The Personalized Scene-Based AI Agents: Exploring Chinese Language Learning Among International Students
With the development of deep learning and large language model (LLM) technologies, AI agents, as a key application of generative artificial intelligence (AIGC), have demonstrated significant potential in the field of international Chinese education. Personalized scenario AI agents, leveraging open-source AI chat frameworks, large-scale Chinese language corpora, and dynamic adjustment mechanisms (RAG, PAL, ReAct), effectively address key challenges in Chinese language acquisition for international students. These challenges include a lack of communicative practice, difficulties in acquiring everyday expressions, the inability of large-class instruction to accommodate individual differences, and the absence of real-time feedback. By constructing immersive interactive scenarios, personalized learning pathways, and intelligent feedback systems, AI agents enhance learning efficiency and flexibility while providing new pathways for the intelligent development of international Chinese education.
Authors
- Heng Xu (ORCID: https://orcid.org/0000-0001-5642-6543)
- Jun Peng (ORCID: https://orcid.org/0009-0008-6282-3762)
- Baohua Su (ORCID: https://orcid.org/0009-0000-3793-9933)
- Kaiyi Chen
- Zhihua Zhou (ORCID: https://orcid.org/0000-0001-5014-6946)
- Liu Hua (ORCID: https://orcid.org/0009-0002-1249-8566)
Institutions
- Ningbo University (CN)
- Chongqing University (CN)
- Jinan University (CN)
Publication Details
- Published
- 2025-05-14
- DOI
- https://doi.org/10.1109/icaie64856.2025.11158030
- Primary Topic
- Educational Technology and Pedagogy
- Type
- article
- Field-Weighted Citation Impact
- 0.00