A Study on the Automatic Generation and Optimization of Chinese Language Teaching Content in Higher Education Based on Artificial Intelligence and Natural Language Processing
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Abstract
The automatic construction and dynamic optimization of Chinese language teaching resources in higher education have new technical ideas based on artificial intelligence and natural language processing technology. This study introduces an automatic content generation model which combines semantic encoding, knowledge enhancement, conditional control of learning objectives, and multi-granularity generation to solve the problems of knowledge bias in the generated content, lack of fit with learning objectives and lack of structural hierarchy. Moreover, a closed-loop optimization mechanism including quality evaluation, problem diagnosis and strategy refinement is set up. Experimental results show that the proposed model achieved BLEU-4, ROUGE-L, and BERTScore scores of 38.12%, 50.36%, and 0.887, respectively, representing improvements of 2.71, 2.74 percentage points, and 0.018 compared to T5; the knowledge accuracy rate and instructional objective alignment rate reached 91.8% and 90.7%, respectively, validating the model’s effectiveness in terms of generation quality and instructional suitability.
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