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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T ImmersiveCFL: Multi-Agent Learning Path Generation for Scenario-Oriented Chinese Language Learning
%A Yang, Shanghui
%A Wu, Zhixuan
%A Zeng, Yanan
%A He, Feixiang
%A Wang, Chunyang
%A Qian, Weining
%Y Agrawal, Sweta
%Y Abad, Alberto
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-461-3
%F yang-etal-2026-immersivecfl
%X 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.
%U https://aclanthology.org/2026.emnlp-demos.75/
%P 796-805

Markdown (Informal)

[ImmersiveCFL: Multi-Agent Learning Path Generation for Scenario-Oriented Chinese Language Learning](https://aclanthology.org/2026.emnlp-demos.75/) (Yang et al., EMNLP 2026)

• ImmersiveCFL: Multi-Agent Learning Path Generation for Scenario-Oriented Chinese Language Learning (Yang et al., EMNLP 2026)

ACL

• Shanghui Yang, Zhixuan Wu, Yanan Zeng, Feixiang He, Chunyang Wang, and Weining Qian. 2026. ImmersiveCFL: Multi-Agent Learning Path Generation for Scenario-Oriented Chinese Language Learning. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 796–805, Budapest, Hungary. Association for Computational Linguistics.