Designing Personalized Chinese Character Learning Paths for Learners from Non-Hanzi Cultural Spheres Empowered by Artificial Intelligence

Le résumé fourni par la source

The global rise in Mandarin Chinese learning has illuminated a significant pedagogical challenge: the acquisition of Chinese characters (Hanzi) by learners from non-Hanzi cultural spheres, particularly those whose first language (L1) is based on an alphabetic system. These learners face unique cognitive hurdles stemming from the logographic nature of Hanzi, including graphemic complexity, the opaque relationship between form, sound, and meaning, and substantial memory load. Traditional pedagogical methods, often characterized by a one-size-fits-all curriculum, struggle to adequately address the diverse inter-learner and intra-learner variabilities in cognitive styles, L1 interference, and learning trajectories. This paper presents a comprehensive conceptual framework for an Artificial Intelligence (AI)-empowered system designed to create dynamic, personalized learning paths for this specific learner demographic. The proposed framework moves beyond static, technology-enhanced learning tools by integrating principles from second language acquisition, cognitive psychology, and computational linguistics. It is architected around four core modules: a multi-dimensional Learner Profile Module for capturing dynamic learner states; a structured Knowledge Representation Module that models Hanzi as a complex network of graphical, phonetic, and semantic features; a Personalization Engine that leverages machine learning algorithms to analyze learner data and generate optimized learning sequences; and an Interactive Content and Assessment Module that provides adaptive, multi-modal learning activities and diagnostic feedback. The paper elaborates on the theoretical underpinnings of this framework, detailing how AI can facilitate adaptive scaffolding, intelligent error diagnosis, and contextualized learning. By systematically deconstructing characters based on structural complexity, etymological lineage, and semantic relativity, the system can sequence content to mitigate cognitive load and leverage prior knowledge. We argue that such an AI-driven approach can transform Hanzi pedagogy from a linear, memory-intensive task into an intuitive, exploratory, and highly efficient learning experience. This research contributes a detailed theoretical blueprint for the next generation of intelligent language tutoring systems, specifically tailored to the profound and persistent challenge of Hanzi acquisition.

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