Developing an adaptive teaching algorithm for Chinese grammar rules using a deep reinforcement learning framework

Yang Yang

doi:10.1007/s41060-026-01228-0

Developing an adaptive teaching algorithm for Chinese grammar rules using a deep reinforcement learning framework

2026-08-07

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DOI：10.1007/s41060-026-01228-0

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Learning Chinese grammar is a difficult endeavor to pursue because of the combination of linguistic properties. The traditional rule-based approach in pedagogy tends to be rigid, and because it cannot facilitate individualized needs or scaffold the learning experience, it can be inefficient and demotivating. In this paper, we present a contribution to addressing these shortcomings in pedagogy through a hybrid natural language processing (NLP) and deep reinforcement learning (DRL) model to derive an adaptive teaching algorithm for Chinese grammar. This model incorporates NLP for learning grammar and DRL for dynamic choice of teaching strategy based on learner profiles, with personalized feedback and real-time adjustment of learning difficulty. Distinct from traditional AI algorithms that are fixed, the new model incorporates proximal policy optimization (PPO) and experience replay, which contributes to the levels of adaptability and stability of learning. Experimental results demonstrate that the proposed DRL-based adaptive teaching system reduced the average time-to-mastery from 12 to 4 sessions (66.7% improvement) and increased grammar error correction accuracy from 78.2 to 95.3% (21.8% improvement) compared with traditional rule-based teaching methods. In addition, the system achieved more consistent learning progression across sessions through personalized feedback and adaptive difficulty adjustment. Relative to other AI-ready grammar correction systems, the proposed method provides greater flexibility, effectiveness, and accuracy of correction of grammatical errors. As a whole, the work provides an original and data-informed approach to an intelligent method of grammar learning that enhances learner engagement and performance while using a model that can scale to adaptive language learning in AI-supported learning environments.

Keywords:

Deep reinforcement learning

Natural language processing

Adaptive teaching

Chinese grammar correction

Intelligent language learning

Developing an adaptive teaching algorithm for Chinese grammar rules using a deep reinforcement learning framework

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