generates multimodal scenario dialogues, and creates task-based interactive simulations with multi-skill exercises. Experimental results show that ImmersiveCFL improves difficulty control, curriculum coherence, and personalization over general LLM-based baselines. An online prototype[http://smartlearn.shuishan.net.cn:8089] further demonstrates its potential for goal-driven and interactive CFL learning.